I don’t remember the last time a model release made me this restless.

Claude Fable 5 came out while I was already on vacation, up in the mountains in Romania, the kind of trip where you're supposed to put the laptop away. I’d brought it anyway. My FOMO won, and for once I’m glad it did.

I tried it on a few tasks and I was blown away, every time the same way. I’d hand it the task, walk away, and by the time I looked back at my laptop the thing was already done.

Then last night I did something I’d never managed with any AI model before. I watched it finish the whole thing on its own, without the usual headaches, the ones that come from not being a developer and not knowing how this stuff even gets built.

I just described what I needed the way I’d describe it to a person, without knowing how to steer it down one path or another, and Fable went and did it. Perfect, from the first message.

It got me so excited that at 2am I started writing a full list of the hard, tangled things I’d been wanting to do, the ones that would be real game-changers for me if they got done. I lined up the prompts, ready to run them in parallel the next day and finally clear the list before I leave the country tomorrow (for my real, no-laptop vacation), the same day this email lands in your inbox.

But then, the news came…

I’ll get to that part. But I don’t want to start with the loss. I want to start with what we had (and what some of you still do).

So let me show you what Fable is, what people built with it in the few days we all had it, what the AI Blew My Mind community pulled off, and the thing I finally built myself after failing at it for months.

If you’re in the US, and I know about half of you reading me are, maybe this gives you new ideas for what to try with it while you still can. If you’re outside the US like me, well, hopefully we get it back soon.

Let’s begin.

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Here’s what we’ll cover:

• What Claude Fable 5 actually is (a Mythos-class model, explained)

• The 72-hour build wave: how Claude Fable 5 came and went

• What people on the internet built with Claude Fable 5 in 72 hours

• The cross-platform analytics dashboard I finally built with Claude Fable 5

• What the AI Blew My Mind community built

• Why Anthropic pulled Claude Fable 5 (and which model to use now)

What Claude Fable 5 actually is (a Mythos-class model, explained)

If you only know one thing about Fable 5, know this: it is the most capable AI model the public had ever been allowed to touch.

It’s the first model from a new tier they call Mythos, which sits a level above the Opus models most of us were already using.

I made a cheat sheet the day it launched, back when we still thought we all had until June 22. The capability summary holds up. The countdown didn’t, for most of us.

The same model exists in two versions. The public got Fable. A small set of vetted organizations, cyber defenders and infrastructure providers, got Mythos 5, the identical model with some of the safety limits taken off.

So the version you and I could touch was the careful one.

That careful part is something called safety routing. If your request gets close to a few sensitive areas, mainly cybersecurity, biology and chemistry, Fable hands that one request to the older Opus 4.8 instead. Anthropic says this happens in under 5% of sessions, and that you’re told every time it does.

That last part is where people disagree. Plenty of users online say they were never told, that they got downgraded mid-task with no notice, and a few reported being routed away from ordinary technical work, fluid dynamics, medical imaging code, that isn’t dangerous at all.

So Anthropic says one thing, a chunk of the community says another. I never hit it myself, which means either everything I gave it stayed in Fable’s lane or I got switched and didn’t notice, exactly what those users are describing.

Now the part that made everyone lose their minds: what this thing can do.

A few numbers went around. Before launch, Stripe handed it a code change across a 50-million-line codebase, the kind of job a team grinds through for about two months. Fable did it in a day. It can work on its own for hours without losing the thread. And it topped nearly every benchmark Anthropic tested it on.

But benchmarks are abstract. What made the week feel different was watching what regular people did with it once it was in their hands.

The 72-hour build wave: how Claude Fable 5 came and went

The timing is the whole story. Fable went free on the paid plans on June 9. By June 12 it was gone, for those of us outside the US. So we had roughly three days.

My feeds filled with the same reaction over and over, some version of “I can’t believe it just did that”, attached to a game, an app, a tool, a whole website someone described in a sentence and watched get built. Even my business partner, Matei, called it "a f***ing beast", and that was the final straw. My FOMO won, and I opened my laptop.

And it wasn’t just my feeds. The moment Fable dropped, I posted in the AI Blew My Mind community chat asking what everyone was making with it, and the thread that came back was so good it gave me the idea to write this whole post:

What people on the internet built with Claude Fable 5 in 72 hours

After three days of scrolling, the same few categories kept showing up: games and interactive toys, apps and SaaS built from a sentence or a screenshot, real engineering and production work, and the small everyday-life builds people made just to make their own days run smoother.

What follows is what I picked, the most interesting ones I came across. I haven’t verified every claim, I found these the same way you would, by scrolling, so treat them as a tour of what people said they made, not a lab report.

Every one has a link so you can go look and judge for yourself. Here’s a sample from each:

Claude Fable 5 games and interactive builds: 6 use cases

Three games from three sentences | Ethan Mollick
Mollick, the Wharton professor most of us read for level-headed AI takes, built three playable games with one prompt each in Claude Code: a Snake clone, a lantern-lit tunnel maze, and a wandering game built on Rilke’s poetry. He said Fable outran every other public model he’d used by a considerable margin, and that it worked on its own for up to a dozen hours on long instructions.

An entire Hogwarts castle | Matt Shumer
Fable one-shot a full explorable Hogwarts, classrooms, the Great Hall, the Quidditch pitch, the lot.

An infinite explorable universe, assets and all | Anshu
Connected to image-generation and image-to-3D APIs, Fable built an infinite explorable universe with characters and lore, every asset and sound made from scratch, in three prompts over two hours on max effort.

A Backrooms horror escape game | u/Turbulent-Sink-6171
One prompt produced a playable browser-based Backrooms escape game with eight pages to find and unsettling audio, the builder said they stopped playtesting at 2am because they didn’t want to keep going alone. Worth including precisely because the comments fought about it: the top replies suspected Fable remixed existing Backrooms and Slenderman code from training data rather than inventing it. Live, with public source.

A Minecraft clone in 20 minutes, one shot | ChrissGPT
A single “make a Minecraft clone” prompt to Fable on high effort produced, in about 20 minutes, a world with multiple biomes, a day-night cycle, different ores, and caves. The builder’s own reaction was just “I’m stunned,” which is roughly the universal response to this category.

A one-prompt game a single person could ship | via tyrtyre201
More a story than a demo, and it’s the thesis of this whole piece in miniature: someone built a playable game with Fable in a couple of hours, one prompt for a working prototype, then a few hours of back and forth for a real level with working mechanics, art that loads, sound that triggers. Not the best game ever made, but the cheapest fun game ever made by one person.

Fable 5 apps and SaaS, from one prompt or a screenshot: 4 use cases

An “extremely delightful” iOS calorie tracker, one prompt | Anshu
Asked for an “extremely delightful” calorie tracker for iOS, Fable produced a working app from that single prompt, delight brief included. The interesting signal here is native mobile, not just web, coming out of one instruction.

A beautiful one-shot landing page | Alex Prokhorov
Fable one-shot a polished landing page. A clean example of the “designer-quality first draft” people kept reporting, where the output looks shippable rather than like a placeholder.

A website about itself, with full creative freedom | Aurélien
Given complete freedom to make a website about itself, Fable produced something the builder called wild. A fun entry because it’s the model making an aesthetic call with no human art direction, and the result holds up.

An entire app from nothing but screenshots | one-shot breakdown
A creator fed Fable only screenshots of a SaaS landing page and it built the matching app. The reason it matters for non-developers: you don’t have to describe architecture you don’t understand, you can hand it a picture of the result you want.

Claude Fable 5 for real engineering work: 2 use cases

A working V8 engine CAD model in under 10 minutes | Aaron
Aaron asked Fable to model a V8 engine and got back a fully working CAD model in under ten minutes.

A QDD actuator, designed and collision-tested | Jake
Fable designed a quasi-direct-drive actuator, then animated the gearbox and ran collision inspection as part of its own validation loop, about 30 minutes and 400,000 tokens.

Everyday-life builds with Fable 5

A wall-calendar dashboard for a 77-inch OLED | u/orangejulius
A legal-writing newsletter author had already built a household calendar dashboard for a giant wall display, plus apps to control Sonos and Spotify, then had Fable spin up a portfolio site on Ghost.

The cross-platform analytics dashboard I finally built with Claude Fable 5

This week I shared the Claude Code agent that runs my whole content distribution, the one that takes each article and pushes it out to Reddit and LinkedIn while I sleep, increasingly onto platforms where I’m barely present yet.

I only started most of those accounts about a month ago. Now I’m posting everywhere and committed to growing on each one, the way I’ve been growing Substack.

So I wanted one cross-platform analytics dashboard to track all of it: every metric, every platform, side by side, so I can see what’s driving the growth instead of checking five apps.

And I built it as a new project inside that same autonomous distribution OS. The agent pushes the work out, the dashboard tracks what comes of it.

The wall I’d hit before: Substack has no public API. If you want your own numbers out, you’re on your own. I’d seen others do it through Substack’s unofficial endpoints, so months ago I tried. But I’m not a developer, and after multiple tries I couldn’t manage it, so I eventually gave up.

This time: I wrote a plain description of what I wanted, just like I had with other models before, but this time Fable went and got it.

How Fable did it: When you click around your Substack stats, the site is pulling your numbers from its own servers in the background, requests you never see. Fable opened a browser it runs itself with no window (a headless browser), had me log in once, and watched which of those background requests fired. Then it just made the same requests itself, pulling my data the same way Substack’s own pages do. No official API needed.

It did the same for Instagram, Threads, my Facebook page, and LinkedIn through Postiz (I covered what Postiz is here). For now, that is. All of them except Substack have APIs, so next I’ll just add their keys instead of going through the headless browser.

What the dahsboard shows:

• Total audience: every channel combined, in one number.

• Total views: across all platforms.

• Per-platform cards: each one with that platform’s own key metrics.

• Last 48 hours: everything I posted, all in one place.

• Three-month trends: so I can see direction, not just today.

• Hit rate: how often a post beats my median.

• Content library: everything I’ve published.

• Cross-posting gaps: posts that did well on one channel and never made it to the others, so I can repost the winners where they’re missing.

• Daily cohort analysis: on my subscribers, so I can track the lifetime value of a reader.

It matched my brand on its own, and it’s live on my own server right now, refreshing every morning on a 24-hour task so the numbers are current when I open it.

I'm really happy with how it turned out and how fast it all came together, without me ever having to figure out where those Substack endpoints live.

Final impressions: Fable felt different from any model I’ve used. It can go for hours without me babysitting it, and it’s far more action-oriented than the models I’m used to. That’s also why it’s not one you chat with. It’s built for complex, many-step work, either figuring out the steps on its own or running the ones you hand it. That was the real shift I felt: how long it could keep going, and how much sharper its reasoning was at finding a solution and actually building it.

One last thing. By the time I’m writing this, I’ve had a few hours to come down from the morning, when I found out it’s gone and I can’t use it anymore. Sitting with that frustration, I realized how much I’d already come to want it, and how quickly these models work their way into how we operate. How much we lean on them without noticing, and how strange life before AI now seems, at least if you work with it as closely as I do. Ugh.

What the AI Blew My Mind community built

Like I said, the community thread is what gave me the idea for this post. Here’s a sample of what people made. Follow them if something’s your kind of thing.

A full assessment system, held together across sessions, by

Rich fed Fable a multi-phase scope of work and had it hold that as the running spec for everything downstream, then turn raw field data and interviews into scored records, build measurement instruments calibrated against real high and low performers, and keep all of it straight across sessions so each one resumed from the current state instead of starting over. He defines the ask in detail, Fable builds, he validates, and every correction becomes a standing rule rather than a one-time fix.

A skill that makes Claude reflect at the end of a session, by

Hans built /reflect, a skill that runs a three-phase review when a Claude Code session ends, pulling out the durable facts, corrections, and workflows worth keeping and writing them back into his config so the next session starts smarter. He also ran Fable across two big active repos with two accounts going at once, and his takeaway was simple: he was glad to finally see a model that could hold that much context without losing the thread.

Nine workstreams in one night, by

Chris handed Fable nine separate workstreams in a single evening, including a full knowledge-base audit, building something and deploying it, rebuilding a CRM more efficiently, and updating dozens of his existing skills. The striking part isn’t any one task, it’s that the model held nine of them at once without dropping the thread.

An app that estimates the climate cost of everyday objects, by

Gabriel built carboneye.app, which lets you photograph or describe a product and get an estimate of its climate impact, water and CO2, in units a normal person can feel, like miles driven or showers taken. It came out of a conversation with a friend who’s anti-AI on environmental grounds, and the point was to show AI working on the kind of problem it usually gets blamed for. He called it the easiest and most pleasant vibe-coding he’s done, no real context rot even working mostly in one long chat.

A 100-million-token overnight test harness, by

John pointed a persistent Fable setup at building and testing a benchmark harness, then let it run on its own for ten to twelve hours overnight, iterating and correcting as it went, well over 100 million tokens in a single stretch.

Headless publishing to a platform with no API, by .

Daniel had Fable automate the whole path from article draft to published Medium post, and since Medium offers no API for it, Fable hacked it together through a Chrome extension running on his own machine. He also had it produce a demo video for a product he’s about to release. The thread running through his work was self-containment, the model handling the entire messy chain rather than one clean step.

Why Anthropic pulled Claude Fable 5 (and which model to use now)

On June 12, the US government issued an export-control directive, and Anthropic disabled Claude Fable 5 and Mythos 5 worldwide to comply. If you searched why Fable got banned or pulled, that’s the answer. The most capable model the public had ever touched was gone almost as fast as it arrived.

The directive covers any foreign national, inside or outside the US, so for those of us abroad, me in Romania, about half of you reading this, the thing we were using on Friday night was no longer available to use by Saturday.

So if you’re outside the US like me, the rest of Claude is still here and still very good. Here’s a reminder of which model to reach for while we wait for Fable to come back:

And if you’re in the US and Fable is still in your menu: go play. Build the thing you’d never have attempted, run that list you keep putting off. And tell me what you make with it.

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Just save it for the truly hard jobs, the ones Opus can’t handle alone. It’s not only that Fable burns credits fast, it’s that asking it something basic is a waste of what it can do. So don’t waste Fable on “how many Rs in strawberry”.

If this gave you a useful look at what Fable could do, I’d love it if you passed it on.

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Last week I showed you the Cowork operating system I built to hold my whole working life in a set of folders Claude reads automatically, so I never brief it from scratch.

That’s the system for the work I want to stay deep inside: writing the newsletter, client projects, Amplifiers. Automations run in there too, skills, commands, whole workflows, but I’m present for every decision, by choice.

Over a month ago, maybe closer to two, I lost count, I started building a second system in Claude Code. It borrows the same folder structure and the same human-in-the-loop thinking, but it exists for the opposite kind of work: the part I dread.

For me, that’s distribution. Getting my work in front of people, every day, on every platform.

• Turning a published article into a Reddit post that fits each subreddit, then finding and answering the threads where people ask the exact question I just covered in >2,000 words.

• Drafting a LinkedIn version, making the infographics to go with it, and queuing it for posting.

• Pulling the best lines out of the article and posting them as standalone Substack notes.

• Repurposing it for the rest: Instagram, Facebook, Threads, where I at least have a process, and X, where I’d be starting from scratch.

None of it is hard, all of it takes time, and it has to happen every single day.

So I built a system to carry it. This is the engine for the growth side of my business, running while I do everything else. And it happens on its own, in the cloud, while my laptop is closed and I’m asleep.

Think of this as Part 1: how the AI agent works today and what I learned building it, so you don’t repeat my mistakes. Part 2 is the DIY guide (your own Claude Code OS, step by step). Even if your work has nothing to do with distribution.

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Here’s what we’ll cover

• Where I draw the line: what I automate and what stays human

• How my Claude Code AI agent works, at a glance

• What I’m building next: a research layer, more platforms, and products from articles

• Why I built this in Claude Code, not Cowork

• The six automations running now, and what’s still me on purpose

• Where the whole system lives

• The folder structure, the memory files, and the engine in the cloud

• The anatomy of one automation, taken apart piece by piece

• The internal wiki (inspired by Andrej Karpathy) that remembers every article I’ve written

• The self-learning loop that teaches it to write like me, with the real guardrails

• How to build your own version, for whatever your work is

Where I draw the line on what gets automated

I'm starting here, before any of the how, because this one decision shapes everything else. What I automate comes down to one personal call: what I’m okay handing over and what stays mine.

Every piece I add, and every one I hold back, comes from it, and it keeps shifting as I get comfortable trusting the system with more.

The newsletter writing stays mine. This is the one I’d never automate and I feel strongly about it. The newsletter is what everything else grows from, and most of my articles are builds and experiments. The building takes the time. The words are how I wrap it all up so you can see what I did and do the same yourself. None of that goes into this system.

Distribution gets automated, but nothing publishes without me. I don't enjoy it the way I enjoy the writing, and I don't have the time to do everything that needs doing, so passing the heavy lifting to the system is what I'm after.

But handing it off isn’t the same as stepping away. I’m constantly improving my Claude skills for every kind of writing I do, LinkedIn, Substack notes, Instagram, Facebook, Reddit, X, and I polish every draft until it sounds like me before it goes out.

Plenty of creators post more and faster by letting AI run the whole show and it works for them. I could do that, but I won’t. I’m not trying to become a content machine.

That's my line, and I've made peace with what it costs me: more work and a system that will never be fully hands-off, because I'm not comfortable letting it run without me.

How my Claude Code AI agent works, at a glance

Now, before I take it apart, here’s the whole thing in one view.

What I built is an AI agent, which is just a fancy term for software that doesn't wait to be told. Every six hours it wakes up in the cloud on its own, through a scheduled Claude Code Routine, and runs whatever task is due.

Here's everything it does right now, across a day:

• Ingests my articles: the moment I publish, it reads the new article from Gmail and saves it. Then it files it into my internal wiki, built on Andrej Karpathy’s LLM wiki method (more on that later).

• Repurposes for Reddit: it drafts a Reddit post from that article and suggests which subreddits fit.

• Answers Reddit threads: it scans subreddits for questions my work already answers, which it knows because of the wiki, and drafts replies.

• Waits for my call: every draft comes to my Slack, where I approve, reject, or rewrite it. Each reply teaches it something. Approve, and it knows it’s on the right track. Rewrite, and it learns what I'd say and how I'd say it. Reject, and it learns that thread wasn’t worth answering.

• Posts to LinkedIn: it publishes my approved LinkedIn posts, text and image, on schedule, and tells me when they’re live.

• Tunes itself weekly: every Monday it reads all my edits from the week and rewrites its own instructions and skills to sound and think more like me, a small kind of self-learning.

The whole thing is just a folder of plain text files. Here’s the GitHub repo:

And here’s that folder working. One heartbeat run reporting in, a Reddit reply waiting for my approval, and a LinkedIn post it published for me:

This happens whether I’m at my desk, in line at the market, or on vacation. The system runs on Anthropic’s computers, not mine.

My laptop can be off for a week, and the drafts keep landing in my Slack, the new articles keep getting filed, and the posts that are safe to publish keep going out on their own.

My phone (and Slack) became the control panel.

How I’m growing the system

What you just saw is where the system is right now. It’s been running for over a month, and the hours it saves every week are the smaller win.

The bigger one: it got me doing work I’d avoided for months.

I knew Reddit mattered for growing this newsletter and for getting indexed by LLMs. I knew the same about consistent LinkedIn posting. I just dreaded both, so the work stayed undone.

Now it happens daily, because the part I dreaded belongs to the system and the part that needs me takes minutes.

That became my golden rule of automation: start with the tasks you dread. Automating work you’d do anyway saves you hours. Automating work you keep avoiding gets it done at all.

The whole system grew out of that one idea and I still have a long list of dreaded things waiting their turn.

Why I add one workflow at a time

But I’m in no rush to clear that pipeline. The whole system grows slower than my ambition, and that’s deliberate, because I’m a perfectionist about what comes out of it.

What makes it slower than usual is that the work itself is new to me. I’m not automating something I’ve done for a long time. I’m still learning distribution as I go, finding my voice on each platform, seeing what lands on Reddit versus LinkedIn versus X. I have to get good at it by hand before I can teach the system to do it like me.

My Instagram carousels are a good example. The setup is all there: each article is saved with its images embedded in the HTML, so the system could take a new one and build realistic carousels on its own. The visuals come out great. The copy is where I’m not consistently happy yet and I won’t hand the whole thing over, publishing included, until I can steer it reliably.

That’s the pattern for everything here: I do a task by hand long enough to learn how Claude handles it and where my role can safely shrink, then I let the system take it over, review its drafts, and build the next piece when it’s ready.

The bigger plan

It started with repurposing my own articles, because that was the easiest place to begin. I already have months of content that never sees the light of any platform beyond Substack, so there was a backlog just sitting there, waiting to be put to work.

But repurposing is only one direction. I want the system to grow on both sides of it, getting more out of what I’ve already written and helping me create new content from scratch:

• Feed it before I write. A research layer that comes earlier, surfacing what people are asking, what nobody has answered (well), what’s going viral, and the cool things worth getting inspired by or resharing, so my ideas come from real signals. Amplifiers already pulls live data, and it has research workflows I can plug straight in, like my content research workflow and a news-tracking system, with more I can keep building. That gives me a steady source of inspiration for new content and pushes the system past repurposing what I’ve already written.

• Reach more platforms. Get onto X, where I’m starting from scratch. Fully automate the Instagram, Facebook, and Threads repurposing I still do 20% by hand. Start reposting articles to X (maybe?).

• Spin lead magnets out of my content. Pull free guides and resources from my own articles (using my brand skill), then post them across Instagram, LinkedIn, and X. Wire in ManyChat so when someone comments to get one, the system delivers it and handles the conversation, all from Claude, instead of me setting it up by hand after every post.

• Turn some of it into products. Package articles into paid products on Gumroad, for readers who don’t want a subscription but would happily unlock one good thing.

• And I could keep going.

For all of that to run on its own, in the cloud, while I’m asleep, it had to be built somewhere specific.

Why I built this in Claude Code, not Cowork

This isn’t the Cowork OS with a few extra folders. It’s a second system, in its own folder.

The difference is the role I play. In the Cowork OS, I’m in the middle of everything, deciding each step. This one runs in the cloud on a schedule and pulls me in only for the parts that need my judgment.

And the cloud part is the technical reason this had to be Claude Code. Cowork has scheduled tasks too, and I use them, but they run on my computer, with the app open.

Claude Code has Routines: scheduled tasks that run in Anthropic’s cloud, so one fires with my laptop closed and me on a beach. Without that, a system that works while I sleep wouldn’t exist.

I keep the two separate on purpose. The Cowork OS is stable, the work I’m hands-on with every day and depend on. This one changes every week, a constant loop of iteration where I hand over a little more each time. That handover takes more than trust. It takes building each skill and workflow until the output is good enough that I'm comfortable letting it go.

Everything below is for premium readers: the full breakdown of all six automations, the folder structure and memory files, the engine, the internal wiki, the self-learning loop, and my watch-out checklist, so you can start building your own. Upgrade here.

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What other people have already written. What’s getting traction. What the top YouTube videos cover. What people argue about on Reddit. What questions keep coming up that nobody answers well.

I do it for two reasons:

1. To find the gap I can fill, instead of publishing the 100th version of something that already exists.

2. To broaden my own view. Reading across every platform almost always hands me an angle I hadn’t thought of or a point that already resonated with people that I’ll want in my own piece.

Done properly, this used to take me hours. Sometimes days. Tabs everywhere, half-read articles, three YouTube videos open, a Reddit thread I lost track of.

I even tried handing this research to Claude controlling my computer, after a lot of you told me you wanted an easier way to do it as well. And it works. But it's slow, it burns through credits, and because it scrolls one page at a time the way a person would, it distills far less than you'd want.

So I built a Topic research workflow inside Amplifiers, which already has access to all the social platforms I want to pull from. Now it gives me the research brief I need before writing an article or creating any piece of content, in about 5 minutes.

The video below is a quick look at it running, if you want the glimpse before the detail.

The rest of this is how it works and why it changes how you start any piece of content.

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In today’s article:

• What this AI content research workflow does (5 steps, start to finish)

• The content research report you get back

• It works for any format, not just articles

• Why the data is real (and why that matters most)

• Where content research fits in my AI content system

• The research step that comes before everything

What this AI content research workflow does

The workflow is called Topic Research for content creators. It lives inside Amplifiers, the AI blew my mind connector for Claude and ChatGPT, and it runs on the same research tools I covered here, now wired together across 8+ platforms.

Once you’ve set up Amplifiers, using it is as simple as it sounds.

Step 1: You open Claude and ask

Something like “research [this topic] for a piece of content using Amplifiers”.

That’s the trigger. Claude finds the topic research workflow and runs it.

Step 2: You tell it what you’re researching

Two things:

• The topic you’re exploring: broad like “AI for consultants”, or specific like “is Codex better than Claude Code for non-technical founders building their first app”, whatever you’re thinking of covering

• The kind of content you’re making: an article, a YouTube video, a LinkedIn post, an Instagram reel or carousel, a newsletter, whatever you’re creating

Step 3: You pick the platforms

YouTube, Reddit, Google, LinkedIn, X, Instagram, Facebook, whichever ones matter for this topic. All of them, or just the two you care about.

Step 4: It does the research you used to do by hand (or not at all)

• Searches YouTube for what’s performing, then pulls transcripts and comments from the top videos

• Searches Reddit for the real questions and frustrations people post

• Checks what’s ranking on Google and reads the top articles

• Scans LinkedIn, X, Instagram, Facebook, whichever ones you picked

Step 5: It tags everything as it goes

For each piece of content: the angle it takes, the format, the hook, how it performed, and what the comments reveal that the content missed.

The content research report you get back

Everything comes back as one structured report:

• The landscape at a glance. How much already exists, how good it is, and where the obvious opening is before you get into the details.

• Saturation by platform. Which platforms are crowded and which are wide open, plus which angles are already over-covered on each one, so you know where your piece has the best shot at being noticed and which takes to avoid.

• Engagement patterns. What’s getting traction across the top performers: the formats, the structures, the emotional angles people respond to, pulled from real view counts, upvotes, and comments.

• Hook formulas from top performers. The exact opening moves the best-performing content uses, broken down so you can borrow the pattern.

• A gap map. The specific things people are asking that nobody answers well, each backed by a real thread or comment.

• 3 to 5 ranked angles. Concrete directions you could take the piece, each with a draft hook and a reason it would stand out, ordered by which has the best shot.

And it doesn’t stop at the report. If you pick an angle, it also builds the outline.

Approve the outline, it writes the draft. So you go from “I want to write about X” to a researched, structured draft in one sitting.

It works for any content format, not just articles

I keep saying “writing”, but that’s just how I use it. The workflow asks where your content lives, and tailors everything to that.

It works the same whether you're building a personal brand or a business, and whatever channel you're creating content for: articles, YouTube, LinkedIn, Instagram, TikTok, X.

Same content research underneath, the output just bends to the format.

Why the data is real (and why it matters most)

If you ask Claude or ChatGPT to find what’s on Reddit about a topic, it’ll give you something. Reddit is well indexed by LLMs, so it can surface a few useful things. But it’s a handful, it’s shallow, and the links it hands you might be made up.

Go to YouTube, LinkedIn, or Instagram and it’s worse. The model can’t reach into those platforms on its own. It migh improvise something, but you have no way to tell what’s real.

That’s the reason Amplifiers exists. When you run this workflow, it’s not pulling from what the model half-remembers. It’s going to each platform and reading what’s there:

• Real YouTube videos with real view counts and transcripts

• Real Reddit threads with the real upvotes and comments under them

• Real articles currently ranking on Google

• Real LinkedIn, X, and Instagram posts, with the engagement they got

So when the report tells you a topic is everywhere on YouTube but barely touched on Instagram, that’s because it looked.

An AI on its own can still help you think through a topic. You can brainstorm with it, shape an angle, talk it out, and that’s useful. But it can’t give you the level of insight you get from the data itself, like the virality patterns, the hooks that are working, and the questions people keep asking that nobody answers well.

That kind of insight comes from reading what’s already out there, which is exactly what the workflow does for you.

It’s the same thing that let me cut prospect research from 30 minutes to 2, just pointed at content instead of people. It’s also how I keep up with the news I care about, pulled from real sources I can open and verify, instead of links a model invented.

Where content research fits in my AI content system

Over the past months I've built a whole stack of tools and systems for content creation and marketing with AI. But all of them come after this one. The research is the part underneath, the step before any asset gets created, because it’s what tells me what’s worth making in the first place.

Most of them are their own articles if you want to go deeper.

Two are writing skills, the ones that shape how the content sounds:

• Voice DNA. Makes Claude write like me instead of like a polished stranger. My most popular one.

• Audience profiles. So Claude always knows who I’m writing for, covered in the same write-like-me guide above.

The rest are content-creation skills, the ones that turn the writing into a finished, on-brand asset:

• The brand skill. Every PDF, deck, and doc comes out on-brand without me touching the design.

• The carousel skill. Turns an article into branded Instagram carousels in one go.

• The lead magnets skill. Built on top of the brand skill, turns content into branded PDF lead magnets in about five minutes.

• Infographics for LinkedIn or my articles, generated right inside Claude now that it can make images.

Research first, then write in my voice, then turn it into the asset.

The research step that comes before everything

This is the workflow I reach for before I write anything, and it’s probably the one you’ll want too.

It does the part that used to eat my hours: reading what’s already out there across every platform, finding the gaps, handing me a brief I can write from. For most content, that’s exactly what you need before you start.

And if you want it to work a little differently, you can shape it.

Maybe you want the report in a different format or you only care about Reddit and Google and would rather skip the rest.

You can build your own version by opening a conversation with Claude (or ChatGPT, if that’s where you work), pointing it at the Amplifiers tools you want to pull from, and iterating until the output is right. Then you document it with the free Claude skill that turns the conversation into a reusable workflow, and save it inside Amplifiers so it’s there every time you need it.

That’s the whole idea. Start with this, and if what you need is different from what I need, build the version that fits you.

Now tell me… Where do you do most of your research now, and is it working for you?

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And if this saved you from another evening lost to infinite scrolling, share it with someone who’s still doing their content research the hard way. It helps them get their time back, and it helps this newsletter grow.

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Everyone wants the same thing from AI now. Not a chatbot you re-explain yourself to every morning. Something that already knows you.

Your work, your projects, your preferences, how you like things done, what you’d never want it to do. Something with a real memory, that remembers your decisions and the context you gave it last week instead of forgetting the moment you close the tab. An AI that opens already caught up, so you skip the briefing and start working.

A personal AI operating system, basically.

Hardly anyone has one yet, for two reasons.

1. The first is context. Most of what the AI needs to know lives in your head and never gets written down: how you structure a client email versus a team one, the words you avoid, the rule you’d never break. So every session starts close to zero, and the more work you push into AI, the bigger that tax gets.

2. The second is setup. It looks complicated. What goes where, how to give it a memory, how to lock in decisions so they stick, how to hold every area of your work without it turning into a mess. None of it is hard, but it looks like enough work that most people never start.

In my last piece I gave away a free Claude skill that saves a workflow so you can reuse it instead of rebuilding it every time. A Cowork operating system is the home most of those pieces live in.

That matters more the deeper you’re in. I do a lot of my work in Claude Cowork now, and at that volume the missing structure is what costs you. So I set mine up properly, with an initial nudge from Jeff Su’s setup, and the surprise was how little I had to write myself, because most of it was already sitting in skills I’d made months ago. You’ve probably made the pieces too. What’s missing is how they fit together.

So I took my whole setup and turned it into two workflows that build your Cowork operating system around your actual work. Every file you need, ready to run from the first session. In under 60 minutes, start to finish.

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Here’s what we’ll cover

• What a Claude Cowork operating system looks like (my real setup)

• How a Cowork operating system works: root, workspaces, projects

• The two ideas that make it run: rules vs. facts, and routing

• How to build your Cowork operating system in 5 steps

  • Step 1: Plan your setup with the Cowork Workspace Planner

  • Step 2: Sync Claude Cowork across computers with Google Drive

  • Step 3: Build it with the Cowork Workspace Builder (+ migrate your Claude Projects)

  • Step 4: Make the system yours

  • Step 5: Keep it sharp with the Cowork Session Sync skill

• What you’ll have by the end

This is the system I (really) wish I’d had months ago. Let me show you what it does.

What a Claude Cowork operating system looks like (my real setup)

Before any explanation, here’s the thing itself:

Just a couple of folders, right? Except these few folders now hold close to my entire working life, organized into one structure Claude reads automatically.

When I start a session, I don’t explain anything. No setup, no paste, no re-briefing. I just ask, and it already has the context, the skills, and the rules it needs.

Here’s a demo with just a few tasks, though it works just as well for anything in my workspace, from client work to end-of-month finance to marketing Amplifiers.

In this demo:

• I asked for a carousel from “my last article” and never gave it a link or a title, because it knows where to get it from on its own. It found my latest post, pulled the full text, and built the carousel copy.

• I switched to a LinkedIn infographic and post mid-session. It reached for the right skills, matched my brand and voice, and routed the image through the Amplifiers connector, all without being told how.

• I asked for internal linking opportunities for SEO. It scanned all 104 of my published articles and handed me the best ones to add, with the exact anchor text and why each fits, in the format I always want. I never told it where to find my articles, what I meant, or how to format the answer.

And changing how it works is just as easy. I say what I want once, and it locks the new rule into the right file, so next time it knows, without me repeating it:

None of that is clever prompting. It’s structure. The system knows where everything lives, so it never has to ask, and I never have to repeat myself.

Now here’s how the whole thing works, in a way that’ll make immediate sense.

How a Cowork operating system works: root, workspaces, projects

Think of your Cowork operating system as a company. Three levels, each one knowing a little more than the level above it.

The company: your root

Every company has things that are true for everyone in it. The mission, the values, how people behave, where to go for what. Set once, and everyone operates under them.

That’s your root, the top-level folder, and it runs on three files:

• CLAUDE.md, the org chart and rulebook in one. It holds the rules that apply to everything you do, like your preferences for how Claude should act and talk to you. It also tells Claude where to go for each kind of work, and gets read first at the start of every session.

• MEMORY.md, the current state of your world. What you’re working on right now, your recurring tasks, the facts you reference constantly.

• ARCHIVE.md, the old records. Finished projects, past decisions, history you don’t need every day.

The difference between those last two matters. MEMORY is the desk you work at, Claude reads it every session, so it stays lean. ARCHIVE is the filing cabinet, Claude only opens it when you ask about something old (”what did we land on for that sponsorship deal back in April?”). So you keep your full history without it slowing down or costing you anything on a normal message.

The departments: your workspaces

A company has departments, each with its own goal, scope, and way of operating, on top of the company-wide rules.

Those are your workspaces, one for each ongoing area of your work. For me that’s the newsletter, content and social, client consulting, Amplifiers, the finances. Each one has its own rulebook and memory. When you work in that area, the rules stack:

• Claude reads the company-wide root first.

• Then it layers the department’s rules on top.

So when I work with Cowork on an article, it follows my preferences for how Claude should behave with me from the root, plus the newsletter workspace’s own conventions for the writing itself. Each layer adds detail. No layer repeats the one above it.

The projects: your specific work

Inside a department sit projects. Specific pieces of work with a start and an end.

Those are your projects, folders nested inside a workspace, the tightest scope of all. An Amplifiers launch inside the Amplifiers workspace. One client engagement inside consulting. They're temporary, and when one's done you just tell Claude to archive it.

Why it stays fast, and remembers

That’s the whole structure: company, departments, projects → root, workspaces, projects.

And like the map above shows, every level carries its own CLAUDE.md and MEMORY.md. That’s what makes it both smart and fast:

• It remembers. Each MEMORY file holds the decisions, context, and current state for its level, written down instead of vanishing when you close the tab. The decisions I make inside an Amplifiers launch live in that project’s memory. Facts that apply everywhere, like my business model, live in the root.

• It stays fast. The root never has to hold everything. Ask “what’s happening with the Amplifiers launch”, and Claude checks the root, confirms the project exists, then jumps straight to that project’s memory for the detail. The structure does the navigating.

And you never write any of these files yourself. They’re just plain text, the markdown format, that looks a little like code but reads like normal writing. You tell Cowork what you do and how you work, and it writes and maintains every file for you.

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The two ideas that make it run: rules vs. facts, and routing

The two prompts I built to set up your own Cowork operating system already know how this works, so they handle it for you. But understanding the ideas is the only thing that lets you keep growing the system once the foundational setup is in place.

Idea one: rules versus facts.

Every piece of context either tells Claude how to behave, or it’s a fact that could change.

• “Always” or “never” (”always sign off with my name”, “never use em dashes”) is a rule. Rules go in CLAUDE.md.

• Anything that could change later (a project’s status, a contact, a tool, a decision) is current context. That goes in MEMORY.md.

Keeping the two separate is what keeps the system reliable.

Idea two: routing is just navigation.

This is what makes it feel like the system knows you. Routing is plain instructions in your CLAUDE.md files that tell Claude where to go and what to use, and it works at every level.

So when I say “polish my draft,” the root CLAUDE.md reads it, sees it’s an article, and routes to the newsletter workspace. The workspace’s CLAUDE.md doesn’t spell out every step, it just points to the polishing workflow saved in its resources, which holds the sequence:

1. Apply my voice DNA to rewrite it.

2. Use the article-infographic skill to find infographic opportunities.

3. Use the dense-infographic skill to generate them, then the image-SEO skill to give me a filename and alt text for each.

4. Use the SEO-audit skill to suggest a title, meta description, and URL slug, and tighten my section headings.

5. Use the thumbnail-generator skill to suggest thumbnail angles, then generate the one I pick.

I never spell those steps out. They live in the workflow file, and the routing just calls it, so it runs the same way every time and my CLAUDE.md doesn’t get bloated no matter how many of these I build.

That’s the whole concept: a company structure for your context, rules and facts kept apart, and routing that points Claude to the right place.

The prompts build all of it for you, which is what the next part walks through.

How to build your Cowork operating system in 5 steps

You’ve now got the full picture of how it works. Next, you build your own Cowork operating system, in under 60 minutes today.

You could also build all of this manually. Map your work, create every folder, write every file, get the rules-versus-facts split right, wire the routing. It works, but it’s a lot.

So I built two prompts that do it for you:

1. The Cowork Workspace Planner, which brainstorms your whole setup with you, your areas of work, what’s ongoing, what you already have, then hands you a clean blueprint to build from.

2. The Cowork Workspace Builder, which takes that blueprint and constructs the entire setup, folders, files, and connectors, in one pass.

They’re both inside Amplifiers, but I’m also giving you the full prompts, so you can paste them straight into Cowork if you prefer it that way.

Let’s begin.

Step 1: Plan your setup with the Cowork Workspace Planner

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You started broad, then you corrected a few things, pointed the AI in a better direction, asked it to dig deeper into one area, told it the format wasn’t right, adjusted the length. By the end of the conversation, you had something useful. And more importantly, your LLM had learned how you wanted this type of task done.

Then next week, you needed to do the same thing again for another client. And you started from scratch.

That moment, where you rebuild something you already figured out once, is the difference between having one-off chats and systems you can reuse. That reusable system is what an AI workflow is: the goal, the steps, and your standards, documented once so you can run the same task again without re-explaining anything.

Because every time you corrected and guided the AI, you were training it on your preferences, your quality bar, your way of working. When it’s a task you do over and over, throwing that away and starting cold every time makes no sense.

So today I’ll show you the easiest way to document an AI workflow once and reuse it — for the kind of work you already do with AI, instead of re-explaining and re-correcting the same things every time. It runs on a Claude skill I built for exactly this.

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Here’s what we’ll cover:

• Why the easiest workflow to build is one you already finished (no extra work, you just save what already happened)

• The free Claude skill that turns a finished conversation into a workflow you can reuse (walked through step by step with screenshots)

• The two places I save workflows so I can find them again (and which one to use when)

• How to get the skill and set it up (whether you’re in Claude or ChatGPT)

Over the next few weeks I'm going to show you the systems I've built for myself and my business, in Claude Code and Cowork. But you won't be able to build your own until this part, the foundation, is in place. So this is where we start.

Let’s get into it.

The easiest workflow to build is one you already finished

The hardest part of building reusable AI workflows is that nobody saves the work they already did.

Now you might say, “But Daria, I don’t have time to take notes after every conversation.” Fair. The good news is, you don’t have to. Not with this method I’m about to show you.

When we hear “documenting”, we immediately think of the hard version, the one where you sit down to write up a process that lives in your head, something you do by hand and have never spelled out before.

What we’re doing here is the opposite. We’re looking at the chats where you spent 30 minutes, an hour, two hours working a task through with the AI. Teaching it how the task should be done, what format you wanted, correcting it until you landed on a version you were happy with.

That chat already happened. Claude was in it the whole time, watching every correction and learning how you want this done.

The only thing you never did was save it. You closed the tab, and next time the task came up, you started from scratch.

This is where the method comes in, and why I think you’ll find it so easy to use.

You run the Claude skill I built at the end of a work session, once you’ve landed on the output you wanted, and it documents everything about how you got there. That’s the easiest possible way to turn a chat into something you can reuse, which is exactly why it’s worth starting here.

And it does more than give you a repeatable workflow. It builds a habit. You keep doing what you’re comfortable with, your one-off conversations, but you start taking the first steps toward building real systems around your work.

It takes time to get used to, and this is the gentlest way in: one small layer added at the end of chats you were having anyway. Do it enough, and you're slowly working the way the people who get the most out of AI already do.

How the /conversation-to-workflow Claude skill works

You run all kinds of chats in a day. Quick questions, one-time tasks, things you’ll never touch again. Most of them aren’t worth saving, and that’s fine.

This skill is for the ones that are. And the way you figure out which is by asking yourself one question:

Is this something I’ll do again? Will future-me thank present-me for not having to figure this out from scratch?

If yes, that’s the chat to turn into a workflow.

Using it is simple, and it's the easiest way I know to create a repeatable AI workflow. At the end of a conversation that went well, you drop /conversation-to-workflow into the chat.

Step 1: It reconstructs what happened

Once you trigger it, it reads back over the entire conversation and reconstructs the process into a documented workflow. What it pulls out comes down to four things:

1. The goal. What you were trying to produce.

2. The steps. The sequence that got you there, including any tools you used and in what order.

3. The preferences. Everything you asked for along the way: tone, format, length, what to include, what to leave out.

4. The corrections. Every time you said “no, shorter,” or “more direct,” or “drop that section,” or “that’s not the format I wanted.”

That last one is the most valuable part. All those corrections you made in the chat, the ones Claude watched and learned from, a plain set of step-by-step instructions would lose every single one.

A list of steps only tells you what happened. The corrections tell you why the first version wasn’t good enough and what made the next one right. That’s where your standard lives, and it’s the context that separates an AI that does the task from an AI that does it the way you’d do it.

Step 2: It shows you the summary, you review and correct it

Then it shows that summary back to you and asks whether it matches how you’d want this done every time. This is your chance to catch what it got wrong and to add what you figured out mid-task.

Most of the time the version you want to save is better than the conversation that happened, because you learned something halfway through that should have been there from the start. So you bake it in now.

Step 3: It pokes holes in its own summary

Then it does something I think is really important. It looks for anything that relied on context that won’t exist next time:

• A preference you corrected once, but never said was your default.

• A step where it’d have to guess if it ran the task cold next week.

• An input the workflow needs that you never named.

And it asks you about those things directly, before saving, so the saved version works on a fresh task instead of falling apart the moment the context is gone.

Step 4: It builds and saves the clean version

Once it’s confirmed and clarified, it writes the final version and saves it.

The whole thing feels like a five-minute wrap-up at the end of a good chat, not a second project. You finish the task, run the skill, answer a couple of questions, and walk away with a documented workflow.

Step 5. It asks where to save it

Now you've got the workflow. The skill's last move is asking where to keep it, because a workflow you save in a Google Doc, your notes app, or some database is a workflow you'll lose. You won't dig it out next week. It's too much friction to find it, copy it, and paste it back, and half the time you don't even remember what to search for. So you redo it from scratch, which defeats the whole point.

So instead of leaving you to file it away somewhere you’ll never look, the skill gives you two places to put it, both built to be there when you need them.

Where to save your AI prompts and workflows so you never lose them

The skill gives you two options, and which one fits depends on whether you want it applied automatically every time or saved so you can pull it up when you choose.

1. Save it to your private Amplifiers library.

Amplifiers is a connector you add to Claude or ChatGPT. You might know it for giving your AI my full library of prompts, workflows, and tools it doesn’t have out of the box.

But it also works the other way around: it gives you your own private library, where you can save the prompts and workflows you build, right inside the chat where you already work.

So you just say “save this to my Amplifiers library”, and it’s there. No copying it into another app, no context-switching, no losing it in a folder. And when you want it back, you don’t have to remember the exact name or hunt for keywords. You just tell Claude what you're after, mention Amplifiers so it knows where to look, and it pulls it up for you.

PS. It works in ChatGPT too, which doesn’t have skills at all (yet), so whatever workflow you build, you can easily reuse in both.

2. Build a Claude skill.

A skill teaches Claude how you want a task done, then applies it automatically every time the task comes up. You lock in the format and the quality bar once, just like I built mine for writing, on-brand documents, or carousels.

The reel workflow above shows the difference. I wouldn’t turn it into a skill, because I don’t want it running on its own every time, and what I want out of it changes each run. That’s why I want it saved somewhere I can pull up on purpose, and tell it what I need that day. That’s what the Amplifiers library is for.

Get the conversation-to-workflow skill

I made this skill free for a reason. When ChatGPT first came out, we all used it the same way. Ask a question, get an answer, move on. Most people never left that mode.

The shift I want everyone reading this newsletter to make is out of it. Out of one-off answers and into building things you keep and reuse. This skill is one step across that line.

Get the skill

In the folder you’ll find a markdown file with the skill. Here’s how to use it depending on where you work:

• If you use Claude: Upload the markdown file to Claude as a skill (Customize → Skills → Upload a skill). At the end of any conversation worth keeping, type /conversation-to-workflow, and it runs.

• If you use ChatGPT: Open the markdown file in Drive and copy the text. Paste it into ChatGPT. After you set up Amplifiers, ask it to save the prompt to your private library. From then on you pull it up in any chat just by asking, instead of pasting it in every time. It’s the closest thing ChatGPT has to skills, and it’s how you keep this process within reach there.

Building repeatable AI systems starts here

There’s a reason most people never do this. We’re wired to value the result, not the process that got us there. You finish the research, you get the draft, you move on. Going back to document how you got there feels like extra work.

But documentation is the work that eliminates future work.

When you capture a process that worked, you’ve made something you can hand to AI next time and say “do it like this”. You start from your standard, not from zero.

One workflow on its own is useful. It saves you that twenty minutes the next time the task comes up. But the real transformation happens when you have a few of them, and you start to see how they connect, and where they all live: a Cowork operating system.

Your trend research feeds your content planning. Content planning feeds the writing. Writing feeds the promotion. Each one is a small, documented piece, and together they stop being a pile of prompts and start being a system. That’s when it gets interesting. Once you can see the pieces, you can connect them, the output of one becomes the input of the next.

But every system starts the same way: by capturing one process that works.

So start with one conversation this week. Something that went well. Before you close it, run the skill. Five minutes. Then build another, and another.

What’s the first workflow you’d build? Hit reply and tell me, I read everything.

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And one more thing. This method is about as simple as it gets, anyone can do it, and the skill is free. So if it was useful to you, share it. Send it to a friend, a colleague, anyone still stuck having the same conversation with AI over and over. You’ll be helping them work smarter, and you’ll be helping this newsletter grow, which means a lot to me.

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Every few weeks now, a new model comes out that’s a little more realistic, a little easier to control, a little less AI-looking.

The latest one is Gemini Omni, which Google launched just about a week ago. It’s different from Veo, and it lives right inside Gemini, where a lot of us already work.

I’ve been playing with it for the past three days. I wanted to see how easy it is to use, what it can handle, and whether I could start making videos with it for real. For my content, for marketing, for the kind of short clips that used to need a freelancer or hours in an editing tool.

So I tested seven different use cases and I’m going to walk you through all of them.

By the end of this article, you’ll have all the prompts behind my videos, a prompt generator I built that writes Gemini Omni video prompts for you, a curated library of prompts from across the internet, and every limitation I ran into along with workarounds where I found them.

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What’s in this article:

• What is Gemini Omni? Google’s new any-to-any video model explained

• How to access Gemini Omni: Gemini, Flow, YouTube Shorts, and the API

• Gemini Omni pricing: which plan do you need

• The 7 Gemini Omni use cases I tested

  • Use case 1: Turn your photos into social media videos

  • Use case 2: Product visualization videos (exploded view of a Rolex)

  • Use case 3: Talking character videos (an avocado that pitches itself)

  • Use case 4: Storyboard-to-video with image references

  • Use case 5: Kurzgesagt-style explainer animations (compound interest demo)

  • Use case 6: UGC-style ads with AI (solo traveler + Beats headphones)

  • Use case 7: Put yourself in any scene (the “ancient Rome” viral format)

• Gemini Omni avatar and video editing: what I couldn’t test yet

• Gemini Omni video ideas for every profession (12 examples)

• What Gemini Omni does really well

• Gemini Omni limitations and bugs to watch out for

• All my Gemini Omni prompts and the Omni prompt generator (+ a bonus prompt library)

• My verdict on Gemini Omni after 3 days of testing

What is Gemini Omni? Google's new any-to-any video model explained

Gemini Omni is Google’s new AI video model. What makes this one different: you can create video from text, images, audio, or existing video.

• Combine your own assets. Photos, music, clips, mixed with what it produces.

• Generate and sync audio. Voiceover, background music, sound effects, either from your own files or from a description.

• Readable text on screen. Captions, titles, and labels that you can post without going to an editor afterwards (more like Nano Banana does).

• Manipulate your own videos. Move yourself to a different location, add effects, change what’s happening in the background.

• Edit through conversation. Refine what it generates instead of starting over every time.

That last part is really new. Before Omni, every time you wanted to change how an AI-generated video looked, you had to revise the prompt and regenerate from scratch. The result was never the same twice. Editing was painful. Now you can say “make the lighting warmer” or “slow down the last 3 seconds” or “the people look too stiff, make it more natural” and it adjusts the existing video. That’s a fundamentally different way to work with AI video.

In Google’s language, it’s an “any-to-any” multimodal model. Input can be text, image, audio, or video. Output can be video, image, audio, or text. You can mix all of these in a single prompt.

How to access Gemini Omni: Gemini, Flow, YouTube Shorts, and the API

There are several ways to use Gemini Omni, depending on how much control you want.

Gemini (the simplest way)

Go to gemini.google.com, open the sidebar, click “Videos,” or select “Create video” from the + button in the chat. You can pick the format (landscape 16:9 or portrait 9:16), choose your model (3.1 Flash Light, 3.5 Flash, or 3.1 Pro), and create videos that are 4, 6, 8, or 10 seconds. You can upload images, audio, and video alongside your text prompts. This is where most people will start. (If you want a deeper look at everything Gemini can do beyond video, I wrote a full guide here.)

Google Flow (for more control)

Flow is Google's AI creative studio. You can generate and refine images, create characters, build storyboards, scenes, and produce videos, all from natural language prompts in a single workspace. You also get more control over things like frames, number of seconds, and how many variations you want to generate.

YouTube Shorts and YouTube Create (free)

This is the free entry point. You can use Omni’s video generation directly inside YouTube Shorts and the YouTube Create app. If you’re already creating content for YouTube, this is a good way to try it. Limited compared to the full Gemini experience, but enough to see what it can do.

The API (for developers)

The API (for developers). Omni isn’t available through the API yet, but Google confirmed it’s coming in the next few weeks. I’m also hoping it means that once it’s out, I’ll get to test video generation from my own videos and audio, since right now those features aren’t available yet in my country.

Gemini Omni pricing: which plan do you need

Gemini Omni is only available on paid plans. Google restructured its pricing around a credit system called Flow Credits, and they moved from daily prompt limits to compute-based usage that refreshes every 5 hours.

• AI Plus ($7.99/month): 200 Flow Credits. Enough to experiment and generate a handful of videos. Good for trying it out and seeing if it fits your workflow.

• Pro ($19.99/month): 1,000 Flow Credits. This is the sweet spot for most professionals and small businesses. Enough for regular content creation.

• Ultra ($99.99 or $199.99/month): 10,000 or 25,000 Flow Credits. Two tiers, both aimed at heavy users. If you’re running an agency or producing video content daily, this is where you go.

The Gemini pricing page has the full details. One thing to keep in mind: video generation uses significantly more credits than text or image generation.

I have the Pro plan and every 3 video generations, I had to wait 5 hours before I could generate again. To test everything I’m sharing in this article (plus many other things I tried), I ended up using multiple accounts, and I still had to wait many times for the limit to reset.

The 7 Gemini Omni use cases I tested

I tested Gemini Omni across seven use cases. Some came from you, like , who wanted to see video animations of how things work or characters talking. Others are general use cases you might need to generate videos for.

For each one, I’ll share what I was trying to do and what came out of it.

All videos below were made using the Gemini 3.5 Flash model, the latest one, and they're all done from the Gemini app. If you want more complex compositions with multiple videos, I'd suggest using Flow (I might cover that in a future tutorial).

Now... let’s get to the fun part.

Use case 1: Turn your photos into social media videos

You already have the photos. They’re sitting in your camera roll, on your Google Drive, maybe in a shared folder from that photographer you hired two years ago. The storefront, the product shots, the team lunch, the latte art. They’ve been used once (maybe twice) and then forgotten.

What I tested: I grabbed 5 stock photos of a coffee shop (see below) and asked Omni to create a 10-second “come visit us” reel.

Result:

Find the full prompt and the Omni prompt generator by the end of the article.

What happened: It preserved everything in each shot from the images and followed my prompt closely, so the whole thing feels very realistic. It looks like someone took the video, not like AI generated it. No extra elements that weren’t already in the images I attached. I really love this one.

Use case 2: Product visualization videos (exploded view of a Rolex)

If you sell a physical product, you’ve seen those slick animations where the product floats, spins, breaks apart to show its insides, then reassembles. They’re everywhere in tech marketing, on Apple’s site, in Kickstarter campaigns, in every product launch video on YouTube. They’re also expensive. A motion designer, 3D renders, weeks of back and forth.

What I tested: I uploaded a photo of a Rolex mechanical watch and asked Omni to create a 10-second product visualization worthy of a Rolex commercial. Exploded view, floating components, gears still spinning mid-air, reassembly, tagline at the end.

The result:

Find the full prompt and the Omni prompt generator by the end of the article.

What happened: This worked like a charm. The Rolex in the video looks exactly like the one in the photo I attached. You can use this for product showcases, landing pages, social content, anywhere you need to show what a product looks like in motion.

Use case 3: Talking character videos (an avocado that pitches itself)

You’ve seen these. A stomach complaining about what you ate last night. An avocado listing all the reasons it deserves to be in your smoothie. Talking vegetables, body parts, household objects explaining themselves, what they do, why they matter, why you should care about them. They’re one of the most viral formats on social media right now, across Reels, TikTok, and Shorts. The absurdity of the character makes people stop scrolling. The self-aware education makes them stay.

What I tested: I asked Omni to create a talking avocado selling you on why it should be in your diet. Not a lecture. A pitch. From the avocado itself.

The result:

Find the full prompt and the Omni prompt generator by the end of the article.

What happened: It worked really well, but I had to iterate. The script I started with was too long and the important parts kept getting cut off when generating the video. If you keep the script to what fits, it builds a full composition with talking, scene changes, and it doesn’t feel rushed. Once I figured that out, the result was solid. You can create this kind of viral content yourself now.

Use case 4: Storyboard-to-video with image references

The idea is simple: you create a storyboard first, a set of illustrated frames showing your story scene by scene, then you hand that storyboard to Omni as a reference and ask it to turn those frames into video. Two steps. Image first, video second.

What I tested: I created two storyboards for short animated scenes, six panels each, one for each shot. I generated both storyboard images first using ChatGPT’s image generation, then fed each one to Gemini Omni along with a detailed prompt and asked it to create the video following the frames exactly.

Result 1: The last brushstroke, a mural painter finishing her work through the night

Result 2: The AIBMM newsletter, a glowing envelope traveling from Bucharest across the world to its readers

Find the full prompts and the Omni prompt generator by the end of the article.

What happened: It took a few attempts. Omni can be picky. Too many scenes and it either stalls or takes much longer than the usual couple of minutes. And the result didn’t always look great. Fitting six storyboard frames into 10 seconds is a lot to ask from one generation. What I’d do differently: instead of cramming the full storyboard into one prompt, break it apart. Generate video from one or two frames at a time, get each scene looking right on its own, then stitch them together. You get better results, more control, and Omni doesn’t have to solve everything at once.

Use case 5: Kurzgesagt-style explainer animations (compound interest demo)

If you teach, coach, or create courses, you’ve probably wanted animated explainers for your content. The kind you see on YouTube channels like Kurzgesagt, with bold flat colors, smooth transitions, and concepts that click visually in ways that words alone can’t do. The problem is these cost thousands to produce and take weeks to make.

What I tested: I asked Omni to create a 10-second animated explainer of how compound interest works, in a Kurzgesagt style. Bold flat-design coins that multiply on screen, a year counter ticking, an exponential curve drawing itself, and clean text labels walking you through the math.

The result:

Find the full prompt and the Omni prompt generator by the end of the article.

What happened: This one got a bit too long. It’s a composition that might work better in Flow, and it would probably land better as two or three shorter videos to finish the explanation rather than trying to cram it all into one. It still looks really good though.

Use case 6: UGC-style ads with AI (solo traveler + Beats headphones)

UGC ads are everywhere, and so are AI-generated ones. I know this is a controversial topic, but it works really well in Omni, so it’s worth showing. UGC creators are expensive, $200 to $500 per video, and you need multiple variations to test what works. If you’re not a company with the budget to invest heavily in promoting your products, it adds up fast.

What I tested: I uploaded a photo of a woman traveling and a product shot of Beats Solo 4 headphones in pink. I asked Omni to create a UGC-style ad where she’s walking toward camera on a sunny street, wearing the headphones, talking about why she loves them for solo travel.

The result:

Find the full prompt and the Omni prompt generator by the end of the article.

What happened: The clothing, the scenery, the headphones, it preserved all the elements from the images and the whole thing feels natural. Almost scary how well it kept everything consistent. The lip sync is great too.

Use case 7: Put yourself in any scene (the "ancient Rome" viral format)

You’ve seen this one everywhere. A creator teleported into ancient Rome, walking through the Colosseum, talking to camera like a travel vlogger who accidentally time-traveled. It’s one of the most viral formats right now.

What I tested: I uploaded a photo of myself and asked Omni to place me inside the Colosseum during a live event, vlog-style, reacting to everything around me.

The result:

Find the full prompt and the Omni prompt generator by the end of the article.

What happened: The scene and setting looked great. But it didn’t really look like me. I used a single selfie, not a video or a pre-built avatar, so the model didn’t have enough to work with. For this format to really work, you need the avatar and video input features, which circles back to the regional limitations I mentioned.

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Gemini Omni avatar and video editing: what I couldn't test yet

I could only create videos from images and text. Audio and video as inputs aren't available in Romania yet. So there are things I couldn't test, and among all of them, these two are the ones I was most curious about:

Clone yourself

I’d be really curious to test this when the API comes out or when the avatar feature becomes available to me. From what I’ve seen other creators do with it, the avatar really looks and sounds like you. Here’s how you set it up:

• Open Gemini, go to settings, click on Avatar

• Hit the Try Now button and scan the QR code with your phone or tablet

• It’ll ask you to speak some numbers out loud and scan your face

• Once it’s done, your avatar is ready. You can use it or retake it if you didn’t like the first one

From there, you can generate videos of yourself without ever picking up a camera. You can even take the motion from a reference video and apply it to your avatar, match the rhythm to a music track you upload, and pick the format you want.

Video manipulation

This one is great for anyone who already creates content with themselves in it. You can upload a video of you doing something and change what’s happening around you:

• Move yourself to a different location

• Add effects to the background

• Snap your fingers and the lights go off, then come back on

• Add things that didn’t happen when you filmed it

You can take content you already have and make it more dynamic, more visual, more fun to watch, without reshooting anything.

Gemini Omni video ideas for every profession (12 examples)

Beyond what I tested, here’s a longer list of what you could create with Omni depending on what you work on:

• Any physical product: exploded view animations, unboxing reveals, ingredient breakdowns, cross-sections, try-on videos, product demos. Works for skincare, electronics, food, clothing, jewelry, supplements, furniture, anything you can photograph

• Restaurant, bar, salon, or studio: upload photos of your space, your dishes, your work. Create a mood reel with a CTA at the end

• Real estate or property development: upload listing photos or architectural renders. Create walkthrough videos of spaces, including ones that don’t exist yet

• Course creator or teacher: animate concepts from your curriculum. How a funnel narrows, how the water cycle works, how a pricing tier is structured. Anything that clicks faster as animation than as text

• Financial advisor or coach: explain budgeting, debt snowball, portfolio diversification, or tax brackets as short animated explainers

• Fitness or health coach: show how muscle recovery works, how progressive overload builds strength, how macros break down in your body

• SaaS founder: explain what your product does in 10 seconds. The onboarding flow, the data pipeline, the integration

• Freelancer or consultant: upload your workspace, a deliverable, and your headshot. Create a personal brand reel

• Event organizer: upload behind-the-scenes photos from a past event. Create a recap reel to promote the next one

• Marketing and ads: generate multiple UGC-style variations for testing, product showcase videos for landing pages, short social clips from existing photos

• Personal memories: upload travel photos and ask for a warm nostalgic mood reel with smooth transitions

What Gemini Omni does really well

• It preserves everything in your images. When you upload photos, it keeps the objects, the details, the labels, the colors, everything. Product photos stay accurate. A Rolex looks like a Rolex. A coffee shop looks like your coffee shop. This is where Omni is strongest and it’s what makes it practical for businesses that already have assets they want to turn into video.

• It feels made for social. The output has a natural, realistic quality that looks like it belongs on Instagram or in a Facebook ad. Both the animated content and the realistic content have this social-native feel that you could post.

• It’s easy to use. If you’ve struggled with prompting in other video tools like VEO, Omni is a relief. Less complex prompting, less effort to get something decent. You describe what you want in normal language and it mostly understands.

• Audio sync is tight. When I added voiceover, the speech matched what was happening on screen. When I described background music, the mood fit. This wasn’t something I had to fight with.

• Text on screen is readable. Previous AI video models produced text that looked like it was melting. Omni renders text that you can read. Captions, titles, labels, they work.

Gemini Omni limitations and bugs to watch out for

What frustrated me the most is that it can feel buggy. Some videos generated in under a minute. Others took 10-15 hours or never finished. I couldn’t pinpoint why. When I deleted those and retried the exact same prompt from scratch, it generated in a minute. It just came out a week ago, so this probably still needs to be fixed, but it’s worth knowing going in.

Beyond that:

• Long scripts get cut off. If you write too much dialogue for the time available, the video just stops before it finishes all your scenes. It won’t compress the speech to fit. It just cuts.

• Too much in one prompt and it stalls. If the scenes or the script don’t fit in the maximum time, more often than not it just won’t generate at all. It gets stuck and nothing comes out.

• Content policy blocks can be unpredictable. I tried to generate a ballerina working hard with bleeding toes as part of a dramatic storyboard, a rising story where all the hard work pays off. Maybe it made sense that it refused that, but a lot of Reddit users report getting blocked on prompts that seem completely innocent too.

• Video editing and manipulation don’t work in all regions. This was my biggest disappointment. I tried editing videos I’d already generated and they just never finished. It’s been 72+ hours and they’re still pending. I’m assuming this is because video generation from video doesn’t work in my country, and that’s probably why editing doesn’t work either, but I don’t know for sure. I’ll keep testing. Uploading videos and cloning myself didn’t work either. Sometimes it told me the feature isn’t available in my country. Sometimes it just sat there pending and never generated. Even with a VPN, nothing changed.

• It can be buggy under load. I had videos loading for 15 hours that never finished. When I launched too many generations at the same time, things broke. Eventually I deleted those conversations and started fresh. Sometimes you also get “too many requests right now, please try again later” and there’s nothing to do but wait.

• Maximum length is 10 seconds. For anything longer, you’re generating multiple clips and assembling them yourself.

• Character consistency can drift. Across multiple scenes within one video, subtle details sometimes shift. Clothing colors, facial features, small things that break the illusion if you look closely.

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All my Gemini Omni prompts and the Omni prompt generator

Here’s everything I promised throughout the article. Below you’ll find:

1. The spreadsheet with every prompt I used for every use case. Copy them, tweak them, use them as starting points for your own videos.

2. The prompt generator I built that writes Gemini Omni video prompts for you. Tell it what you want, it handles the structure, the timing, and warns you before something will break.

   If you have Amplifiers set up, it’s available there too:

   

3. Bonus: a curated prompt library I put together from prompts I found across the internet. Use it as inspiration.

Upgrade to Premium to access everything.

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But a few of them are so good that they blow me away every single time I use them:

1. The writing skills. My voice DNA skill makes Claude write like me. My audience profile skill means Claude always knows who I’m writing for. My business profile means Claude always knows who I am, what my business is, and everything it needs for anything I create. These are universal. Claude applies them to almost everything I do.

2. The brand skill. This one makes sure everything Claude generates (documents, decks, PDFs, Word docs, dashboards, tools) comes out in my brand style. And if you ask me, I think it's better than Claude Design because the skill applies automatically every time I create something, which is way more convenient than opening another app. Once I had this in place, I was also able to build my lead magnet skill on top of it, because Claude already knew my brand.

3. The carousel skill. And this is the one I’m going to share with you today.

A few weeks ago I tried building Instagram carousels with GPT and Nano Banana, but I didn't put enough thought into it and wasn't happy with how they looked. Yet I knew it could be so much better, because GPT Images 2 is that good. So I set it aside wanting to retry it properly.

When I finally sat down and spent a full day on it, and saw how the results turned out… I opened an Instagram account that same day. And a Threads account. And a Facebook account. And in just two days, I built a month of daily content from carousels.

Because now I can repurpose my content by just dropping a link to my articles and asking Claude to create a carousel. That’s how easy it is.

After I shared it in the community chat, a lot of you got excited and wanted to build something similar.

I didn’t just want to share my skill with you. Having my carousel style wouldn’t do much for you, because yours should look like yours.

So I built a workflow that lets you create your own custom style, whether that’s for repurposing written content like mine, promoting your product, or anything else.

And now that I've tested it on multiple brands and seen the results, I'm convinced it does a great job. So today I'm handing it over.

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In this article:

• Three Instagram carousel styles I built with Claude (and how each one started)

• How the carousel workflow works (the big picture)

• What you need before you build your carousel skill

• Step-by-step: how to build your own Instagram carousel skill in Claude

• See it in action: how I built the carousel skill for Playsense (video demo)

• Tips and tricks for building a carousel skill that works for you

• Now go build yours

Three Instagram carousel styles I built with Claude (and how each one started)

To show you how flexible this is, I built three different carousel styles for three different brands. Each one has a completely different vibe, different colors, different layouts.

Mine came first through a lot of messy iteration, and the other two were built through the workflow I created from that process, way more organized and guided.

Style 1: AI Blew My Mind — realistic, modern, screenshot-friendly

What I started with: Brand colors, fonts, and reference images from other people’s carousels that I liked.

The vibe I wanted: Modern, clean, realistic. Something where I could either pull images from my article, upload my own screenshots, or let it generate visuals from the content.

Here’s what it looks like when I share screenshots and the skill styles them into the carousel:

And here’s how it looks without any screenshots, where I just give it content and it generates the visuals:

And one more, where it takes my article, analyzes the images in it on its own, and recreates them so they fit the carousel. These aren’t screenshots, but they’re not built from the LLM’s imagination either. They’re inspired by the content and images I shared in my article on Cluing and recreated:

Style 2: Playsense — soft, drawing-style for an AI parenting app

To test more varied styles, I also built a carousel style for Playsense, the AI parenting app that my business partner Matei is building.

What I started with: A gradient background image (the one you see in slide 1 below), the website URL, and the brand colors. Then I picked a style from the workflow I created, and iterated from there.

The vibe we wanted: Drawing-like, softer, with different backgrounds on the hook and CTA slides. No titles on each slide.

I also created a second style for Playsense, more promotional and a bit more realistic, while still keeping the brand colors and overall feel. (You can build multiple styles in the same skill if you want to switch between them based on what you're posting.)

Style 3: Robots Ate My Homework — papyrus-toned, on-brand for

Because I was still in test mode and wanted to make sure the workflow can generate diverse styles for different brands and visions, I offered in the AI Blew My Mind community chat to build a skill for one premium subscriber.

Mia responded first, so I built it for .

What I started with: The brand logo, colors, chosen fonts, a couple of reference images she had created earlier, with comments on what she liked and didn’t like in each one, and some direction on the visual mood she was going for.

The vibe Mia wanted: On-brand, logo on the first slide, papyrus color background, consistent with her existing visual identity.

Done with the demos. As you can see, very different styles, very different brands, all built from the same workflow. Whatever vision you have for yours, this can fit it.

How the carousel workflow works (the big picture)

Now that my skill is working, using it is the easy part. All I do is this:

1. I give it an article or a piece of content. I can paste text, drop a link, mention a topic, whatever. You can use the slash command like I did with /instagram-carousel, or just mention “carousel” and Claude will know what to use anyway.

2. Claude proposes a storyline. It gives me a copy and visual proposal for each slide. If I approve, it moves to generation.

This middle step is where I still iterate sometimes. The copy or the hook. Maybe I want a different direction, maybe I don’t like a specific word, maybe I visualize something different. That’s the human-in-the-loop part.

3. Once I'm done with that, Claude starts generating the images, and I'm ready.

That’s it. Simple as that.

What you need before you build your carousel skill

Start by setting aside 30 to 60 minutes, then make sure you have these things in place before you begin.

1. Your visual branding (colors, logo, fonts, references)

Prepare whatever you have:

• Brand colors (this is the only thing that’s mandatory)

• A logo, if you have one (Claude can extract your colors from it)

• Fonts, if you have specific ones

• A website, so Claude can analyze your style

• Reference images, if you have something you created in the past or something you liked on Instagram

If you don't have reference images, Claude will show you a style gallery I created so you can pick one that fits (see below). It's a starting point, not a locked-in choice. You can pick something that resembles what you have in mind and then iterate on it until it works for your brand.

2. Amplifiers (AI Blew My Mind MCP)

You need Amplifiers set up, which gives Claude access to 30+ tools, my prompts and workflows (including the one that will build your carousel skill), and image generation through GPT Images 2 and Nano Banana.

You can also use this workflow in ChatGPT without API keys, but ChatGPT doesn't have skills. You'd save the instructions in a Custom GPT instead.

3. Your API keys for image generation (GPT Images 2 or Nano Banana)

Add your API keys at auth.aiblewmymind.com before you start. The workflow defaults to GPT Images 2 for generation, and I'd recommend keeping it that way because the results are so much better.

As for cost, it’s around $1 for 20+ images. And if you live in Claude like I do, this setup makes more sense than using ChatGPT for carousels. Claude already knows your style, your writing, your preferences. (And PS. you don’t need a ChatGPT subscription just for images when you can add your own API key and likely spend less.)

Step-by-step: how to build your own Instagram carousel skill in Claude

I spent a couple of days building a workflow that guides you through the whole thing. Claude walks you through every decision step by step, so all you need to do is follow along and give feedback.

By the end of it, you'll have your own carousel style ready to package into a skill.

Even though I'll show you the process for Claude, you can use it in ChatGPT too if you have Amplifiers set up there. Once you have the final Markdown file with your skill's instructions, you can add it to a custom GPT.

Open a new chat in Claude (or ChatGPT) and call the Amplifier

Either tell it what you need:

“I want to create a carousel skill, find an amplifier that can help me.”

Or go straight to it:

“Use the carousel skill builder amplifier and help me build my custom carousel skill.”

Follow the step-by-step workflow

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A few weeks ago I showed you how to build a voice AI agent in 30 minutes with ElevenAgents. Whether you need a customer support agent, an AI receptionist, or an outbound sales assistant, that article covers how to get one running with no code, no technical background, in under an hour. Every click, every prompt, every setting.

What I wanted to show you this time is what it looks like when you put a voice agent inside a real product. Not a demo, not a tutorial. A real app built by someone I work with every day.

is my business partner. He’s been building Playsense, a parenting app, for about a year. He’d tried having voice conversations with AI before through GPT and other LLMs, but the experience was never good enough. The AI would interrupt you mid-sentence. It couldn’t tell the difference between a pause and a finished thought. It never felt like you were talking with someone and it was just annoying.

With ElevenAgents, that changed. The agent waits. It recognizes when you’re still mid-thought. It adjusts its tone to match yours. And for the first time, the voice interaction felt like a conversation worth having.

So Matei wrote this piece to walk you through what he built, how voice fits into Playsense, and what a voice agent can do when it’s designed around the person using it, not just the business running it.

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What’s inside:

• Why most AI parenting tools are solving the wrong problem

• How a voice AI agent changes what parents notice about their kids

• What Playsense does and how ElevenAgents powers the voice experience

• The case for voice AI agents beyond sales and support

• A TestFlight link if you want to try Playsense

I’ll let Matei take it from here.

Your child is not a project

The play area was about to close. A bored teenager with a clipboard had started telling families it was time to leave. My two-year-old son started running towards the exit in a panic. Not because of unspent energy. He was freaking out. At the exit he met another staff member, signaled him to STOP, then ran back to the two girls he’d been playing with and started pulling them towards the door.

I did exactly what every parenting book and every well-meaning relative tells you to do. I praised his sense of responsibility for the girls, his independence, his agency.

Six months later, in a toy store we’d entered just before closing, he did the same thing the moment the shopkeeper started pulling down the metal shutters. We headed for the exit, and then he panicked about our friends who were still inside, ran back after them, shouting. And just yesterday, at a friend’s barbecue, one of the older kids put him in a “jail” (an innocent game where the jail was a corner of the couch, not enclosed in any way at all). He panicked anyway.

I hadn’t connected any of this. Each instance had felt like its own thing. A brave moment. A long day. A sensitive moment.

It wasn’t.

My son is afraid of being trapped. Specifically, the moment of being trapped. Even the idea of it. Once I saw it, I couldn’t un-see it. And once I started journaling about it in the right tool, I realized I had probably observed it ten times already and dismissed it as ten different things.

I built that tool. And the reason I’m writing about it here, in a newsletter you mostly read for AI at work, is that I think we’re collectively missing one of the most interesting things AI has done in the last year, and it’s not happening in your inbox.

The interruption

There’s a split happening in AI that almost no one is naming. There’s productivity AI (do the thing faster) and there’s attention AI (notice the thing better). Almost everything you read about, including in this newsletter, is in the first category. The second category is much smaller. Much weirder. And, I think, much more important.

Playsense is in the second category. It’s an AI for parents, and its primary job isn’t to give you advice, generate meal plans, or help you book the pediatrician faster. Its job is to help you see your own child more clearly than you could on your own.

That sounds soft. It isn’t. I think it’s the most underrated use of AI of the past 18 months, and almost nobody is building for it.

Carpenter parents and gardener parents

The developmental psychologist Alison Gopnik has a phrase I think about constantly: the gardener and the carpenter.

Carpenter parents build a specific kind of person from a blueprint. They have a vision of who their child should become (confident, independent, athletic, articulate, kind, gifted at music) and they spend eighteen years sanding the kid into shape. Most parenting books are written for carpenters. Every kindergarten KPI is a carpenter’s KPI. Every “five habits of high-achieving toddlers” listicle is a carpenter manifesto.

Gardener parents create conditions and observe what grows. They notice what their child reaches for, what they recoil from, what they keep coming back to. They suggest, they nurture, they protect, but they don’t build, because they understand they’re not the architect.

The dominant cultural mode is carpentry. And AI is now turbocharging it: faster optimization, faster benchmarking, faster comparison to other people’s blueprints. There are at least a dozen AI parenting apps in the App Store already, and almost all of them are tools for better carpentry.

I built Playsense because I wanted a tool for the gardener.

The diary almost no parent keeps

If you ask any developmental specialist what the single highest-leverage practice is for understanding your child, almost all of them will say the same thing: keep some kind of observational journal. Maria Montessori built her entire pedagogy on this. Pediatricians ask for it. Therapists treasure it.

Almost no parent does it.

I didn’t, and I tried. Three weeks in, every time. The reason is mundane: the cognitive cost of asking is this worth writing down? before every entry kills the practice. The most important observations are usually the small, weird, in-between ones, and those are exactly the ones that don’t pass the journal-worthiness filter. So you write down the first time your kid says “I love you,” and you don’t write down the moment three months earlier when they pulled away from a hug for the first time. The first one becomes a nice memory. The second one becomes nothing. When in fact the second one was the more important data.

Playsense’s first job is to lower the friction of that practice to the point where you keep at it. You can write to it like a chat. You can drop in photos from a continuous photo feed and add a sentence. You can ask it to start a conversation with you when you’re staring at a blank text box.

Why voice changes the math

Talking is the medium of paying attention out loud.

When you write, you pre-edit. You decide what counts. You delete the stuff that feels too small. When you talk, you ramble. And the ramble is where the real observation lives. The throwaway sentence. The aside. The “oh, and also.”

The Playsense voice agent is built for that ramble. You can talk to it the way you’d debrief a long day with a friend who happens to know your kid well, except this friend remembers everything, and extracts and updates the profile in the background. Same pipeline as the chat. Same fact extraction. Same merge logic. Same context-aware replies. The only difference is it’s three minutes of talking instead of fifteen minutes of typing, which is the difference between a habit you keep and one you don’t.

It runs on ElevenAgents platform, and I want to come back to that in a minute, because I think they’re sponsoring something more interesting than they realize.

What the Playsense voice agent sounds like

The thing that makes Playsense different from every AI parenting tool I’ve tried is what the agent doesn’t do. It doesn’t lecture. It doesn’t recommend a parenting philosophy. It doesn’t quote any of the parenting authors you’d expect it to quote. It doesn’t tell you that you’re doing too much screen time or not enough emotional regulation work.

What it does is mirror. It identifies trends, traits, inclinations, and small contradictions in what you’ve told it over time. It suggests things to try based on what you’ve observed about your kid, not what some study said about a generic four-year-old in California in 2018.

Here’s roughly what that looked like when the clinginess pattern surfaced:

When my son’s pattern came up, the agent didn’t tell me what to do about it. It laid out what it had noticed across separate entries, asked me a few questions to confirm whether I saw the same pattern, and then suggested a few things to try the next time we approached a situation involving an enclosure or a closing. Small experiments, not interventions.

That distinction matters. An intervention assumes you already know what’s wrong. An experiment assumes you don’t, and is designed to teach you something either way.

The rest of the app

The voice agent is the new thing, but it sits inside a product that already does a few things worth showing.

The chat diary. Open the app, type or talk, done. The agent extracts events, facts, observations, and milestones, and merges them into the profile in the background. Age is always part of the context, because what matters at three doesn’t matter at five.

Gallery catch-up. Most parents have thousands of photos and zero diary entries. Playsense gives you an infinite photo feed where you select a few photos or videos, write a sentence, send to the diary, and keep scrolling. You can catch up on three months in fifteen minutes without leaving the feed.

Multi-player. Playsense is built for the people who do the raising. You, your partner, the godmother, the grandparent, the nanny, whoever’s in. Everyone contributes observations. The profile gets richer than any single perspective could make it, and the blind spots of any one caregiver get filled in by the others.

The evolving profile. Behind the scenes there’s a structured profile of your child and the family that updates with every interaction. Facts can change. Phases end. Age is always weighted. That profile is the context for every conversation, which is why the agent’s replies feel specific instead of generic.

A note on the build: ElevenAgents under the hood

I want to say something about the technology underneath this, because it surprised me.

I logged into ElevenAgents and started poking around. The system prompt field was obvious. That’s what you use with any AI agent. Dynamic variables were the next thing I noticed, and that immediately answered the question I had walked in with: how do I give the agent the right context for this specific parent and this specific child? They also publish agent skills, which meant I could realistically expect Claude to one-shot the implementation. It did.

Two things really blew my mind.

The first: I can set a different voice for each language, and the agent auto-detects which language the conversation is in when it starts. Romanian parents and English parents talking to the same agent get a voice that fits.

The second: I can set the agent’s mood, and I can specify when the mood should change inside a conversation. That’s not a feature I knew I wanted until I had it. It is the difference between a voice that sounds like it’s reading a script and one that sounds like it’s listening to you.

And then the thing I didn’t expect at all. Every voice agent I’d used before had the same anxiety-inducing flaw: the moment you pause, the agent jumps in. You can’t think out loud. You can’t take three seconds to choose the right word. The result is that you start rushing your replies, just to head off the agent’s interruption. Talking to most voice agents feels less like a conversation and more like a quiz with a buzzer.

ElevenAgents lets you ask the agent to wait and skip its turn. This feature lets you think and it makes a world of difference.

What ElevenAgents is building

I want to close with a thought that’s not really about Playsense.

If you’ve encountered a voice agent in the wild this year, it was almost certainly one routing your support ticket, confirming an appointment, or selling you something. That’s the dominant story about voice AI right now.

But voice agents are becoming something broader, a new category of intimacy interface. Products you talk to about your own life, not products that talk at you about theirs. Voice journals. Voice companions for grief. Voice tools for therapy homework. Voice notebooks for caregivers.

The pattern is the same: voice removes the cognitive overhead of writing, and AI removes the cognitive overhead of organizing what you said. The combination produces a kind of observational capacity that didn’t exist before.

ElevenAgents is, as far as I can tell, the company building the infrastructure for all of it. The productivity use cases, the personal ones, and everything in between.

They sponsored this piece, and I want to be straightforward about that. But the reason I built Playsense’s voice agent on their platform is that the latency, the naturalness, and the conversational flow are all good enough that the technology disappears, which is the only way any of these applications work.

A word before you tap the link

I’m putting a TestFlight link at the bottom of this piece. Before you tap it, the honest version of what you’re getting:

Playsense has been in development for about a year. I expect it to take another year before I’m happy with it. And that’s not a technical timeline. The technical part is mostly working. The part that takes time is figuring out what is helpful in parenting, which is a slow problem that requires real users, real children, real observations over months, and a lot of revision.

So: expect bugs. Expect features that are clearly half-baked. Expect things that look good in concept and don’t quite work in your hands yet. The one thing I can promise is solid is the core: the journaling captures what you say or write, and the profile extraction is real and gets richer the more you use it. Even if everything else around it changes (and it will), that part is reliable, and that part alone is, in my experience, worth the price of the friction elsewhere.

If you’re the kind of person who wants polished, ship-it-when-it’s-ready software, give this six months. If you want to be early and you’re okay using something rough that’s trying something new, the link is below.

One last thing

Productivity AI helps you do your job faster. The most important job you have as a parent is the one that doesn’t reward speed.

Your child is not a project. They are a person you’ve been given the chance to slowly come to know. Most of the tools you’ll be sold this year will help you optimize them. A few of them, and I hope Playsense becomes one, will help you observe them.

If you want to try it, check out: https://playsense.app/

If you want to forward this piece to your adult kids who are now parents, that might be the most useful thing you do with this email today.

Matei

Whether you’re building a productivity tool, an attention tool, or something else, ElevenAgents has taken the voice AI experience to a different level. If you were worried that voice agents don’t sound natural enough or that users will get annoyed by awkward pauses and interruptions, that’s not the case anymore. And you don’t need any technical skills to build one. It’s just clicks.

Sign up for ElevenAgents using my link for 11% extra credits when you upgrade.

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If you don’t want to read this article, just watch this 10-minute video:

For everyone who prefers reading, let’s go.

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In this article:

• Where prospect research usually goes wrong

• The AI-powered workflow I use now

• The step-by-step process

• Setting up Amplifiers (1 minute)

• More AI workflows for sales and client work

• How to find the right workflow for any task

• Try it before your next call

Why most prospect research takes 30 minutes (and still misses)

You get a name. Maybe a company. And then you do one of three things:

1. The manual way: LinkedIn + Google + scattered tabs

You open their LinkedIn, read their profile, switch to Google, click through a few results, maybe check their Twitter. It takes 20 to 30 minutes per prospect, and even when you put in the time, you end up with surface-level information. Not the kind of context that helps you position yourself well or ask the right questions in a call.

2. The DIY automation way: APIs + workflow builders + technical setup

You connect APIs, set up an n8n or Make workflow, pull data from LinkedIn and social platforms, and add an AI layer that creates the report from all the info. But it takes technical knowledge, API keys, and hours of setup. And if you want to change anything later, you’re back in the workflow builder tweaking nodes. Most non-technical people never get there.

3. The “I’ll just wing it” way

No research at all. You walk into the call cold, write the message off the top of your head, or blast the same pitch to fifty people. I get these on LinkedIn all the time. Messages from people pitching me services that have nothing to do with what I do. It’s obvious they didn’t spend a single minute looking at my profile. That’s not prospecting, that’s spam. And besides wasting your own time, you’re annoying the exact people you’re trying to build a relationship with.

My 2-minute AI prospect research workflow with Amplifiers

Amplifiers already connects Claude and ChatGPT to LinkedIn, Google, Reddit, Twitter/X, company registries, and the open web. The AI Prospect Research Workflow ties all of these tools together into a step-by-step process that Claude runs for you, pulling data from each platform and building a full report based on your goal.

Think of it as the n8n workflow, but without any of the technical setup. You don’t have to manage API keys or connect nodes or figure out how to wire tools together. And if you want the report to focus on something different, you just say so in the conversation. You don’t have to go back and rebuild the workflow. It adapts to what you ask for, which makes it much more flexible than anything you’d build yourself.

And instead of thirty minutes of tab-switching and skimming, you get a full prospect brief in about two minutes.

What's inside an AI Prospect Research brief

• Company overview: What the company does, how it’s positioned, what it’s saying publicly, recent activity, legal entity, marketing and ads presence, reviews and press coverage

• Industry context: Top pressures, industry language to know, what’s shifting right now

• Person profile: Career trajectory, professional background, what they’re saying publicly, tone and personality signals, social media presence across platforms

• Fit with your work: Assumptions about what they’re likely dealing with, where you could step in, what to watch out for when pitching, and whether they’re the right contact

• Sources for everything: Links to every claim. Anything Claude couldn’t confirm is flagged as an inference, not stated as fact.

I wanted to demo this on someone real, so I picked , one of my earliest subscribers and a person I have a lot of respect for. He’s a learning scientist and CEO of . The report surfaced things I wouldn't have found on my own, even if I'd spent thirty minutes looking.

Because even if you do put in the time, you’re still just skimming profiles and clicking through search results.

The workflow reads everything it finds across platforms, every post, every article, every public mention, and connects it all to your specific goal. You’d have to scroll for hours to get to the same level of insight, and you’d still miss the connections.

How to research any prospect in 2 minutes (step-by-step)

You give it a name, tell it why you’re researching, and it produces the report. That’s how simple it is to use this workflow.

Step 1: Ask Claude to run the workflow

Open a new chat and say something like:

“Use an amplifier to help me research a prospect.”

Claude will search the Amplifiers library and find the Prospect Research Workflow.

Step 2: Give it the prospect’s name

If you have their LinkedIn URL or company name, drop that in too. The more you give it, the less time it spends confirming identity.

Step 3: Give it context (don’t skip this)

Tell Claude why you’re researching this person. Are you selling a service? Preparing for a collaboration? Scoping a proposal? Prepping for a call?

The context shapes the entire report, especially the “fit with your work” and “what to watch out for” sections.

Then you wait about two minutes while Claude does the research and delivers the full report.

What to do after your Prospect Research Brief

The report is the starting point. From there, you can stay in the same chat and ask Claude to:

• Draft a cold outreach message based on everything it just found

• Write a personalized email that references their recent work

• Prepare questions for a discovery call

• Help you position your pitch based on their likely challenges

Claude already has all the context from the research, so anything it writes from that point is informed by what it found. And because Claude also knows your preferences, your skills, and how you work, whatever you create next draws from both the research on your prospect and everything Claude already knows about you.

How to set up Amplifiers in Claude and ChatGPT

For any of this to work, you need to connect Amplifiers to Claude or ChatGPT first, whichever you prefer to work in. It takes about a minute.

In Claude:

1. Go to Customize in the sidebar, then Connectors, then click the + icon

2. Select Add custom connector

3. Name it AI blew my mind and paste this URL: https://mcp.aiblewmymind.com/

4. Click Add, then go back to Connectors, find AI blew my mind under “Not Connected,” and click Connect

5. You’ll be redirected to auth.aiblewmymind.com. Create your account using the same email you use for your AI Blew My Mind subscription

That’s it. You’re connected.

In ChatGPT:

1. Go to chatgpt.com in your browser. Open Settings → Apps → Advanced Settings.

2. Toggle on Developer mode.

3. Click Create apps. Name it “AI blew my mind”. In MCP Server URL, paste: https://mcp.aiblewmymind.com/

4. Set Authentication to OAuth. Click Create, then Connect.

5. Create your account on auth.aiblewmymind.com using the same email as your AI Blew My Mind subscription to connect.

More AI prospect research workflows and prompts inside Amplifiers

The Prospect Research Workflow is one of many. Inside Amplifiers, there’s a full set of workflows and prompts that cover the entire arc of finding, researching, pitching, winning, and delivering client work.

How to find the right Amplifier for any task

There are so many workflows and prompts inside Amplifiers that a lot of you have been asking me how you’re supposed to keep track of everything.

The thing is, you don’t have to. You just tell Claude what you’re trying to do and it finds the right one.

Here are a few ways to navigate Amplifiers:

1. Task-first: Tell Claude what you’re trying to do and ask “Find an amplifier that can help me with this.”

2. Browse everything: Ask “What’s everything inside Amplifiers?”

3. Personalized recommendations: Ask “Based on everything you know about me, which amplifiers would help me most in my work?”

And if you want to run prospect research briefs on multiple people at once, you can give Cowork access to a spreadsheet in a local folder with a list of prospects and ask it to research them one by one. That’s how you scale this.

The new way to do prospect research

I kept this article shorter than usual on purpose. If I were writing about how to build a prospect research system from scratch, connecting APIs, setting up n8n workflows, managing enrichment tools, this would be four or five thousand words. I know because I’ve written those articles.

But that’s the whole point. You don’t have to build any of it.

We added the APIs to 30+ research tools directly inside Amplifiers, so Claude and ChatGPT can pull real data from a bunch of platforms without you touching anything technical.

All you set up is Amplifiers. After that, every workflow runs inside Claude or ChatGPT, right where you’re already working.

Next time you have a call coming up or a cold message to write, try it. Two minutes, and you’ll walk in knowing more about your prospect than most people learn in a week.

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Now that you’ve seen how fast AI prospect research can get you a full, detailed brief on anyone, I’m curious: after seeing this, would you still spend thirty minutes Googling a prospect?

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Claude Code is the corner I’ve touched least. Most of you reading this are non-technical, like me. And since Cowork launched, you can get most knowledge work done there anyway (I’ve tested it).

But I know many of you are using Claude Code anyway, despite not being developers, just like me. And even though I’ve written about routines, subagents, building a website for your business with Claude Code, managed agents, automating a web scraper, and a full content-distribution agent that runs in the cloud each one assumes you’ve already got the basics set up.

So it's about time someone writes a proper starting guide for Claude Code.

And for this one, I asked my business partner, , to take it. He's been building with Claude Code far longer than I have, and he'll share the habits you need to start any Claude Code project like a pro. All in plain English.

One thing I’d ask: don’t just read this one. Build along with us, even if you’ve touched Claude Code before. The point isn't to finish a tutorial. It's to walk away with habits and a workflow you repeat every time you start a project.

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In this article:

• Why Claude Code changes how you build with AI

• Step 1: Set up your Claude Code project folder — Install Claude Code on Mac or Windows, connect your project to GitHub, and add the Superpowers plugin. The 30-minute setup you only do once.

• Step 2: Tell Claude what the project is (and make sure it never forgets) — How a CLAUDE.md file and a docs/ folder turn Claude from a slot machine that forgets everything into a teammate that remembers every decision you’ve ever made.

• Step 3: Plug Claude Code into real services like Supabase — Add a real database in 5 minutes, connect the Supabase plugin, and understand what MCP does (without reading a single piece of documentation).

• Step 4: Brainstorm your project before writing a line of code — The 5-minute habit that prevents 200 lines of code solving the wrong problem. Plus why Next.js and Tailwind are the safest stack for beginners in 2026.

• Step 5: Run your Claude Code app locally to see it work — What localhost and port 3000 mean, and the small moment your project stops being theoretical and starts being a real app in your browser.

• Step 6: Deploy your app to the internet with Vercel — The 3-minute one-time setup that connects Vercel to your GitHub repo, gives your app a live URL, and auto-deploys every change you push.

• Step 7: Use the brainstorm-plan loop for every change — The workflow rhythm I use for every new feature, bug fix, and refactor. And the single 2-minute step that protects you from Claude going rogue.

• Step 8: Train Claude to work the way you work — How Claude Code memory files stop you from re-explaining your preferences every single session, and why memory is different from project documentation.

• 5 Claude Code mistakes that cost me time (and how to avoid them)

• Where to go from here

I’ll let Matei take it from here.

Why Claude Code changes how you build with AI

We run an ecosystem of about 15 projects. Our central authentication platform, Amplifiers (our MCP server that turns anyone who installs it into an AI-demi-god), a marketing site, a lab where we share dozens of workflows and resources, plus a handful of mobile and web apps. All of them are touched by Claude Code most days.

A year and a half ago, that sentence would’ve been unthinkable.

Back then, “working with AI” meant opening a chat window, pasting some code, getting something slick-looking back, and then spending three hours figuring out which parts had invented themselves.

The AI was a slot machine: most pulls were close-but-wrong, and the wins didn’t add up. You couldn’t build on them.

What we have now is different. Claude knows the shape of every project, remembers decisions we made last week, and pushes back when we try to skip planning.

That shift didn’t come from a trick we found in a thread somewhere. It came from building a workflow, one habit at a time, mostly by paying attention to the evenings we lost to bugs Claude could have prevented if it had known one more fact about the codebase.

This article is that workflow. If you’ve never used GitHub, never opened a terminal, and have only ever talked to AI through a chat window, you’re exactly who I’m writing for. Every piece of jargon gets explained the first time it shows up. Every section has a “Try this” panel with the exact commands to run.

To keep things concrete, we’re going to build a made-up project together.

Meet MealMap: a small web app where you save recipes you cooked, tag what worked and what didn’t, and over time it learns enough about your taste to suggest a weekly meal plan. We won’t write a line of MealMap’s code.

We’ll walk through how Claude would help you build it, and the habits you can reuse across any project you build with Claude Code.

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Step 1: Set up your Claude Code project folder

For Claude to be useful, it needs a place to live. That place is a folder on your computer.

This sounds basic. But without a project folder, Claude Code doesn't know what it's working on. It has no files to read, no context to build on, nothing to remember between sessions.

Claude Code, Anthropic's command-line version of Claude, expects a project folder. It reads everything in that folder and edits files in place.

What you need before you start

Three things have to happen before Claude can be useful inside that folder:

1. Claude Code installed. A one-time setup.

2. A terminal you can type commands into. On Mac, it’s the app called Terminal (already on your machine: search Spotlight). On Windows, you already have PowerShell (search for it in the Start menu).

3. The folder turned into a “git repository” — a fancy way of saying “Claude can keep track of every change it makes, and you can undo any of them.”

One thing worth knowing before you open a terminal for the first time: it works exactly like a chat window. You type something, you press Enter to send it, and you wait for a response. Nothing happens until you press Enter, so don’t worry about accidentally running something by just typing it.

How to install Claude Code on Mac

Try this:

1. Open Terminal (Spotlight search → “Terminal”).

2. Paste this and press Enter: /bin/bash -c “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)”

3. That installs Homebrew, the tool we use to install other tools. Wait ~2 minutes.

4. Paste: brew install node — this gives you Node.js, which Claude Code needs.

5. Paste: npm install -g @anthropic-ai/claude-code — this installs Claude.

6. Paste: claude --version — if you see a number, you’re done.

How to install Claude Code on Windows

On Windows, install Node.js from nodejs.org, then run the same npm install -g @anthropic-ai/claude-code step in PowerShell.

Create your project folder and turn it into a git repository

Now we need a folder. Make one called mealmap — anywhere convenient, but Desktop is fine.

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So if you now look at where we are and compare it to those early days, it's wild how fast this moved.

I've been tracking it for a while, writing about each iteration as it came out, from Nano Banana to Nano Banana Pro to Nano Banana 2 with 58 infographic prompts I built for myself.

And since we launched Amplifiers, image generation has been built in from day one, so you can generate images right inside Claude, first with Nano Banana 2, and more recently with GPT Images 2 too.

Now that both models are available in the same place, I’ve been wanting to put them side by side on the same prompts.

Not the way I usually switch between them, where one model disappoints me and I try the other. A proper experiment. Multiple use cases, same prompts, same conditions, just to see where each one shines and where it falls short.

And today I finally sat down and did it.

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In this article

For each test, you’ll see the exact prompt I used, both outputs side by side, and my verdict:

• Test 1: AI character consistency from a single selfie

• Test 2: Turning a pet phone photo into a Vogue-style editorial

• Test 3: Recipe blog post to cookbook-style visual guide

• Test 4: Premium brand campaign board from a product photo

• Test 5: Realistic AI newspaper mockup with your own branding

• Test 6: SaaS landing page mockup from an article URL

• Test 7: Educational Instagram carousel from a blog post

• Test 8: Full brand identity system from a single logo

• Test 9: Physics atlas page with diagrams and formulas

• Test 10: Technical infographic poster of a CPU processor

• So which image generation model is better?

Let’s begin.

Test 1: Headshot collage with character consistency

Prompt:

Use amplifiers to create a high-end portrait collage using a headshot I will upload  as the identity reference. Maintain strong character consistency across all variations. The person must remain clearly recognizable in every frame with the same facial structure, skin tone, eye shape, hairstyle, and overall identity.
Generate a cinematic multi-panel collage featuring the same person wearing different professional and modern headsets across multiple scenes and moods.
Include a mix of: sleek corporate LinkedIn-style portraits, cinematic studio lighting, podcast/interview setup, Netflix character poster, dark moody cyberpunk lighting, Wired magazine portrait photography.

Nano Banana 2:

GPT Images 2:

Verdict: The difference here is night and day. The images Nano Banana generated from my reference photo don’t look like me. Not even close. GPT Images 2, on the other hand, kept my face, my hair, my eyes, even the little mole next to my eyebrow. It also preserved my head position from the selfie I uploaded. The clarity, the fidelity, and how natural it all looks is on a completely different level. For character consistency from a reference image, GPT wins this one by a mile.

Test 2: Turning a pet phone photo into a Vogue-style editorial

Prompt:

Use amplifiers to transform the photo I upload of my cat into a high-fashion editorial photoshoot for a luxury pet fashion magazine. Keep the transparent plastic wrapping as avant-garde styling. Use cinematic studio lighting, shallow depth of field, ultra-realistic fur texture, elegant composition, and Vogue-style fashion photography.

Nano Banana 2:

GPT Images 2:

Verdict: GPT Images 2 has noticeably higher saturation, which sometimes makes it feel over-processed. The plastic wrapping looks more convincing, the fur texture feels more realistic, and Toby just looks better in the GPT version. Nano Banana’s output looks over-smoothed in comparison. They’re closer on this one than on the headshot test, but GPT still edges it out on realism.

Test 3: Recipe blog post to cookbook-style visual guide

Prompt:

Use Amplifiers to turn this recipe: https://iamafoodblog.com/fluffy-japanese-pancakes-souffle-pancake-recipe/ into a premium cookbook-style step-by-step visual guide for fluffy Japanese pancakes. Include elegant food photography, ingredient callouts, numbered cooking steps, clean editorial layout, soft natural lighting, and realistic pancake textures throughout.

Nano Banana 2:

GPT Images 2:

Verdict: Both versions are good on this one. But I still prefer GPT’s. The text placement in the ingredients and step-by-step images feels more natural for a cookbook. In the final pancake image, the maple syrup and butter placement looks more realistic in GPT’s version. In Nano Banana’s, the syrup container sits on the table at the same size as the matcha mug, which looks off.

Test 4: Premium brand campaign board from a product photo

Prompt:

Use amplifiers to create a hyper-realistic, ultra-detailed brand campaign board for Hairburst gummy vitamins (I'll upload a photo) designed like a premium creative agency presentation for a modern wellness brand. The image should feel like a luxury campaign showcase or editorial moodboard, combining multiple visual sections into one cohesive composition.
Use a clean editorial grid layout with a mix of hero product photography, close-up gummy texture shots, lifestyle wellness imagery, packaging mockups, ingredient highlights, infographic-style benefit callouts, typography sections, and social-media-inspired product visuals. The composition should feel curated, balanced, and visually rich, similar to a high-end Behance branding project or launch campaign presentation.
Center the visual identity around glossy pink Hairburst gummies, premium supplement bottles, healthy hair and wellness aesthetics, and ingredient storytelling focused on biotin, selenium, and zinc. Include elegant supplement facts, benefit snippets, and modern wellness branding integrated naturally into the layout.
Use a warm, premium color palette with soft pinks, creamy whites, pastel peach tones, light turquoise accents, and subtle beige neutrals. The lighting should feel soft and natural, with realistic shadows, reflections, and tactile textures throughout.
Add microdetails like sugar-coated gummies, spilled vitamins, condensation on bottles, realistic packaging textures, soft reflections, subtle ingredient illustrations, and polished infographic-style design elements. Avoid sterile pharmaceutical aesthetics, childish candy visuals, dark dramatic lighting, or cluttered branding.
The final image should feel modern, premium, aspirational, and highly shareable, like a luxury wellness campaign created by a top-tier creative agency. Ultra-detailed, 4K.

Nano Banana 2:

GPT Images 2:

Verdict: I can’t believe these two visuals came from the same prompt. The GPT version looks like something a creative agency would present to a client. The fonts, the colors, the editorial grid layout, the product photography, it all comes together as a premium, aspirational wellness campaign (as per the prompt instructions). Nano Banana’s version isn’t bad. But when the prompt asks for luxury and premium, GPT delivers that feeling in a way that Nano Banana doesn’t come close to matching here.

Test 5: Realistic AI newspaper mockup with your own branding

Prompt:

Use Amplifiers to create a hyper-realistic mockup of a printed AI newsletter or newspaper called “AI News” with the tagline “Your Daily Piece of AI News.” Design it like a modern editorial tech newspaper focused on practical AI blending the visual style of Monocle, business newspapers, and creator economy media.
The newspaper should feature multiple AI news stories, practical tutorials, sidebars, charts, mini headlines, and editorial sections arranged in a realistic printed newspaper layout with folds, paper texture, ink imperfections, and natural lighting.
The main featured story should prominently showcase:
“Master Claude Like the Top 1% of AI Users” with AI BLEW MY MIND naturally integrated as the featured practical AI newsletter. Include believable editorial copy suggesting that readers use AI blew my mind to learn advanced Claude workflows, save hours at work, improve productivity, and move their business forward through practical AI tutorials and step-by-step guides.

Nano Banana 2:

GPT Images 2:

Verdict: This is the closest matchup so far. Both models produced solid newspaper mockups with realistic layouts, paper texture, and believable editorial structure. I’d call this one a tie.

Test 6: SaaS landing page mockup from an article URL

Prompt:

Analyze my article: https://aiblewmymind.substack.com/p/claude-image-generation and then use the Image Generation Amplifier to create a hyper-realistic flat 2D landing page design for it, showcasing how Claude can now generate stunning visuals using Nano Banana and GPT Images with Amplifiers.
The result should look like a real modern SaaS website screenshot, not a 3D mockup or Behance presentation. Use a clean front-facing layout with realistic web design sections, modern typography, polished UI components, beautiful spacing, subtle gradients, and professional SaaS aesthetics.
Present it as a polished vertical landing page showcase with multiple scrolling sections visible together, similar to a premium Behance or Dribbble case study.
Show examples of AI-generated visuals inspired by the article, including infographics, brand systems, social media graphics, product mockups, technical posters, and landing page concepts generated directly inside Claude.
The overall vibe should feel futuristic but approachable, built for creators, marketers, founders, and non-designers who want professional visuals from AI without switching tools.
Core positioning: “Turn Claude into your creative studio.” / “Generate world-class visuals directly inside your chat.” 

Nano Banana 2:

GPT Images 2:

Verdict: GPT did a better job here. The result feels more realistic and looks more like an actual landing page. Nano Banana’s looks more like a collection of drawn elements arranged together than a real web page. Neither is perfect, but GPT’s version is stronger. With some iteration, GPT’s output could get close to something you’d pass to a coding agent to start implementing.

Test 7: Educational Instagram carousel from a blog post

Prompt:

Analyze this article I wrote: https://aiblewmymind.substack.com/p/how-to-build-voice-ai-agent-elevenagents and use the Image Generation Amplifier to create an educational Instagram carousel. 
The carousel should explain how easy it is to create a voice AI agent with ElevenAgents, step by step, based on the article. Make it practical, clear, beginner-friendly for non-technical people, and outcome oriented for business owners and execs.
Include my branding naturally somewhere on each slide, such as a small AI BLEW MY MIND logo or “aiblewmymind.substack.com” in a corner. Make it feel like an educational carousel that teaches people how to build the voice AI agent using Amplifiers.
Format it as a polished Instagram carousel with multiple 4:5 slides, consistent layout, readable typography, and a final CTA slide encouraging people to read AI BLEW MY MIND for practical AI workflows.

Nano Banana 2:

GPT Images 2:

Verdict: Neither is perfect. Both models placed the footer in different styles across slides when it should stay the same. Nano Banana even printed a raw color hex code on one slide, and GPT shifted my accent color from purple to blue. The step numbers are also inconsistent in size across slides for both models, which breaks the visual rhythm of a carousel. This is exactly where a dedicated carousel skill would help, since my current brand skill is built for documents, not Instagram slides.

Test 8: Full brand identity system from a single logo

Prompt:

Use the Image Generation Amplifier to create a complete visual identity system for AI BLEW MY MIND, presented as a single large-scale brand board composition based on the logo I’ll upload. The final result should feel like a professional creative direction board that combines branding, editorial design, UI inspiration, social media direction, typography, color systems, and visual storytelling within one cohesive image.
The composition should feature multiple visual sections arranged in a clean editorial grid layout, including logo variations, typography systems, color palettes, Instagram carousel examples, newsletter covers, landing page concepts, graphic motifs, UI cards, content thumbnails, illustration direction, photography treatments, visual patterns, icon systems, social media mockups, AI workflow graphics, editorial layouts, and brand application examples.
Include a dedicated section showing the Image Generation Amplifier in action, featuring realistic before-and-after AI image examples, prompt workflow UI, and generated visuals created through the amplifier. Make it feel like a real product workflow integrated into the AI BLEW MY MIND ecosystem.
The overall aesthetic should feel modern, polished, editorial, and highly curated, similar to a premium Behance branding case study or high-end creative agency presentation.

Nano Banana 2:

GPT Images 2:

Verdict: Nano Banana went for a more editorial layout with distinct grid sections, while GPT produced a cleaner, more structured grid with better text legibility and more realistic UI mockup elements. Nano Banana looks more like a mood board, more artistic. GPT looks more like a Behance case study, more production-ready. Neither is what I'd go with though, because even though my logo is dark, I don't want the overall brand colors to skew that dark. Some of GPT's images lean too dark for my taste, but it was working from the logo and trying to figure out the visual direction from that.

Test 9: Physics atlas page with diagrams and formulas

Prompt:

Use the image generation amplifier to create a detailed physics atlas page explaining how gravity works, with scientifically accurate diagrams, formulas, annotations, force vectors, planetary orbits, spacetime curvature, mass interactions, and Einstein relativity concepts. Include labeled visual explanations, mathematical equations, cross-sections, field diagrams, and realistic textbook-style scientific illustrations. Use clean academic typography, organized educational layouts, subtle paper texture, and highly detailed information design that looks like a real modern physics encyclopedia or advanced university textbook page.

Nano Banana 2:

GPT Images 2:

Verdict: Completely different styles and both are good in their own way. Nano Banana’s looks like a page from a school textbook: clean, more hand-drawn, with a few core elements explained well. GPT’s looks like a page from an actual atlas: dense, packed with information, more visual variety, and more elements on the page. Two patterns keep showing up across these comparisons: Nano Banana tends to keep things minimal while GPT fills the frame with more content, and GPT consistently produces more realistic-looking outputs while Nano Banana leans more illustrative.

Test 10: Technical infographic poster of a CPU processor

Prompt:

Create a highly detailed technical infographic poster of a futuristic CPU processor, designed like a premium engineering blueprint and semiconductor architecture sheet. Show the CPU as the central hero object with ultra-realistic metallic materials, microscopic circuitry, layered chip structures, cooling systems, thermal maps, transistor layouts, motherboard integration diagrams, nanometer process annotations, exploded internal components, and glowing data flow visualizations. Surround the processor with technical callouts, engineering measurements, scientific labels, performance graphs, manufacturing details, and advanced computing schematics. Use a dark premium background, cinematic lighting, clean scientific typography, subtle grid systems, realistic reflections, and high-end industrial design aesthetics. The composition should feel like a mix of an Intel engineering manual, NASA technical documentation, and a collectible futuristic technology poster. Ultra-detailed, photorealistic, 8K.

Nano Banana 2:

GPT Images 2:

Verdict: Both are strong and this is probably the hardest one to call. They took very different creative directions from the same prompt. GPT’s looks more photorealistic and chose to use what looks like an Intel Core processor as its reference point. Nano Banana’s feels more like an illustrated blueprint. I don’t have a clear winner here. They’d serve different purposes, and both would work well as a poster or visual asset.

So which image generation model is better?

I was really curious to run this comparison because I wanted to see the patterns side by side.

And the patterns are clear. GPT Images 2 wins most rounds. It produces more realistic outputs, packs more elements into the scene, handles editorial layouts more professionally, and respects prompt intent more faithfully. When I asked for an atlas, it gave me an atlas. When I asked for a luxury brand campaign, it delivered something that looks like it came from a creative agency. Its character consistency from reference photos is on another level entirely.

Nano Banana 2 is still good, and there are situations where its cleaner, more minimal style is what you need. It’s worth knowing the differences so you can pick the right model for what you’re creating.

I mostly generate images in Claude these days using both models. Since I have both API keys set up, I switch between them depending on the result I’m going for.

You can do the same. Here's how to set up Amplifiers so you can use both Gemini and ChatGPT together inside Claude.

What do you think of the differences? Which model do you use right now?

Leave a comment

And if you found this useful, share it with someone who’s been wondering which image generation model to use.

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This post is free. Paid subscribers get access to all premium prompts and tools inside Amplifiers (the AI blew my mind MCP), weekly premium articles, all premium resources inside the AI blew my mind Lab, and exclusive partner discounts. Upgrade here.

And I get the confusion. If you’ve been using Claude lately, you’ve probably wondered the same thing yourself.

So you go search Claude Code vs Cowork online, and you get the same answer everywhere (I really tried to find a more nuanced take on both Google and YouTube): Claude Code is for developers, Cowork is for non-technical people.

Except it’s not that simple anymore. You can use Cowork to build websites and code entire projects. You can use Claude Code to write client proposals, organize your invoices, plan your week. Work that has nothing to do with code.

The line between them is blurring fast.

And then there are the two camps. The more technical people will tell you Claude Code is the tool worth using. The non-technical people who just want to get their work done, think it's too technical for them before they even try.

Meanwhile you’re reading newsletters or scrolling X, and it feels like every productive person has already moved to Claude Code and you’re still the one who never opened a terminal. The FOMO kicks in. The doubt follows.

I get both sides. If you’ve never worked with code before, something called Claude Code already sounds like it’s not for you. If you’re already technical, you know Cowork is the little sibling of Code, and there’s nothing you can do in Cowork that you couldn’t do in Code.

But for the kind of non-technical knowledge work most of us do, is it really necessary to open Claude Code? Or can you get the same job done in Cowork?

You’re about to find out. First we’ll look at the obvious differences between the two tools, what we already know about how they’re built and what they can do.

Then the more interesting part: I ran the same prompts in both tools on three different tasks so we can compare the outputs properly, in speed and in quality.

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What’s in this guide

1. What Claude Code and Cowork are

2. Who they were built for

3. Key similarities and differences

4. Where the “developer vs non-technical” distinction falls apart

5. Claude Code vs Cowork: testing both on 3 real tasks
   • Task 1: Clean messy data and build a salary dashboard
   • Task 2: Turn a spreadsheet into an executive slide deck
   • Task 3: Run a competitor positioning analysis as a PDF

6. What the tests showed

7. So do you actually need Claude Code?

8. A simple decision framework for when to reach for each tool

What Claude Code and Claude Cowork are

Before we run anything in both tools to properly compare how each one handles the same tasks, let’s go through what we already know about them.

The kind of comparison you can make before you even touch them.

Claude Code

Claude Code is Claude as an agent that runs directly on your computer. Most people use it through the terminal, but you can also run it inside your desktop app, VS Code, and a few other places. Wherever you launch it, the experience is the same. You type your request in plain text, and Claude reads files, writes files, runs commands, and can take over full workflows on your machine.

Claude Cowork

Claude Cowork is the same agentic engine, but inside your Claude desktop app. You open a window, give Claude a goal, and watch it plan the steps, work through your files, use your connected apps, and hand back finished work. Documents, spreadsheets, dashboards, organized folders, drafts of all kinds. It runs in a sandboxed environment, which means a closed-off space where Claude can only touch what you explicitly approve.

Who Claude Code and Cowork were built for

Cowork came out of what Anthropic saw after launching Claude Code. The tool was built for developers, but two things started happening:

1. Non-technical people were trying to use it for non-coding work.

2. And coders themselves were using it for tasks that had nothing to do with code.

So Anthropic built a lower entry point. A version that runs in the desktop app, with a friendlier interface, sandboxed permissions, and a setup process that doesn’t require you to know what a config file is. That’s Cowork.

At the surface, the original answer still holds. Claude Code is for people who already work in a terminal and do technical work. Cowork is for everyone else.

What the plugins say about who they're for

Plugins are bundles of tools, skills, and integrations you can install in one click to extend what each product can do.

They tell you a lot about who each tool is being built for, because the community and Anthropic build plugins for the people they expect to be using the tool.

When you open the plugin directory and filter by tool, the difference is hard to miss.

• Plugins for Claude Code are developer-focused: GitHub, Playwright, Linear, language servers for TypeScript, Python, Rust, Swift, Java, Kotlin. The top installs are tools like Frontend Design (564,000+ installs), Superpowers (476,000+), and Context7 for live documentation lookup. These are tools for shipping software.

• Plugins for Cowork are different in kind. They cover sales, marketing, finance, legal, HR, operations, customer support, product management, design, and data analysis. Knowledge work. The kind of stuff most non-technical people do every day, packaged as ready-to-go workflows you can install in one click.

The plugin ecosystem reflects the design intent. Each tool is being built up around the user it was made for.

Claude Code vs Cowork: key similarities and differences

Now let’s look at the similarities and differences that matter most in practice.

Where Claude Code and Cowork are the same

• Same Claude models, same agentic core. Same ability to plan, take actions, use tools, edit files, run code, and finish work without you babysitting every step. The intelligence underneath is identical.

• Skills work in both. The reusable instructions you write once and Claude pulls in whenever the task calls for them.

• Plugins work in both. The directories are filling out at different speeds, and some plugins work in both products while others are specific to one or the other.

• Both connect to your own apps. Gmail, Drive, Notion, Calendar, Slack, and more, through connectors and MCP servers.

• Both can do research, writing, building, and automation. Anything Cowork can do, Code can do. Most things Code can do, Cowork can also do.

• Both run on a schedule. Cowork has Scheduled Tasks, which run when your computer is on and the desktop app is open. Claude Code has more options: /loop for in-session work, /schedule for persistent local tasks, and Routines that run on Anthropic’s infrastructure even when your laptop is off. Set up a workflow once, tell it when to run, and the work happens without you starting it.

  This is exactly what I used to build a content-distribution agent that runs while I sleep — the clearest case for reaching for Claude Code over Cowork.

• You can control both from your phone. Claude Code has Remote Control for picking up a local session from the mobile app. Cowork tasks show up there too via Dispatch. Kick something off at your desk, check in from the couch.

Where Claude Code and Cowork differ

The differences are everywhere except the engine.

• Where you run them. Cowork only runs inside your Claude desktop app. Claude Code runs in the desktop app too, but also in the terminal, in VS Code, and a few other places.

• How visible the work is. In Cowork, you watch Claude plan the steps, see the connectors and skills it’s using, and see files appear as they’re created. In Claude Code, the work scrolls past as text in the terminal, and the files it creates land in local folders you have to open yourself. For non-technical eyes, Cowork is easier to follow.

• How much you need to know to get started. Cowork is open-the-app-and-go. Claude Code asks you to be comfortable with a terminal, plus it asks you to approve permissions, commands, and file changes as it works, even when you might not know exactly what each one means.

• How you find and install your tools. In Cowork, plugins, skills, and connectors all live in one “Customize” panel in the left sidebar. Browse, click install, done. In Claude Code, you install plugins with commands typed into the terminal, and skills are markdown files you place in folders on your computer.

• Resource limits. Cowork runs in a sandboxed environment that shares your machine’s resources but caps how much you can throw at it in one go. Claude Code runs directly on your machine with no sandbox, so it has more headroom for heavy data processing or large file batches.

• Cowork has features Code doesn’t (yet). Dispatch lets you hand Claude tasks that use everything on your desktop. Live artifacts render previews of HTML, React, and code as Claude builds them. Projects keep your files, instructions, and context organized in one place.

• Code is more powerful for technical work. Code has direct access to your computer, so it can install software, push to GitHub, deploy a website, or do anything you’d normally do in a terminal. It can also be triggered automatically by other tools (a script, a calendar event, a code commit), and it can split a big task into smaller pieces and run them in parallel.

Where Claude Code and Cowork blur for non-technical work

If you’ve read this far, it might seem like Claude Code is just too technical to bother with. And looking at the comparison so far, it really does look like Code is for developers and Cowork is for the rest of people.

But the barrier between you and Claude Code isn’t really the capability. It’s familiarity and a bit of learning curve.

The two tools share a lot underneath and you talk with both of them through natural language, just like you’re used to. So the real question is whether it's worth learning a new tool when the one you already use does most of what you need.

And the only way to find out is to put them to the test on the same tasks, with the same prompts, and see what happens.

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Claude Code vs Cowork: testing both on 3 real tasks

For these experiments, I ran Claude Code in the terminal and Cowork in the desktop app side by side, with a timer on my laptop and one on my phone, so I could track how long each tool took. After running each task, I did my own analysis and comparison, and then used Claude Code to compare the outputs from a few other angles.

I ran them on three tasks that cover the kind of work most of us do with AI: cleaning messy data, building a dashboard, creating a PDF, putting together a slide deck, and doing research.

The question I’m trying to answer is the one you already know: do you really need Claude Code for non-technical work or does Cowork cover you?

When I started this, I was just as curious as you are. What I found surprised me. So let me show you.

Task 1: Clean messy data and build a salary dashboard

Read more

And whether you’re auditing your own site or keeping tabs on what’s changing somewhere else, the need is the same: fresh data from websites, on a schedule, without doing it by hand. That's web scraping.

Claude Code can help with that, whether it’s tracking Amazon listings, inventorying a WordPress blog, or monitoring competitor pages, as long as the site structure stays relatively stable.

It’s not the kind of AI use case that makes you stop and stare. But it’s the kind that saves real time in your work week by taking the tedious, repetitive stuff off your hands.

In this article, you’ll see how to set that up with Claude Code: a Playwright script that scrapes Amazon daily, email alerts when your data changes, and the whole thing running automatically with GitHub Actions.

The example is built for Amazon, which has a stable page structure that makes it a good fit. But you can adapt the same approach for your own use case, as long as the site you’re tracking doesn’t change its layout too often.

One of my readers, , recently asked about using AI to generate a full content inventory for a WordPress site. Post counts, age breakdowns, a bird’s eye view of what’s in the archives. Same web scraping need, different situation.

So Marylee, if you’re reading this, drop the link to this article into Claude and ask it to help you build the same thing for your WordPress site.

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Before we get into it, let me introduce who’s walking you through this one.

Expertise, reimagined with AI — featuring

For this one, I wanted someone who isn’t writing about this from a distance. Someone who built it, runs it daily, and hit the walls you’d hit if you tried it yourself.

I came across when he joined the AI Blew My Mind community chat. He’s a former B2B SaaS product manager who left tech to buy and run e-commerce brands on Amazon. He now manages 11 brands generating seven figures in annual revenue, all as a one-man operation.

Once I started reading his articles at The Automated Operator, I loved seeing how far he’s taking things with AI in his business. So I asked him to share something practical for you, something you could take and implement, since I know many of you are running e-commerce businesses or working in retail.

What you’ll learn in this article

Alex walks you through building a daily web scraper for Amazon with Claude Code.

• Why he scrapes Amazon daily (and what he tracks)

• Claude in Chrome vs. Playwright: which to use for web scraping

• How to build the web scraper with Claude Code, step by step

• Limitations and pitfalls of web scraping with Claude Code

Now, over to Alex.

The Automated Operator

In which I document my attempts to use AI to fully replace myself in running my business

By Alex Willen

How to Build a Daily Amazon Web Scraper with Claude Code

Public sentiment around AI capabilities seems to always follow the same pattern. A lab launches a very early version of some feature, and people try it and complain loudly about flaws and a lack of reliability. The labs keep chugging away, putting out improvement after improvement, and then at some point the flaws get solved, the feature becomes extremely reliable, and nobody talks about it because they’ve all moved on to complaining about the next new feature.

First image generation had all kinds of weird artifacts that made it unusable for anything serious, then it couldn’t do hands, and now it’s basically perfect. Video was low-quality with physics that didn’t make sense, and then some time passed and news outlets were reporting on the near-perfect AI fight scene between Brad Pitt and Tom Cruise. And we all know how it’s gone with coding — SWEs mocking vibe coders and the slop produced by LLMs while models steadily improved with every release, and then all of a sudden Karpathy posts in January that agents are writing 80% of his code and in February that they’re completing entire projects for him.

There’s one place where agent capabilities have recently crossed the threshold into being useful and reliable, and yet I hear almost no one talk about it — web browsing. I’ve been playing with web use since OpenAI launched it as a preview in ChatGPT, and it was unusably bad until suddenly, when Claude launched Opus 4.5, it just worked. Most folks were too focused on that model’s agentic coding capabilities to notice, but all of a sudden it was passing all of my tests with flying colors. Then it got cheaper with Sonnet 4.5 and better with the 4.6 models.

So if you’ve been held back from implementing some AI workflow because it involved using the web, now’s the time to give it a try!

I’ll walk you through a simple web scraping task that I’ve got running daily and how you can set up something similar, plus I’ll discuss some of the limitations that remain.

Why I scrape Amazon daily (and what I track)

I own a dozen brands that sell products on Amazon, and AI in its various forms is critical to my ability to keep up with all of them without having to be working constantly. Mostly I have Claude Code using Amazon APIs, but there’s some information that Amazon doesn’t expose that way.

There are a few things in this category that I’d like to track on a daily basis for each of my products:

1. Has the number of ratings changed? If so, has the average rating changed?

2. Have any badges appeared or disappeared? Amazon sometimes puts badges on product pages, and they can be positive (e.g. Amazon’s Choice) or negative (e.g. Frequently Returned).

3. Have any variants suddenly been separated from the listing? Sometimes, for no particular reason, Amazon will remove a product variant (e.g. some particular color of a product) from the main product page and put it all on its own. Amazon does not tell you when it does this, and frequently it’ll still show up in your seller dashboard as though it has not been separated. The only way to tell for sure is to go to the product page and check all variants.

So, let’s get Claude on it! But first, there are a couple of approaches we can take here that are worth understanding before building out the flow.

Claude in Chrome vs. Playwright: which to use for web scraping

What Claude in Chrome does

In December of last year, Anthropic launched Claude in Chrome in open beta. It’s a Chrome extension that lets Claude take control of your browser to do web-based tasks. It can be used as a standalone tool, in which case you’ll get a familiar chat interface in the right sidebar in which you can ask it to do things.

Alternatively, Claude Code can launch Claude in Chrome. This is particularly useful for when Claude Code is building a web application and needs to test it by having a user click around and try things out.

I definitely recommend trying it out — it’s a great way to get a handle on the current state of browser agents. You can download the extension here.

What Playwright does

The other way Claude can access the web is by using Playwright, which is a browser automation framework that lets you programmatically control web browsers. It understands what’s on a page based on the underlying code (different than Claude in Chrome, which takes screenshots to “see” what’s on a page), and then Claude writes code to tell the browser that it wants to do something like click on a button.

Which one to pick for repetitive scraping

So, which do we want to use here? The honest answer is that in the overwhelming majority of cases, Playwright’s the better option. It’s much faster and meaningfully more reliable. Claude in Chrome has the advantage of being able to insert some intelligence in your workflow (e.g. interpreting information on the page and then deciding whether to take an action based on fuzzy criteria), but if you’re just doing repetitive web browsing tasks that don’t require mid-stream decision-making, then you don’t need that intelligence.

Playwright MCP vs. writing Playwright scripts directly

This leads to our second decision: whether to use the Playwright MCP or just have Claude write Playwright code directly. Generally, the MCP is better for ad hoc tasks or explorations that Claude’s performing. It gives Claude Code tools to do tasks like navigate to a page or click on an element without having to write the full code for each action each time.

For repetitive tasks, though, you’re going to want to have Claude write a Playwright script. For one, it’ll execute faster. Beyond that, it’ll also be more robust and consistent, since it doesn’t require Claude to actively make any decisions along the way. That means you can set it to run on GitHub Actions or something similar, without having to use Claude at all once the script is written.

How to build the web scraper with Claude Code, step by step

Install Playwright and the Playwright MCP

First things first: install Playwright and Playwright Chromium, plus the Playwright MCP. The former two are the library Claude needs to use Playwright and the browser it’ll use to execute Playwright code. I mentioned above that we don’t want to use the MCP for daily automation, but you should install it anyway, because it’ll make it easier for Claude to browse around the page at first and figure out where things are before writing the script. I would give the commands to install those, but let’s be real, you should just ask Claude to do it.

Decide what to scrape, store, and notify

With that done, let’s get a simple version of my scraper set up. We want it to visit a pre-set list of Amazon URLs, find the number of ratings and the average and save them, then compare them to yesterday’s numbers and alert me if they have changed.

To do this, we need a few things:

• The URLs to scrape

• A place to store the info we gather

• A mechanism to notify me of changes

• A way to run the job daily

We have the URLs, so no problem there. In terms of where to store things, you’ll want to consider both how much data you’re collecting and whether it matters for long-term analytics.

For example, I have a separate job that pulls a lot of numbers from the Amazon Ads API daily. I’m using that to build a historical record of ad performance by day, and it’s quite a bit of data. For those reasons, I store everything in a SQLite database that’s online and also synced to my local machine. That’s optimal for being able to query the numbers, plus it’s backed up lest either my hard drive or the cloud database die somehow.

This case is different; I’m gathering very little information and have no need to run any kind of complex queries of the data. I don’t even really care about the historical record, just the daily update. Given that, just about anything works, from a Google Sheet to a local file. I’m a big believer in not overcomplicating things, so let’s just do the simplest possible thing and use a .txt file.

Test the first scrape and save the data

At this point, we’ve got enough to test the initial part of the flow — scraping the data and saving it. I learned from my many years as a product manager that it’s best to try individual components of a workflow to make sure they each work independently before piecing them all together, so let’s ask Claude to give this a whirl.

Prompt:

We’re going to build a workflow that checks public Amazon product pages each day to scrape the number of reviews and average review, then sends me a notification if either has changed since the previous day. Right now I want you to start with a simple test of the first part of the flow - scrape one URL, get both pieces of information and save them to a local txt file. You have the Playwright MCP installed, so you can use that to explore the page, but ultimately you’re going to need to write a Playwright script that can automatically run each day.

Note that I gave it the broader context before the specific task. Since this is a case where we’re going to be building on this throughout the conversation, it’s a good idea to let Claude know what we’re working towards. I also let it know that it has the Playwright MCP installed, because even though it’ll usually check for that without being prompted, it’s not 100% consistent there and might just start writing its own Playwright code instead.

Your Claude instance might ask you if there’s a particular Amazon page you want to use as your test case; mine knows the URLs of all the products I sell, so it just went ahead with one of them. You’ll see it pop up a new Chrome window, which is separate from any existing ones that you have open and doesn’t share any context like cookies, which is good from a security perspective (it can’t access sites you’re logged into), but has some downsides we’ll discuss later.

Mine took a couple of minutes then came back and confirmed success:

Perfect! Since I gave it full context on the flow, it’s offering to move onto the next step, but it’s always a good idea to ask what it’s going to do before proceeding plus add any more helpful context. Next prompt:

First explain how comparison and alerting will work. Also, I want to be alerted via email - how will you set that up?

Here’s part of the response:

In this case, it’s using historical context of previous alerting systems we’ve built that run via GitHub Actions on my machine and send alerts via Gmail. I recommend both of these for ease and simplicity, so let’s get those set up.

Set up Gmail alerts and GitHub secrets

First, you’ll need a GitHub account; I’m going to assume you have one, but if you don’t just ask Claude to help get that set up. Once that’s done, ask it to create a repo for the project.

From there, get the Gmail App Password by going to https://myaccount.google.com/apppasswords. Give it a name, click Create, and you’ll see a 16 character password. You have two options for how to upload that as a GitHub secret — you can either just give it to Claude and ask it to upload, which is not a great idea from a security perspective, but I’m not going to tell you how to live your life, or type:

gh secret set GMAIL_APP_PASSWORD --repo Awillen/REPO_NAME

Where GMAIL_APP_PASSWORD is the password and REPO_NAME is the name of the repo that Claude just created (you should also swap out Awillen for your username). It’ll need to store the email address you’re sending alerts from (the one you just got a password for) and the email address you want alerts sent to (these can be the same), but it’s fine to just give those to Claude to set.

Test the comparison and alert logic

Once that’s done, let Claude get to building! It will probably do some testing on its own, but if not, ask it to test the following:

• Add a line to the txt file that shows changes in the number of ratings and average rating, then trigger the comparison and alert workflow

• Add a line that shows no changes and trigger the workflow

If everything’s working correctly, the first case should trigger an email. In my case, I got this:

Functional but not exactly aesthetically pleasing… let’s ask Claude to spruce it up.

Much better!

The second case shouldn’t send anything (though Claude will confirm in the console that it did run).

Schedule the workflow to run daily

Now our last step is to automate it. Everything’s already configured in GitHub, so all we need to do is set it up to run daily. Just ask Claude, and make sure to specify that you want it to run locally on your machine (more on this below) — it’ll take care of it, and you’re all set! You’ll receive an email daily any time there are changes to the Amazon reviews of the product you’ve specified.

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Limitations and pitfalls of web scraping with Claude Code

That was easy (I hope) but there are a few ways this sort of task can get a bit more complicated:

Access Challenges: IP blocking and authentication

For this project in particular, you must use a GitHub Actions Runner on your local machine (as opposed one hosted by GitHub), because Amazon is notorious for blocking attempts to scrape its pages and recognizes GitHub’s IP addresses. Amazon’s not the only company that does this, so if you’re planning to use a hosted service for a job like this, make sure you test it first.

Another issue you may run into is the need to authenticate into a user account in order to access non-public pages. For actual implementation of this demo, when there are new ratings on my product, I’d like to check if there are new reviews (most people just give products a star rating but don’t actually write a review). Unfortunately, you can only access the full list of reviews if you’re logged in.

If you run into a similar case, you can give Claude your account credentials to log in (ideally securely, as secrets in GitHub or similar) or create a user account just for it. Here, I’m just too worried about Amazon associating any sort of bot activity with my own account or another account that’s logging in from my IP, since that sort of thing will get automated systems to flag your account, which would be a nightmare since that would likely impact my Amazon Seller accounts. Just not worth it, I’m afraid.

I’ll also just flag here that using a Google app password is convenient but not the most secure option. Be thoughtful about where to trade off convenience against security.

Playwright script fragility (and CAPTCHA handling)

At the last job I worked at as a PM, we used Playwright to access some of our customers’ software when there was no viable alternative like an API. This mostly worked fine and gave us a big advantage over competitors who would just shrug and tell prospects that if there’s no API, integration is impossible.

I can’t say whether or not they knew that Playwright was an option (this was mortgage-related software, so most companies in the industry did not employ the greatest engineers) or whether they just didn’t want to use it because they knew of its major weakness — it can be fragile.

The problem with running a pre-written Playwright script every day is that if the page you’re scraping changes, there is a very solid chance it will break your script. That’s obviously a problem if you want to use it for anything critical, so consider how likely it is that a page is going to change.

One thing I didn’t mention above is that while building this, Claude ran into an Amazon CAPTCHA while trying to access a page. This was a very simple one (it just had to click a button to continue), so it was easy to add logic to the script to handle that if it arose. If it had thrown up something more complex, though, a script wouldn’t have been able to handle it.

The good news is that if you expect to encounter a more dynamic website or one with potential roadblocks, you can take the alternate approach of having Claude Code do the scraping with a scheduled job. If you go that route, it can use the Playwright MCP to navigate the page and figure out how to get what you need, even if it’s not in the place you expect. For simple web scraping cases, that’s overkill (and if you’re scraping a lot of pages it’ll burn tokens very quickly), but it’s worth keeping in mind for complex or critical workflows.

Monitoring and alerting when your scraper breaks

Since I only care if the number of ratings for a product has changed, I set up the flow so no email gets sent if everything is the same as it was the day before. The downside is that if I don’t get an email, there’s no way for me to distinguish whether that’s because there were no changes or because the whole workflow failed.

That’s not ideal, because once you’ve got enough daily monitoring tasks running, some of them will fail eventually, and you’re going to want to know that. So, some options:

• Have it send you an email even if there are no changes. That way you know the absence of an email is a problem. Speaking from experience, though, if you’re busy you might not notice that you missed your daily email right away.

• Use GitHub Actions notifications. These are on by default and will email you if something fails, so they’re very helpful. However, they won’t work if the runner never starts (e.g. if you’re running locally and your computer is off) and they aren’t smart enough to know if something goes wrong with your script that’s not a failure of the run itself (e.g. the web page changes and your scraper isn’t able to collect information).

• Have a separate service to monitor for failure. A scheduled Claude Code job is a pretty good option here — have it run daily after your GitHub Action to check and make sure that it updated the txt file properly. If you’ve got a bunch of these running daily, it’s absolutely worth having an external service do a quick check on all of them.

I hope this was useful, and if you have any web scraping questions please don’t hesitate to tag me in the AIBMM chat!

Your turn

If you’ve been doing any kind of regular website checking by hand, this is a good place to start automating it.

Pick one thing you check weekly, share this article with Claude Code, and ask it to help you build a scraper for it.

And if you try it, we’d love to hear how it goes. Leave a comment with what you’re tracking, what you’d want to scrape, or any other use case in your work you’d like to automate.

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Found this useful? Share it with someone who could use it too.

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If this got you into Claude Code, here’s more of what I’ve built with it:

• Claude Code Routines — run automations like this scraper in the cloud, on a schedule, even with your laptop closed.

• I Built a Claude Code AI Agent That Runs My Content Distribution While I Sleep — six automations firing four times a day, hands-off.

• Claude Code for Beginners: The Workflow Behind 15 Live Projects — the exact setup behind everything I build.

• Build a Website with Claude Code — payments, email, SEO, analytics, all live.

• Do You Really Need Claude Code If You Already Have Cowork? — the decision rule, tested on 3 real tasks.

This post is free. Paid subscribers get access to all premium prompts and tools inside Amplifiers (the AI Blew My Mind MCP), weekly premium articles, all premium resources inside the AI Blew My Mind Lab, and exclusive partner discounts. Upgrade here.

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Why automating a newsletter with AI used to be painful (and what changed)

One of my long-time readers, Sid, has been asking me for a while for a way to automate the creation of a newsletter with AI.

He wanted a weekly AI-in-education roundup he could send to his university colleagues so they could all stay on top of how AI is changing their field.

I kept postponing it. The only way to build this reliably was through a complex n8n workflow: tracking RSS feeds for specific news outlets, relying on LLMs for the rest, then building guardrails on top of that, maybe even an LLM council to check links and catch hallucinations. Multiple APIs, multiple tools, and costs that add up fast.

And the biggest problem wasn’t even the time to build it. It was the links. Every AI model I tried for pulling current news had the same issue: it would confidently cite sources that were never published and link to pages that led nowhere.

That was the real wall.

Then we launched grounded web search inside Amplifiers, powered by You.com’s Research API, right inside Claude and ChatGPT. Results come back with real source URLs you can verify, which is the closest thing to reliable we have right now. We also added social media platform tracking, so you can pull in what people are saying about your topic across LinkedIn, X, Reddit, and more alongside the news.

That's dozens of research tools across eight platforms, all available inside Claude without you having to set up a single API, connector, or workflow node for any of them.

So I finally built Sid’s newsletter. And while I was at it, I built one for myself too (that now feeds into the bigger distribution agent I built in Claude Code).

• For Sid, I set up a newsletter about AI in education, The Rundown style, with AI-generated images for each main story, exported as a Word document he can review, tweak, and send whenever it's ready. Everything, including the images, is generated in one run. It runs as a scheduled task in Cowork. Here's a sample.

• For myself, I built a live artifact in Cowork so I can stay updated on the news and what people are talking about across X and Reddit, and refresh it whenever I have the time to read what’s new.

This works for any topic (competitor tracking, legislation changes, sports, politics, food blogs, movies, whatever you care about) and it’s the simplest, cheapest, most non-technical way to automate the creation of a news-style newsletter with AI.

One prompt, a couple of tools inside Amplifiers, and you’re done.

Today I want to show you exactly how I automated both newsletters inside Cowork: the full prompts behind mine and Sid’s, plus a no-code Newsletter Builder workflow you can use to spin up your own, for yourself or to share with others.

Let’s get into it.

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What we’ll cover:

• Who an automated AI newsletter is for (use cases)

• How to automate a news-style newsletter with AI: 2 examples

  • Example 1: A daily AI briefing as a live artifact

  • Example 2: A weekly AI-in-education newsletter

• How to build your own AI newsletter (any topic, any format, step by step)

• Trade-offs and limitations

Who an automated AI newsletter is for

You don’t have to be a newsletter creator to use this. If you need to stay informed or keep others informed, this is for you.

• Be the AI champion in your company. Take this workflow, build a newsletter tailored to your industry, your competitors, your business, and share it internally. When your colleagues ask “wait, how did you do that?”, who knows what doors that opens.

• Keep your small business afloat in a fast-moving world. Track what matters to you: legislation that affects your operations, market shifts in your niche, supply chain disruptions. It doesn’t have to be AI news. Even a conflict halfway across the world could have direct implications if you depend on shipping routes through that region. Make it about your business, whatever that looks like.

• Stay sharp as a freelancer or consultant. Track trends in your space, follow legislation or policy changes, see what people in your industry are discussing across platforms. All pulled together in one place, on your schedule. When a client mentions something they just read about and you already know the full picture, that changes how they see you.

• Run a community, teach, or lead a team. This is what Sid needed. If you’re responsible for keeping a group of people up to date on a topic, whether it’s students, colleagues, or members of a community, this replaces hours of manual curation.

• Build it for yourself. Not everything needs an audience. Maybe you just want one place where only the news you care about shows up, already filtered and summarized, ready when you have 10 minutes to catch up.

How to automate a news-style newsletter with AI: 2 examples

Below, I’ll walk you through how I built these two newsletters. Both are about AI, both use the same Amplifiers tools, but the scope and the output are different.

There are many ways you can go about this, and you can create your own variation on any topic, which I’ll cover in the next section.

Example 1: My daily AI briefing as a live artifact in Cowork

Since I run a newsletter about AI, I read the news a lot. But “a lot” still isn’t “everything”, and things move fast enough that I was missing stories that mattered.

So I built myself a daily AI briefing as a live artifact in Cowork (rather than a Word document or an email, because I open Claude the moment I open my laptop anyway, and I’d rather have everything right there instead of hunting through my inbox or files).

What it does: It pulls the latest news using grounded web search, tracks what people are discussing on X and Reddit, and lays it all out in a scannable two-column layout I can skim in a few minutes.

How I use it: I just open it and refresh it when I have the time to sit down and read what’s new.

How I built it:

Read more

If you’ve spent any time around AI audio in the past two years, you’ve run into ElevenLabs. Their voice technology is everywhere. Creators use it to clone their voice for video content. Publishers use it for audiobooks. Game studios use it for in-game dialogue. And a lot of us, myself included, have been using their API inside n8n workflows to build voice agents for months.

That last part was tricky. Building a voice agent through n8n was a pretty complex workflow, and not something you could put together without a developer or technical experience.

Then ElevenLabs launched ElevenAgents, and going from zero to a working voice agent can now be done in as little as 20 minutes, without any technical background.

Which is a big deal, because a lot of businesses need one and don’t have one. You probably interact with a few every week. Maybe you run one yourself.

The dental clinic, the law office, the SaaS company with a support line, the e-commerce store handling returns. When the team clocks out at 6pm, the calls don't stop. And most of what comes in after hours means a frustrated customer who didn't get help when they needed it or a potential one who just moved on.

Once the barrier to building a voice agent drops this low, it's no longer just about missed calls. It's a 24/7 support line, a sales assistant, a booking agent. There for your customers whenever you're too busy or not available.

So in this article, I’ll walk you through what ElevenAgents is and how I put together a customer support voice AI agent for Amplifiers (the AI Blew My Mind MCP), so you can see how to build one just as fast for your own needs.

Whether you want a receptionist, an outbound agent, or something completely different, the process is pretty much the same.

Let’s get into it.

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Here’s what we’ll cover

• What ElevenAgents is

• What ElevenAgents gives you out of the box

• The integrations that make a voice agent useful

• The 60+ agent templates you can start from

• How I built my Amplifiers FAQ agent, step by step

• What you can build next

What is ElevenAgents? (and why it matters now)

ElevenLabs has been the go-to for AI voice for a while now. With ElevenAgents, they built a full agent platform on top of it.

Agents that talk, type, and do things.

Under the hood, it has three core parts working together:

• The voice. Their text-to-speech engine, which has been the best on the market since day one. Sub-100ms latency, which means the conversation feels natural instead of feeling like a lagging video call.

• The brain. A large language model that understands what the caller said and decides what to say back. You can pick the LLM you want, from Gemini Flash to GPT-4o to Claude.

• The hands. Tools and integrations so the agent can do things during the call. Check a calendar. Send a text. Log a call in your CRM. Book an appointment. Pull data from a database.

And you can build everything in the dashboard with no code. It’s just buttons and clicks to get your agent up and running.

However, if you’re a developer or you want to build more complex post-call workflows, you can hook it into n8n or Make, or use the API.

What ElevenAgents gives you out of the box

Now, having a voice agent is one thing. Having one that people enjoy talking to is another.

If the agent sounds robotic, cuts people off mid-sentence, can't switch languages, or pulls generic answers that have nothing to do with your business, you're delivering a bad experience. And bad experiences don't bring people back.

Here’s what ElevenAgents gives you to make sure that doesn’t happen:

• Turn-taking. The agent detects when someone is done talking. It picks up on cues like “um” and pauses so it doesn’t cut people off mid-sentence.

• 70+ languages with mid-conversation switching. A caller can start in English, switch to Spanish, and the agent follows. If your business serves clients from multiple countries, or you’re bilingual and want to build a personal voice AI agent, you have over 70 languages to choose from.

• Knowledge base. Upload your FAQs, pricing, policies, SOPs, whatever documents describe how your business works. The agent pulls from them during calls, so it has all the information it needs to respond like your own employee would. This is retrieval-augmented generation (RAG), if you want the technical term.

• ExpressiveMode. The agent adjusts its emotional tone based on the conversation. Someone calls upset? It softens and slows down. Someone calls excited? It matches their energy.

I linked a video below so you can hear it yourself, because describing it doesn’t do it justice. Listen to the conversation and tell me in the comments, would you say it’s an AI agent?

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Which tools ElevenAgents integrates with

ElevenAgents connects to the tools your business already runs on. When a call comes in, the agent pulls data, books calendars, logs records, and triggers workflows while the caller is still on the line.

Here’s what’s available, grouped by category:

• CRMs: Salesforce, HubSpot, Zoho, Pipedrive, Monday.com, Jotform. Every call gets logged, every lead gets routed.

• Customer support: Zendesk, ServiceNow. The agent can open tickets, pull account history, and escalate with full context.

• Scheduling and communication: Google Calendar, Outlook, Cal.com, Calendly, Mailchimp, Slack. Booking appointments, sending confirmations, pinging your team.

• Data platforms: Airtable, Asana, Supabase, Palantir Foundry. For when the agent needs to read from or write to a database during the call.

• Payments and commerce: Stripe, Shopify. Check order status or process a payment in real time.

• Telephony: Twilio, WebRTC, SIP. How the agent connects to a real phone line.

• Inference providers: Swap the LLM powering your agent. OpenAI, Anthropic, Google, and others.

• Automation: Zapier, n8n, Make. If one of the native integrations doesn’t cover your tool, you can always bridge it through a workflow step.

And if your tool isn't on the list, you can build a custom integration through their API. That's how the more advanced setups get done, connecting tools that aren't natively supported yet. The full directory is at elevenlabs.io/agents/integrations.

60+ ElevenAgents templates to start from

If you want to build a voice agent, you don’t even have to start from scratch.

There are more than 60 pre-built templates. They cover pretty much what you’d expect (and beyond):

• Customer support and operations — technical support, IT help desk, order tracking, subscription support.

• Receptionists by industry — healthcare, veterinary, legal intake, salons, restaurants, hotels, property management.

• Sales and outreach — inbound lead qualifier, AI SDR, appointment setter, outbound sales rep, win-back caller.

• Education and HR — language tutors, training roleplay coaches, new hire onboarding, benefits hotline.

• Booking and after-hours — appointment scheduler, catering inquiry, after-hours answering, restaurant phone orders.

• Finance and feedback — accounts receivable, FDCPA-compliant collections, survey intake, feedback collector.

Most businesses will find something close enough to their use case to start from.

To use a template, select the one you want, then click “Use template” → “Connect your tools” → “Create Agent”.

They already come with the system prompt written, guardrails in place, and a conversation flow mapped out, so the agent knows how to branch a call based on how it goes. You can adapt and change all of it however you want.

After that, you configure it further just like I’ll show you below with the custom agent I built from blank for Amplifiers.

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How I built a 24/7 voice AI agent for Amplifiers, step by step

For this walkthrough, I'm building a voice agent for Amplifiers, the AI Blew My Mind MCP that you connect in two minutes to your Claude or ChatGPT and it supercharges it with tools it doesn't have out of the box and 90+ expert prompts across work domains.

Since we launched it a couple of weeks ago, I've received so many questions from you, especially around how to connect it and how to get started.

So I put together a document with the answers to the most frequent ones, plus info about everything inside of it.

Now let’s put it to use and build our voice AI agent.

Get started

Make sure you’re on the ElevenAgents platform. ElevenLabs has multiple products (ElevenCreative, ElevenAgents, ElevenAPI), so click the dropdown in the top left and select ElevenAgents.

Step 1. Create a new agent

I didn’t use a template for this one. I wanted to show you how easy it is to set everything up from scratch. So I went to “Agents” in the left side menu, clicked “+ New agent”, and started with a blank setup.

From there, you go through a few steps to start configuring your agent.

First, you pick what type of agent you want to create: Blank Agent, Personal Assistant, or Business Agent. I chose Business Agent.

Then it asks what industry your business is in. I chose Technology & Software.

And what use case you need it for. I chose Customer Support.

Last step: complete your agent. You give it a name, optionally add your website so it can pull public info to personalize the agent, and describe the main goal. There’s also a “Chat only” toggle here if you want a text-based agent without voice. I left it off since I’m building a voice agent.

Then you hit “Create Agent”, wait a few moments for the agent to be finalized, and you’re in the dashboard

Step 2. Configure the agent (system prompt, voice, language, LLM)

When you land in the dashboard, the agent already comes with a system prompt, a voice, a language, and a first message, generated from what you filled in during setup. All of it configurable with clicks, no coding.

Here's how I configured it to make it final and ready to go.

System prompt

The system prompt is the core of your agent. It defines who the agent is, how it behaves, what it should and shouldn’t do.

The auto-generated one was a solid start, but I wanted something more tailored. So I used the ElevenAgents Voice Prompt Builder I created inside Amplifiers.

It interviews you about the agent, then writes the full prompt following a six-section structure that ElevenAgents is tuned to work with: Personality, Environment, Tone, Goal, Guardrails, and Tools.

A few questions, a ready-to-paste prompt, dropped it into the dashboard, done.

If you don't use Amplifiers, you can either tweak the system prompt that's already generated or use the standalone prompt builder I created. Either way, keep the six-section structure. That's what ElevenAgents is tuned to work with.

Set the first message

The first message is what the agent says when the conversation starts. Mine is:

“Hi, thanks for reaching out about Amplifiers. I can help you with setup, troubleshooting, or any questions about the tools inside. What can I help you with?”

Pick the voice

The voice library has a lot of options. For a product support agent, I wanted something warm and encouraging, so after testing a few, I picked Lauren.

You can also turn ExpressiveMode on or off from here. I’d keep it on. You saw how natural it sounds. And there are suggested audio tags you can add if you want the agent to laugh, sigh, or speak with specific emotion. I didn’t select anything here for now.

Select the languages

This is also where I selected multiple languages. I turned on English and a bunch of other languages based on where most of my audience is.

The agent detects which language the caller is using and switches automatically. For a global product with users from different countries, this was a no-brainer.

Choose the LLM

You can pick which language model powers the agent. I kept Gemini 2.5 Flash, which is fast and cost-effective for an FAQ agent. If your agent needs to reason through more complex situations, you can switch to other more powerful ones.

And this is what the full configuration looks like when everything is set:

On the right side, you can already test it from here. But I didn’t want to do that yet. First, I needed to add the knowledge base.

Step 3. Upload your knowledge base

This is where the agent gets the expertise it needs to help people. The system prompt tells it how to behave. The knowledge base tells it what to say.

Navigate to the Knowledge Base tab and click “Add document”. From there you get three options:

• Add URL: paste a link to your website, public documentation, or any page you want the agent to reference.

• Add files: upload documents directly. Supported formats include PDF, DOCX, EPUB, HTML, and Markdown. This is what I used. I uploaded the FAQ document I had already prepared before starting the build.

• Create text: write or paste content directly into the platform.

There’s also more advanced configuration like RAG settings, but we won’t get into that here.

Step 4. Add tools to your voice agent (WhatsApp example)

This is where the agent goes from “answers questions” to “answers questions and does stuff”.

My setup isn’t that complex. I don’t have a CRM or a ticketing system to connect to like you might. But I wanted the agent to send users a WhatsApp summary of the conversation if they need the steps in writing, like for troubleshooting or setup.

Here’s how I set it up:

First, I navigated to the “Integrations” tab in the left side menu. From there, I clicked “+ Add integration”.

I selected WhatsApp from the Telephony category.

Then it asked me to configure the WhatsApp connection and I clicked “Import an account here”.

Which took me to the WhatsApp accounts page where I clicked “+ Import account”.

A new window opened where I had to set up my WhatsApp Business profile. I already had a Facebook page for AI Blew My Mind, so this was just 5 quick steps to connect it.

Once I went through all the steps, I clicked “Connect”. Done.

Next, I went to the “Tools” section in the left side menu.

I clicked “Add Integration tool”, selected my WhatsApp integration, and checked the “Send Message” tool.

Finally, I navigated back to my agent from “Agents” in the left side menu, went to the “Tools” tab, and clicked “Add tool”. WhatsApp was showing up there, so I selected it.

Once it’s added, you can also adjust the System tools on the right side. These let the agent perform built-in actions like ending a conversation, detecting language, transferring to another agent or phone number, and more.

Now that we have the WhatsApp tool set up, we can create a small workflow to integrate it into the conversation flow.

Step 5. Build the conversation workflow

If your agent just answers questions and that’s it, you can skip this. But if you want the agent to do something specific at certain points in the conversation, like send a message, transfer to a human, or change behavior based on what’s happening, this is where you build that.

You connect nodes, set conditions, and the agent follows the flow during the conversation.

My workflow is simple. All I want is for the agent to offer to send the user a WhatsApp message with a summary of the conversation once it’s wrapping up.

If you don’t want to build from scratch, you can start from a template like I showed you earlier. And if you want to dive deeper into how workflows work check the workflows documentation.

My workflow overview

The user and the AI agent have a conversation. When the conversation reaches an end, the agent asks whether the user wants a WhatsApp summary. If they do, it asks for their phone number and sends the message, then ends the conversation. If the user doesn’t want a summary, it just ends the conversation.

How I configured each node

I built this one from scratch (not from a template).

Start node. This is the entry point of the workflow. Nothing to configure here, it just kicks things off.

From the Start node, I clicked the + icon and added a Subagent node.

Subagent 1: Main conversation. This is where the agent has the full conversation with the user. I set the conversation goal to answer all questions about Amplifiers using the knowledge base, help with setup and troubleshooting, and stay in the conversation until the user says they’re done.

Now that the main conversation subagent is in place, I needed a second subagent for when the conversation reaches an end. This one handles the wrap-up: it asks if the user wants a WhatsApp summary of what was discussed.

Subagent 2: WhatsApp summary offer. This is where the agent asks if the user wants a conversation summary sent to their WhatsApp, collects their phone number if they say yes, and sends it. You can see the full conversation goal in the screenshot.

Now we need a transition rule between the main conversation and the WhatsApp summary offer, so the agent knows when to stop answering questions and move on to asking about the summary. I clicked on the connecting line between the two subagent nodes and set up the condition.

Edge: “Conversation wrapping up”. I set the transition type to LLM Condition. This means the agent evaluates in real time whether the conversation has reached an end before moving to the next node and asking about the WhatsApp summary.

From the WhatsApp summary offer subagent node I created earlier, I added two more nodes: a Dispatch tool node (for sending the WhatsApp message) and an End node (for when the user doesn’t want a summary). You need to create the nodes first so you have the connecting lines to configure.

Dispatch tool node. I selected the whatsapp_send_message tool. This is the tool I set up earlier. When the conversation reaches this node, it sends the summary to the user’s WhatsApp.

After the Dispatch tool, I added another End node so the conversation closes once the message is sent. Nothing to configure here. It just ends the conversation.

Now with all nodes in place, I configured the two edges coming out of the subagent node.

Edge: “User wants WhatsApp summary”. LLM Condition: “The user has agreed to receive a summary on WhatsApp and has provided their phone number including country code.” This leads to the Dispatch tool node.

Edge: “User declines summary.” LLM Condition: “The user has declined to receive a WhatsApp summary or said they don’t need one.” This leads directly to the End node.

And that’s the full workflow. Six nodes, three conditions, and my agent now offers a WhatsApp summary at the end of every conversation.

Step 6. Test your voice agent with simulations

This is one of the best parts of ElevenAgents. Instead of calling your agent yourself fifty times to check if it handles every situation correctly, you can run automated simulations.

You set up different scenarios, different types of users, different questions, and the agent runs through all of them on its own. You get to see the full transcript and whether it passed or failed. If something’s off, you go back and adjust the prompt or the knowledge base.

There are three types of tests under the "Tests" tab:

• Simulation testing is what I used. It runs a full end-to-end conversation with a simulated user, so you can see the entire workflow playing out without being on the call yourself. You click “Add test”, pick a simulation, and the agent runs through the conversation on its own.

• Scenario testing evaluates specific conversational moments. You set up a situation and check how the agent responds to it.

• Tool call testing verifies that the agent uses tools correctly and passes the right parameters.

One thing to watch out for when testing: before running a simulation, increase the maximum conversation turns. It defaults to 5, which cut off my first tests before the workflow reached the WhatsApp summary step.

Here’s how one of my simulations went from start to finish. Everything worked smoothly:

You can also hit “Preview” in the top right corner to start a live conversation with the agent yourself. You talk to it, it talks back, in real time.

Step 7. What else lives inside the dashboard

There’s more in the ElevenAgents dashboard that I didn’t use for this build but worth knowing about.

Analysis. Set up post-conversation evaluation. You define what counts as a successful conversation and what data to extract (contact details, issue types, feedback). The agent evaluates this automatically after each conversation. More in the docs.

Widget. If you’re embedding the agent on a website, this is where you configure how it looks.

Security. Set rate limits, data retention rules, conversation recording preferences, and PII handling. Worth spending a few minutes here before you go live.

Advanced. Custom LLM configurations, post-call webhooks, event handling, and more. If you’re building something more complex or integrating the agent into a larger product, this is where you’d go.

Step 8. Go live and iterate

Deploy the agent. Watch the conversation logs for the first week. Every time the agent gets something wrong, the transcript usually shows you exactly what to fix.

Missing info in the knowledge base? Add it. Agent giving too long an answer? Tighten the system prompt. Someone asking a question you didn’t anticipate? Update the knowledge base.

The first version is never perfect. That’s fine. The iteration loop is fast because everything is in one dashboard and only takes a couple of clicks to adjust.

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What you can build with ElevenAgents (beyond customer support)

I know this might look like a lot of steps, but that’s because I documented every single one of them. In practice, the whole process took me about 30-40 minutes.

As you saw, you don’t need to be technical to do this. You don’t need to know how to code. You just need to know what your agent should do, write a good system prompt (or use the Amplifier to write it for you), and create a workflow if your agent needs to do more than just talk. That’s it.

You can build a personal voice AI agent for yourself, like a personal assistant. Or one for your business: a customer support agent that handles questions 24/7, a receptionist that takes calls and books appointments, a lead qualification agent that pre-screens prospects and routes them to your sales team, or an outbound agent for follow-ups and campaigns. For your product, your app, your website, wherever you need it.

Either way, the result is the same. You convert more, deliver a better experience, show up when your customers need you and you’re not available, and stop losing people to a voicemail nobody checks.

What will you build? Leave a comment and tell me.

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And if you found this useful, share it with someone who could use a voice agent for their business and didn’t know it’s this easy.

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This post is free. Paid subscribers get access to all premium prompts and tools inside Amplifiers (the AI blew my mind MCP), weekly premium articles, all premium resources inside the AI blew my mind Lab, and exclusive partner discounts. Upgrade here.

Short version: AI research collection is broken in two ways. Web AI hallucinates fake links and articles. Social platforms research require API keys, n8n workflows, and technical knowledge. Amplifiers fixes both:

• 31 research tools across 9 platforms and the open web — YouTube, Reddit, LinkedIn, Instagram, Facebook, Twitter/X, Google, Company Registry, and the open web.

• All inside Claude and ChatGPT — just ask in natural language

• Zero API keys, zero technical setup, less hallucinations

Below: what each tool does, what it would cost you to build yourself, and how to set it up in 2 minutes.

The research collection problem (and why AI alone can't solve it)

We all do research. Tracking what’s happening in your industry, monitoring social platforms, studying competitors, keeping up with trends, understanding a topic well enough to make a decision or create something from it. It's one of the most time-consuming things we all do.

In my last article I talked about using Cowork to glue workflows and automations together into full systems, and I mentioned that research is one area where I never managed to do that until now. There are too many variations, too many platforms, too many ways you might need to approach it.

Part of why it’s so hard to systematize is that research has two distinct phases:

• There’s the collection phase, where you go out and search for things across platforms.

• And there’s the processing phase, where you read what you found, save it, synthesize it, and turn it into some kind of output or understanding.

For processing, AI has made things significantly easier. I use Cluing for saving ideas I come across. NotebookLM for answering questions from my sources only. And Claude for working with what I've collected, connecting ideas, and turning them into something useful.

But research collection? That part is still painful. And it’s just as time-consuming as processing, if not more.

Most of our collection happens in two places: the web and social platforms. And neither has a good solution yet.

For web research, we increasingly rely on AI to search for us because doing it manually takes too long. But our AI assistants make things up. Fabricated news, invented links, presented with full confidence.

I saw this firsthand when I built the same news app in Perplexity, Cowork, Claude Code, and Manus and they returned fabricated articles or broken links. And it’s not just anecdotal. A recent BBC-led study I came across through confirmed it at scale: 45% of AI answers about news had significant issues.

For social platforms, it’s a different problem. You either scroll manually and spend hours on it or you build complex automations: get an API key for each platform, figure out authentication, create a workflow in n8n, and maintain it when something breaks.

I did this once for Reddit and never got around to building the rest. Most people wouldn’t either, whether because of time or the technical knowledge needed.

That’s the research collection problem: unreliable on the web, too manual or too technical on social platforms.

Because I was dealing with this, and because I know it’s a problem for everyone, we built research collection tools directly into Amplifiers, the AI Blew My Mind MCP that gives your Claude and ChatGPT capabilities they don’t have on their own, along with over 90 expert prompts across work domains.

But I'd rather show you than tell you, so let's get into it.

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What’s inside this article:

• The research collection problem (and why AI alone can’t solve it)

• 31 research tools across 9 platforms, no setup needed

• YouTube research tools: transcripts, trends, and channel analysis

• Reddit research tools: conversations, communities, and ad intelligence

• LinkedIn research tools: company profiles, creators, and content strategy

• Instagram research tools: reels, engagement data, and transcripts

• Facebook research tools: ad library and creative analysis

• Twitter/X research tools: profiles, top tweets, and engagement data

• Google search inside your AI chat: organic results, instantly workable

• Company registry: global business data across 244 countries

• Open web research: grounded AI search with verified sources

• What else is inside Amplifiers:

  • Native image generation and editing inside Claude

  • 92 expert prompts across 11 work domains

  • Save and customize prompts inside your chat

• What this would cost you to build without Amplifiers

• How to set up Amplifiers

30 research tools across 9 platforms, no setup needed

Amplifiers now includes 30 research tools across YouTube, Reddit, LinkedIn, Instagram, Facebook, Twitter/X, Google, and the open web.

What makes all of these different (and much better) from asking an LLM to search for you: they are APIs to each platform. They pull data exactly as it exists, directly from the source, the same way you’d get it if you built the automations yourself.

Let me now walk you through what each platform gives you.

YouTube research: transcripts, trends, and channel analysis

YouTube is where a huge amount of valuable content lives, but getting data out of it programmatically has always been difficult. Even Gemini, Google’s own model, won’t give you a video’s transcript. You can only ask questions about it.

With the YouTube tools in Amplifiers, you just ask Claude or ChatGPT, and describe what you need in natural language.

Here’s everything you get access to:

What you can do with it in Amplifiers:

• Competitor and creator research: Search for videos by keyword or hashtag to see what’s being published in your space. Pull a channel’s full profile, browse their video library by latest or most popular, and check their linked socials.

• Content analysis: Get any video’s complete transcript with timestamps, read the top comments to understand audience reactions, and pull full metadata like views, likes, chapters, and keywords.

• Trend monitoring: See what’s currently trending in YouTube Shorts, with a fresh batch of ~48 videos every time you ask.

Reddit research: conversations, trends, and ad intelligence

Reddit is one of the richest sources of unfiltered opinions, product feedback, and market signals. But accessing it at scale has gotten harder, not easier. Reddit changed their API policy, making it significantly more expensive and complex to build automations. I built a Reddit market research automation a while ago and today it would be even harder to set up.

With the Reddit tools in Amplifiers, you just ask Claude or ChatGPT to use the Reddit amplifier, and describe what you need in natural language.

Here’s everything you get access to:

What you can do with it:

• Market research: Search across all of Reddit or within specific subreddits to find what people are saying about your industry, your competitors, or your product category. Filter by time and sort by relevance, top, or newest.

• Community monitoring: Browse any subreddit’s posts by hot, new, top, or rising. Get subreddit details like subscriber count, active users, and community rules to evaluate whether a community is worth tracking.

• Ad intelligence: Search Reddit’s Ad Library by keyword and filter by industry, budget level, and format. Get full ad creative details with an AI-written analysis of headline effectiveness and media strategy.

LinkedIn research: company profiles, creator activity, and content strategy

LinkedIn is essential for competitive research, prospecting, and understanding what companies and creators are doing in your space. But LinkedIn makes it notoriously difficult to extract data. There’s no public API for most of what you’d want to research. You either scroll manually, pay for Sales Navigator, or use scraping tools that break regularly.

With the LinkedIn tools in Amplifiers, you just ask Claude or ChatGPT to use the LinkedIn amplifier, and describe what you need in natural language.

Here’s everything you get access to:

What you can do with it:

• Competitor research: Pull a company’s full LinkedIn profile including industry, size, headquarters, funding stage, and similar companies. Browse their recent posts to see what content they’re publishing and how they’re positioning themselves.

• Creator and prospect research: Get any person’s public profile with their experience, education, follower count, recent posts, and articles. Useful for prospecting, partnership research, or studying what creators in your space are talking about.

• Content strategy: Browse paginated company posts to analyze publishing frequency, content themes, and engagement patterns over time.

Instagram research: reels, engagement data, and content transcripts

Instagram is one of the hardest platforms to research. Almost everything is locked behind a login wall, there’s no useful public API, and even basic things like searching for reels by keyword require you to be logged in. If you’ve ever tried to systematically study what creators in your niche are posting, you know the drill: scroll, screenshot, repeat.

With the Instagram tools in Amplifiers, you just ask Claude or ChatGPT to use the Instagram amplifier, and describe what you need in natural language.

Here’s everything you get access to:

What you can do with it:

• Content discovery: Search for reels by keyword without needing an Instagram account. Filter by date to find what’s trending right now or what was popular last week. This bypasses Instagram’s login gate entirely.

• Creator analysis: Browse any user’s public reels with full engagement data: play count, likes, and comments. Useful for studying competitors, potential collaborators, or creators in your niche.

• Post deep-dive: Pull the full details of any Instagram post or reel, including caption, creator info, engagement metrics, video URL, and top comments.

• Transcript extraction: Get an AI-generated speech-to-text transcript for any reel or video post under two minutes. Turn spoken content into text you can analyze, quote, or repurpose.

Facebook research: ad library, competitor ads, and creative analysis

Facebook’s Ad Library is one of the most powerful competitive research tools available, and it’s completely free and public. The problem is that using it manually is painfully slow. The interface is clunky, filtering is limited, and there’s no way to pull data out at scale. If you’ve ever tried to systematically analyze a competitor’s ad strategy, you know how long it takes to click through ad after ad.

With the Facebook tools in Amplifiers, you just ask Claude or ChatGPT to use the Facebook amplifier, and describe what you need in natural language.

Here’s everything you get access to:

What you can do with it:

• Competitor ad research: Search the entire Meta Ad Library by keyword with filters for country, ad status (active or inactive), and media type. See exactly what copy, creatives, and CTAs your competitors are running right now.

• Advertiser discovery: Find any advertiser by company name and get their page ID. Use that to pull their complete ad history, not just what’s currently running.

• Creative analysis: Get the full details of any ad, including all creative variations and optional video transcripts. Useful for studying what messaging approaches, formats, and hooks other companies are testing.

Twitter/X research: profiles, popular tweets, and engagement data

Twitter/X is where industry conversations happen in real time. But extracting data from it has become increasingly difficult. The API is expensive, third-party tools keep breaking, and even basic things like pulling someone’s top tweets with engagement metrics require paid access or workarounds.

With the Twitter/X tools in Amplifiers, you just ask Claude or ChatGPT to use the Twitter amplifier, and describe what you need in natural language.

Here’s everything you get access to:

What you can do with it:

• Profile research: Pull anyone’s public profile with their bio, follower and following counts, location, verified status, and join date. Useful for prospecting, partnership evaluation, or studying how people in your space position themselves.

• Content analysis: Get a user’s ~100 most popular tweets with full engagement metrics: likes, retweets, replies, and view counts. See what topics and formats get the most traction for any account you’re researching.

• People and brand tracking: Keep up with the people and brands you care about most. Pull their latest popular posts anytime to stay current on what they’re publishing, what’s resonating with their audience, and what direction they’re moving in.

Google search: organic results without the noise

You can already search Google in your browser. But the value here is in what happens after the search. When you run a Google search through the amplifier, Claude gets the results directly and can immediately work with them: summarize, compare, extract patterns, filter for what’s relevant, or combine with data from other platforms in the same conversation.

With the Google tool in Amplifiers, you just ask Claude or ChatGPT to use the Google amplifier, and describe what you need in natural language.

Here’s what you get:

What you can do with it:

• Targeted search: Run Google searches with region targeting and date filtering. Narrow results to the last hour, day, week, month, or year. Target specific countries for localized results.

• Research at scale: Run multiple searches in the same conversation and ask Claude to compare, summarize, or cross-reference the results. Combine Google results with data from other amplifiers (Reddit discussions, LinkedIn profiles, YouTube videos) to build a complete picture without leaving the chat.

Company registry: global business data across 244 countries

If you’ve ever needed to verify whether a company is real, check when it was incorporated, or look up its legal status, you know how painful it is. Every country has a different registry, a different website, a different interface. Some charge for access. Some are barely searchable.

With the company registry tools in Amplifiers, you just ask Claude or ChatGPT to use the company registry amplifier, and describe what you need in natural language.

Here’s what you get:

What you can do with it:

• Company verification: Search over 521 million legal entities across 309 jurisdictions and 244 countries. Find any company by name or registration number, check its legal status, incorporation date, legal form, and registered address.

• Due diligence: Pull the full registry record for any company, including identifiers like LEI numbers for financial lookups. Filter by jurisdiction and registration status to narrow results.

Open web research: grounded search with the highest accuracy available

This is the newest tool in Amplifiers and I’m very excited about it. When you search the web through Claude or ChatGPT normally, you’re relying on the LLM to find and summarize sources. And as we covered in the intro, that’s where hallucinations happen: fabricated links, invented articles, confident nonsense.

The grounded web search in Amplifiers works differently. It’s powered by You.com’s Research API, which currently ranks #1 on DeepSearchQA with 83.67% accuracy, the highest publicly reported score.

Every result comes back with a real source URL, title, and text snippets. No hallucinated links. No fabricated articles. Just real sources you can verify.

With the open web tool in Amplifiers, you just ask Claude or ChatGPT to use the web search amplifier, and describe what you need in natural language.

Here’s what you get:

What you can do with it:

• Grounded research: Search the web and get results with real, verifiable source URLs. Every result includes title, description, and text snippets you can cite. Less checking if a link is real before sharing it.

• News monitoring: The tool automatically detects when your query has news intent and returns both web results and news articles. Filter by freshness (last day, week, month, year) to stay current on fast-moving topics.

• Domain control: Include or exclude specific domains from your results. Search only within trusted sources, or block low-quality sites. Supports search operators like site:, filetype:, AND/OR/NOT for precision research.

What else is in Amplifiers

Research tools are the newest addition, but there’s a lot more inside.

Image generation and editing

Claude can now generate images natively using Amplifiers. Amplifiers includes five image generation prompts designed for professional outputs: infographics, podcast cover art, and structured prompt-to-image workflows.

In ChatGPT, the same prompts work with GPT’s image model.

The difference from just asking for an image: these prompts guide you through style selection, content confirmation, and aspect ratio before generating. You get consistent, professional results instead of random outputs.

You also get a new background removal tool that strips any photo to a transparent PNG in seconds.

And since image generation is built into the same tool, you can turn your research directly into infographics and visuals for your content without leaving the conversation.

92 expert prompts across 11 work domains

Business, Marketing, Writing, Operations, Thinking, Teaching, Coding, Prompting, Image Generation, Life Design, and Learning.

These aren’t generic prompts. They’re structured workflows that interview you, adapt to your context, and produce outputs you can use immediately.

Save and customize prompts

The biggest problem with prompts is that you never save them where you use them. They end up in notes, bookmarks, or screenshots, and you never find them when you need them.

With Amplifiers, you save prompts directly inside Claude or ChatGPT. Just say “save this to my prompt library” and it’s there next time you need it.

You can also take any prompt from the Amplifiers catalog, adapt it with your own context, and save that customized version. Next time you use it, your context is already built in. No re-answering the same questions every time.

Your personal library is private. Other users can’t see your saved or customized prompts.

What all of this would cost you without Amplifiers

Building just the research tools yourself would cost you hundreds of dollars a month in API fees across platforms (n8n at $20/month to run the automations, Apify or similar scraping tools for platforms without public APIs, X API that can reach hundreds per month for read-heavy research).

And that's before you factor in the social listening tools you'd need for platforms like LinkedIn and Instagram that don't offer public APIs for this kind of data.

Plus the time and technical knowledge to build and maintain everything. And if you don't have the technical skills, you're paying an expert thousands of dollars to build all of these automations for you.

With a Premium subscription to AI Blew My Mind at $12/month (or $100/year), all of this is included:

Inside Amplifiers:

• 30 research tools across eight platforms and the open web. No API keys, no technical setup, no maintenance.

• The most powerful prompts in the catalog: skill builders, negotiation coaches, structured workflows.

• Professional image generation directly in Claude, plus background removal.

The newsletter:

• Three deep dives per week on how to use AI for your work. Practical use cases and beginner-friendly how-to guides so you’re never left wondering how to implement something.

AIBMM Lab and perks:

• Full access to every premium resource in the AIBMM Lab resource library.

• Discounts to AI Blew My Mind tools: 50% off AI Migrator, $100 in credits for MyGhosts.

• Exclusive partner discounts: 20% off Cluing, 25% off HeyReach. These discounts alone can cover the cost of the subscription.

I'm not one to brag, but try putting together everything you get here from somewhere else for $12 a month.

Get access to everything

How to set up Amplifiers

Setup takes just a few minutes, whether this is your first time or you already had it installed. Below, I’ll walk you through both paths, plus a few things some of you have been asking about when it comes to logging in.

How to set up Amplifiers in Claude

Step 1. Add the connector

Open Claude in the desktop app. Go to Customize in the left side panel, click Connectors, then the + icon. Select Add custom connector. Name it “AI blew my mind” and paste this URL: https://mcp.aiblewmymind.com. Click Add.

Step 2. Connect it

Go back to the Connectors section and scroll down to Not Connected. Find the AI blew my mind MCP and click on it. Hit Connect. This takes you to auth.aiblewmymind.com where you’ll create your account. Make sure you use the same email you subscribed to AI Blew My Mind with. Then click Always allow for all tool permissions.

Step 3. Add your Gemini API key (optional, for image generation only)

Your first 3 image generations are on me. After that, you need to add your own API key to keep generating images. All prompts and research tools work without it.

1. Go to Google AI Studio and sign in with your Google account.

2. Click Get API key in the left menu.

3. Click Create API key, then Create API key in new project.

4. Copy the key and store it somewhere safe.

5. Head to auth.aiblewmymind.com and add your API key there.

Done. You only pay for credits on the images you generate. Nothing else.

How to set up Amplifiers in ChatGPT

1. Go to chatgpt.com in your browser.

2. Open Settings → Apps → Advanced Settings.

3. Toggle on Developer mode.

4. Click Create apps.

5. Name it “AI blew my mind”.

6. In MCP Server URL, paste: https://mcp.aiblewmymind.com/

7. Set Authentication to OAuth.

8. Click Create, then Connect.

9. Create your account on auth.aiblewmymind.com using the same email as your AI Blew My Mind subscription to connect.

If you already had Amplifiers installed

If Claude or ChatGPT can’t find some of the new tools, you need to do a quick reconnect.

• In Claude: Go to Customize → Connectors, find the AI blew my mind connector, disconnect it, remove it, then add it again and connect it from scratch.

• In ChatGPT: Go to Settings → Apps, find AI blew my mind app, disconnect it, remove it, then re-add it following the steps above.

This refreshes your connection and pulls in all the latest tools.

One thing to keep in mind

Both Claude and ChatGPT sometimes default to using their own built-in tools instead of the amplifiers (like web search). If you notice the results aren’t coming from Amplifiers (you can check by expanding the activity messages in the chat), just be specific. Say “search with the Reddit amplifier” or “use the web search amplifier” and they’ll use the right one.

Your turn

Research collection has been the missing piece for a long time. Too manual, too technical, or too unreliable.

Now it’s built into the same place where you already do your work.

Set up Amplifiers, try the research tools, and see what happens when your AI can pull real data from real platforms instead of making things up.

I’d love to hear how you use them. Drop a comment and tell me what you’re researching, which tools you tried first, and what surprised you.

Leave a comment

And if you found this useful, sharing it would be the best gift you could give me.

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“The challenge isn’t any single piece. It’s stitching it all together into something that runs without me babysitting every step.”

And that’s the problem, isn’t it?

We’re getting better at building individual automations. A Cowork scheduled task here, an n8n workflow there, a Claude Code routine you set up last week.

But your monthly report doesn't know your expense tracker just finished categorizing last week's receipts. Your lead magnet process doesn't know a new article is ready to be turned into a downloadable guide. Your client onboarding sequence doesn’t know you just signed a deal.

You’re the one who finishes step A and remembers to go start step B. The glue between your own automations. The human cron job stitching everything together because nothing connects them for you.

When people talk about AI productivity and building a “personal operating system”, they’re almost always thinking about one of two things:

• On one side, task management. An AI that summarizes your day, tells you what to focus on, reminds you about deadlines. The "AI chief of staff" crowd.

• On the other, deep technical orchestration. Multi-agent architectures, custom pipelines, code-heavy systems that most non-technical people will never build.

There’s a whole middle ground that nobody’s covering: the part where you already have working workflows, and you just need them to talk to each other without you being the middleman at every handoff.

Because if your workflows don’t talk to each other, you don’t have a personal operating system. You have islands.

That’s what I’m trying to figure out: how do I connect the things I’ve already built so work flows through them instead of getting stuck at every junction, waiting for me?

This article is the beginning of that. I believe there are multiple ways to stitch your workflows together, and the right one depends on where your work and automations already live. That’s why I want to show you more variations.

So today we’ll start with Cowork and how to set up scheduled tasks that talk to each other, so the output of one becomes the input of the next.

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In today’s article:

• Why your AI workflows need to work as a system: Why a pile of disconnected automations isn't a system. It's islands. What that's costing you, and the mental model that makes the mess visible.

• How to build and connect your AI workflows: a 5-step framework 🔒: A framework for going from scattered AI automations to a connected system that runs without you. How to see your workflows clearly, spot the handoffs where you're just the glue, and figure out what still needs you.

• How to stitch automated workflows in Cowork 🔒: The practical part. Two real Cowork scheduled tasks stitched together, with a shared database as the bridge. Exact prompts, connectors, the full setup. No code.

• Cowork Scheduled Task Builder — a new Amplifiers prompt 🔒: Don't know where to start? This prompt walks you through designing your own chain of scheduled tasks, whether you're connecting existing ones or building from scratch.

Why your AI workflows need to work as a system

Think about a company’s customer support. A customer has a problem, reaches out, and gets it solved. A week later, same problem. Different person picks it up, gives a completely different solution, and this time it doesn’t get solved.

Same company, same problem, two different outcomes. That’s what it looks like without a system.

Now think about your AI workflows. If the output changes every time depending on which tool you opened, which chat you found, or whether you remembered to copy something between apps, you’re in the same spot. You don’t have a system. You have disconnected steps that sometimes produce something.

And when the steps needed to do only live in your head, three things happen:

1. You can’t step away. You’re the only one who knows how things connect. Everything stalls without you.

2. Every task takes more energy than it should. You’re not just doing the work. You’re deciding how the work gets done, every single time.

3. You can’t delegate. Not to a person, not to AI. There’s nothing repeatable that someone or something else could pick up and run.

And yet, it’s not that simple. A system that runs your business is made of multiple subsystems, and each subsystem is made of individual processes. It’s layers inside layers, and that’s what makes it feel overwhelming.

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Systems, subsystems, and processes: how to map your AI work

• At the top, you have your big systems. These are the major areas of your business: content, products, finance, client work.

• Each system is made of subsystems. These are the specific functions inside each area. Inside your content system, for example, you might have newsletter production, a brand visual system, social media repurposing, and lead magnet creation as separate subsystems.

• And each subsystem is made of processes. These are the individual steps that get the work done. Inside newsletter production, the processes might be: research and outline, write the draft, create visuals, final polish, publish and distribute.

Every business and individual runs on a few big systems like this and most of us are already doing all of this work, every day.

We just haven’t stopped to map it out, to see where we’re just passing things from one step to the next and where we’re the ones making the decisions.

How to build and connect your AI workflows: A 5-step framework

Read more

One of the most frequent questions I’ve received since I started this newsletter a year ago is how to save the things you read online so you can find them again when you need them.

It’s the core problem an AI second brain is supposed to solve — a system outside your head that captures, organizes, and surfaces information the moment you need it.

I remember at some point in the beginning I told one of you about using bookmarks to save links. Then I moved to Notion and built a Telegram personal assistant that could save every new idea or finding in there. I tried Obsidian for a couple of weeks but couldn’t stick with it. Most days I still end up in Apple Notes (where I’m using a Cowork scheduled task to resurface weekly what I'd saved), and I use NotebookLM when I need to dig into research and make sense of it.

None of it fully solves the problem, and I know it’s not just me.

We all consume more information than we can process. And we are all drowning in ideas we find interesting but never do anything with, because we forget to save things, we can’t find them when we do, and even when we find them, we can’t easily connect our research to the rest of what we know.

What I need is to save an article, a quote, a video, a post, right there, in the moment, inside the app where I found it. Have it organized easily. And when I need it later, find it in seconds.

That’s what a digital second brain should do. It remembers things you forgot you saved, and connects information across sources in ways you wouldn’t on your own.

So when Cluing reached out, I was very curious to test it.

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Meet Cluing

Cluing captures insights from everywhere you already read and watch (PDFs, Kindle, YouTube, LinkedIn, Reddit, Instagram, Twitter, Google Docs, Gmail), keeps them organized, and lets you use AI to make sense of everything you’ve saved.

It comes as a browser extension, a mobile app, and a desktop app.

• Save a LinkedIn or Instagram post from your phone or computer into a collection.

• Highlight a paragraph from any webpage or PDF on your computer.

• Open your dashboard to see everything you’ve collected and use AI to connect it all.

And today I’ll walk you through how I used Cluing to research and write an article from start to finish, so you can see how it works in practice.

Stay until the end, because I’ll also share a resource I created with all the best free AI courses currently available.

In this article:

• How Cluing organizes your second brain using the PARA method

• How I used Cluing to research and write an article (step by step)

• 6 more ways to use Cluing in your daily work

• My free AI courses collection (built inside Cluing)

How Cluing organizes your second brain using the PARA method

The first thing you see when you open Cluing is that everything is organized using a framework called PARA: Projects, Areas, Resources, and Archive. It’s a system created by Tiago Forte that gives every piece of information a place to live.

Here’s how it works:

• Projects are the things you’re actively working on, with a clear beginning and end. Building a website, preparing a pitch, researching a new tool. Everything related to that work lives in one project. When it’s done, you archive it.

• Areas are the parts of your life or work that never really end. Marketing, sales, health, leadership. Whenever you come across something useful about one of your areas, you save it there so it’s ready when you need it.

• Resources are curated reference libraries. Notes from a course, a breakdown of a podcast, takeaways from a conference. Things you pull from repeatedly and share with others.

• Archive is how you keep everything clean without losing anything. When a project is done or a resource isn’t current anymore, you move it to the archive. It’s out of your active view, but still searchable.

But the best way to see how this works is through real examples. So let me show you how I’ve been using it.

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How I used Cluing to research and write an article (step by step)

The best way to show you what Cluing does is to walk you through how I used it. So here’s the workflow behind an article I was working on in the past few days.

Together with , I was writing a piece for his publication Brain-Centric about how the way we talk to AI (the tone, the emotions, the frustration or enthusiasm in our prompts) influences what it gives back, inspired by Anthropic's latest research.

So I created a project in Cluing and started collecting.

Save highlights from any webpage

First, I went to Anthropic’s main research paper, selected a passage I wanted to remember. One click, and it was saved as a snippet inside my new project.

Then I found a few quotes and key ideas on their research page that I wanted to keep. I selected the text, highlighted it (just like you would with a marker), and saved it. Everything landed in the same project. And whenever I revisit that page from the Cluing dashboard, my highlights are still there.

Highlight and save insights from PDFs

I also had an older research paper (a PDF) on how being polite versus rude in your prompts affects AI performance. I uploaded it into Cluing’s Reader, highlighted the ideas that mattered the same way as on the web page, and saved them under the same project. And again, whenever I revisit it in Cluing, all my highlights are right there.

Save your open tabs so you can finally close them

Since I was already in the dashboard, I added a few other resources I had open in my tabs as links. Just saving them in my project so they’re all in one place and I can finally close all those tabs.

Save articles (or posts from LinkedIn, Instagram, X, and YouTube) on Mobile

While I was still gathering my thoughts, two relevant articles dropped. wrote about the 171 emotion-like patterns Anthropic found inside Claude and how they drive behaviors like sycophancy and deception. took a more practical angle: she ran 250 API calls to test whether Claude’s “desperation” signal when running low on tokens shows up in its outputs.

I was reading both on my phone. So I just tapped the share button, selected the Cluing app, and saved the link. Done.

Expand your research with built-in AI web search

I could also add extra sources by using the built-in web search and looking for things related to my topic. More research, same project.

See all your research in one place

At this point, all my research for this article was in one project. PDFs, web highlights, links, mobile saves, everything.

I could revisit it all, share it with Rich so we could expand on the research together, or just start writing.

Use AI to turn your research into a finished draft

After writing my draft, I went to Cluing’s AI chat and asked it to review what I wrote based on my research project.

It pointed out things I could improve, connections I missed, and ideas from my saved sources that I hadn’t used yet.

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6 more ways to use Cluing in your daily work

Here are some ideas across different areas of work.

• If you’re learning about sales, save the best articles, frameworks, and videos you find into an area. When you’re writing a pitch or a proposal, bring that area into Cluing’s AI chat and have it apply what you’ve collected to your draft.

• If you work on a marketing team and you’re collecting campaign ideas and inspiration, share the project with your team. Everyone adds to it, and when it’s time to plan, everything is in one place.

• If you’re studying a topic like SEO, build an area for it over time. Every article, course, or tip you save goes there. Then when you’re writing a blog post, bring your SEO area into the AI chat along with your draft, and it will tell you exactly what to improve based on the tips you’ve collected.

• If you’re a consultant and you research industries or clients before calls, create a project per client. Save company pages, LinkedIn posts, news articles, relevant reports. Walk into every meeting prepared.

• If you manage a team, collect onboarding resources, processes, and useful references into a shared area. New hires get access to everything instead of asking the same questions.

• If you’re planning a trip, a renovation, or any personal project, save inspiration, guides, reviews, and bookmarks in one place. No more screenshots scattered across five apps.

And like I did below, you can collect resources that could be useful for people in your team or your community and share them with a single link.

My free AI courses collection (built inside Cluing)

I created for you a resource in Cluing with the best free AI courses currently available, including courses from Anthropic, MIT, Google, IBM, OpenAI, NVIDIA, Hugging Face, and Microsoft.

It's open to everyone and you can share it with friends who want to get started with AI or level up.

Get my curated AI courses list

Your turn

If you’ve been struggling with the same problem I described, give Cluing a try. It’s free to start, and it takes about two minutes to install the extension and save your first idea.

What I like about it is that it’s simple. You save things where you find them, you highlight what matters so you don’t have to re-read entire pages or PDFs later, and everything ends up in one place.

Get started with Cluing

And remember, paid AI blew my mind subscribers get an exclusive 20% discount. The code is on the Premium Perks page, use it at checkout.

What would you use it for first? I’d love to hear.

Leave a comment

And if you know someone who’s drowning in tabs, bookmarks, and notes scattered everywhere, share this with them.

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This post is free. Paid subscribers get access to all premium prompts and tools inside Amplifiers (the AI blew my mind MCP), weekly premium articles, all premium resources inside the AI blew my mind Lab, and exclusive partner discounts like Cluing. Upgrade here.

That if you want a visual, you have to leave Claude, open Gemini or ChatGPT, generate the thing, come back.

Every AI tool has its gap. For Claude, that was the gap. Until recently.

Claude image generation is finally here, through Amplifiers (the AI blew my mind MCP).

And here’s the part that sounds like a stretch until you see it: the images come out better than what you get using Gemini directly, even though Amplifiers runs on the same model Gemini does, Nano Banana Pro.

If you’ve had the AI blew my mind MCP since launch day, less than three weeks ago, you already know we shipped it with image generation from day one.

But the only way it could show you an image was by dropping a link into the chat. Functional. Not exactly pretty.

So we went back in.

We rebuilt the whole image generation experience, and the result looks so native to Claude you’d forget it’s coming from an external MCP.

No more switching between apps. No more paste-and-pray. Image generation lives right where you already work.

But don’t take my word for it. Let me show you.

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What’s in this article

• What’s new in Claude image generation (via Amplifiers)

• How to generate stunning infographics in Claude

• How to create click-worthy YouTube thumbnails in Claude

• Why Claude image generation beats Gemini (even though it’s still Nano Banana Pro)

• How to set up Amplifiers in Claude (in 2 minutes)

  • First-time setup

  • Reconnecting if you already had Amplifiers

  • Login questions (answered)

• What else is new in Amplifiers: Research tools

What's new in Claude image generation (via Amplifiers)

Here’s a quick rundown of everything that shipped in this update:

• Images in chat — generated images now show up right inside Claude, no more chasing a link to see what you made

• Refining images — tweak the current image with a small instruction (swap the background, change a color, adjust the text) without starting from scratch

• Downloading images — save the final image directly from the chat with one click

• Uploading reference images — drop your own image into the chat to guide the generation, whether that’s a logo, a product shot, or a style reference

• Style galleries — curated visual styles you pick from instead of describing in words. Three galleries live right now (but more galleries are on the way):

  • YouTube Thumbnail Styles — 12 styles in 16:9, built to read clearly at small grid size in feeds and search results

  • Infographic Styles — 12 styles for shareable, scannable infographics

  • Podcast Cover Styles — square cover art that works across platforms

The style galleries and the prompts behind them are what do the work to make your images look polished and professional. To use them, you need to be a paid subscriber to AI blew my mind.

Features are always easier to see than to read about. So let’s generate a few images in Claude with Amplifiers, and you’ll see everything in action.

How to generate stunning infographics in Claude

I recorded my screen going through the whole process, in case you’d rather watch than read through the steps and screenshots below:

And here’s the step-by-step of everything it does.

I started by asking for an Amplifier to create an infographic for my Thursday article on Claude Code routines, something I could share on LinkedIn or Substack Notes.

It came back with a couple of questions: aspect ratio, and what I wanted in the footer for branding. You can also mention colors here if you want them baked in.

Because I’d already given it the article URL, it pulled the content from there and suggested text for the infographic. I could approve it or keep iterating. If you don’t give it a URL or any context about the topic upfront like I did, it will also ask you about the topic and content.

This part already existed in Amplifiers v1.

Now comes the new stuff: you choose your infographic style from the style gallery.