Product with Attitude

Claude Fable 5.1: What Chinese Coverage Says and English Doesn’t

Karo (Product with Attitude) — Thu, 03 Sep 2026 07:07:26 GMT

TL;DR: Anthropic released Claude Fable 5.1 and Claude Mythos 5.1 on September 1, 2026: one underlying model with two safeguard regimes. Fable 5.1 cuts cache-read pricing by 75%, to $0.25 per million tokens, but maximum-effort workloads can still cost more because the model produces more tokens. Anthropic also tied thinking blocks to the conversation that produced them for new API accounts created after August 31, an anti-distillation measure. I ran 136 Chinese-language queries alongside my English-language sweep. The two searches surfaced different launches: English coverage led with cost and the 212-page system card. Chinese coverage led with 反蒸馏 (anti-distillation), 中转 (relay) pricing in RMB, and comparisons with Kimi K3, GLM-5.3, and DeepSeek rather than GPT-5.6 Sol.

Claude Fable 5.1 and Mythos 5.1: a September 2026 editorial feature examining performance, scientific research, and AI safety. Source: Anthropic.

My newest obsession is comparing how AI is discussed in the two countries shaping its frontier: the US and China.

I want to see what happens when you place the coverage side by side, for three reasons.

First, a model release may be a technical event, but the coverage around it is often cultural. It shows what each country celebrates and fears.

Second, this experiment has made me appreciate my years at Xiamen University all over again.

Third, we gave AI the ability to translate the world, so let’s not use it to only read English.

Today, I’ll show you how the same Fable 5.1 release became two different stories.

I’m Karo Zieminski, AI PM and builder. I write Product with Attitude, an AI newsletter for tens of thousands of readers across 146 countries, helping them develop critical AI literacy the only way it sticks: through practice.

Welcome to the 399 new readers who joined since last week. Here’s what you might have missed:

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Research method: Between September 1 and 3, 2026, I ran 136 Chinese-language queries alongside an English-language sweep across Perplexity, Baidu, Google Search, and ChatGPT. The queries covered release information and benchmarks, pricing and cost, access and availability, anti-distillation and safeguards, comparisons with Chinese models, and the relay and account market. This is a comparison of the coverage my searches surfaced. It shows editorial framing, not a national state of mind.

The short version:

This comparison table shows how English-language and Chinese-language coverage framed the Fable 5.1 release differently: English sources emphasized pricing, benchmarks, and safety disclosures, while Chinese sources focused on developer access, relay costs, anti-distillation safeguards, and Anthropic’s locked reasoning blocks.

Both language spheres had the same 212-page system card. They picked different lead stories from it. In a way, the split is predictable.

English coverage on day one converged on two things. The cost cut, and the safety disclosures. VentureBeat focused on a 75% cost reduction for cache reads and daily.dev on a system card.

Chinese coverage led somewhere else.

TMTPost (钛媒体) was the first source I found that added anti-distillation to the headline, calling it a ‘‘lock’’.

Original: Anthropic发新模型，头条不该是跑分，该是一把「锁
Translation: Anthropic ships a new model; the headline shouldn’t be the benchmark scores, it should be a lock.
— TMT

QbitAI (量子位) led with eight benchmark wins and a cost reduction of up to 45%, then covered the new anti-distillation mechanism in the article:

Original: 8项屠榜，最高降价45%，反蒸馏机制上线
Translation: Fable 5.1 tops eight leaderboards, up to 45% cheaper, anti-distillation mechanism goes live.
— QbitAI

EE Times China (电子工程专辑) skipped the benchmarks entirely and went with "竟然有反蒸馏机制？看不起谁呢", roughly: ‘‘An anti-distillation mechanism, seriously? Who exactly are you looking down on?’’

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What Chinese Developers Are Priced Against

English comparisons ran Fable 5.1 against GPT-5.6 Sol and Opus 5, because that’s the table Anthropic published. Chinese comparisons used a different set. The models their readers can buy.

One Developer wrote this in his tech blog on Tencent:

Original: 大多数的日常任务，使用国产的 Kimi K3 和 GLM-5.3、或者 DeepSeek 就足够了.
Translation: For most daily tasks, Chinese models such as Kimi K3, GLM-5.3, or DeepSeek are good enough
— Programmer Yupi (程序员鱼皮)

It’s not a surprising conclusion, but something I haven’t found in English coverage yet.

What the 75% Cache Cut Buys, and What It Doesn’t

Anthropic's own estimate is about 25% cheaper for typical workloads and up to roughly 45% for highly agentic ones. Cognition confirmed the shape of that from inside a harness: a FrontierCode task on Devin dropped from about $5.00 to $2.68 because over 95% of the tokens in a coding task are cache reads.

Artificial Analysis measured Fable 5.1 at maximum reasoning effort at $3.76 per Intelligence Index task, compared with $3.14 for Fable 5. That is about 20% more. Fable 5.1 produced roughly 1.7 times as many output tokens, including its reasoning tokens. The model can cost less in many normal workloads but more when you let it think at maximum effort.

^{For writers:}^{An “Intelligence Index task” is not one prompt or one finished article. It is a weighted average across}^{Artificial Analysis’s benchmark suite}^{that tests difficult work such as reasoning, coding, tool use, science, and professional tasks. The cost includes input tokens, cache use, reasoning tokens, and answer tokens.}

^{Most headline, editing, and drafting prompts are much smaller. It matters for heavier workflows: deep research, fact-checking, and agents that work through several steps. In these cases, a model that “works harder” can also produce a larger bill.}

Chinese hands-on coverage got there first and got there in RMB.

Programmer Yupi ran three projects on Fable 5.1 and published the report:

Original: 总共花了约 $66，折合人民币将近 470 块. 省下的缓存钱又被额外的 token 给填回去了.
Translation: It cost about $66 total, nearly RMB 470. The money saved on cache gets filled back in by the extra tokens.
— Programmer Yupi (程序员鱼皮)

How Anti-Distillation work in Claude Fable 5.1:

^{Anti-distillation}^{means making an AI model harder to copy.}^{A competitor can query a strong model and then use its answers or reasoning traces to train a smaller “student” model instead of investing time, money, and research in training its own model from scratch. Anti-distillation methods try to prevent this.}

^{Anthropic says that distillation campaigns}^{operate on an industrial scale, using thousands of fake accounts}^.

How it works:

A thinking block carries reasoning from an earlier turn. Developers can use it in a new turn, so Claude can continue the task.
With Fable 5.1, they can do this only if the earlier messages, tools, and system prompt remain unchanged. A bit like a sealed envelope attached to one conversation history.
If a developer changes that history, Fable 5.1 will either reject the request or remove the thinking block and continue without it. Anthropic’s technical explanation

For now, this applies only to new Fable 5.1 API accounts created after August 31, 2026. Existing API accounts are not affected, and neither are Claude.ai, Claude Code, or Claude Cowork. Anthropic says preserved thinking will apply to all users in future model releases.

The Same Model Did Not Enter the Same Market

The English coverage I found asked: How capable is it? How much does it cost? What do the safeguards block?

The Chinese coverage also asked: Can developers access it? What does it cost through the routes available to them? And what, exactly, is Anthropic trying to stop?

Fable 5.1 is available on every Anthropic platform, but those platforms are not available in every market.

China, Hong Kong, and Macau do not appear on Anthropic’s list of supported countries and regions.

From what I learned, there is a whole workaround economy: API relay services and second-hand accounts.

^A^{relay service}^{buys access to a model and resells that access to other users.}

^{None of these routes is a clean substitute for official access. A relay inserts another party between a developer and Anthropic. A bought account can disappear with the next ban. I am describing this market because it shapes how the launch is received,}^not^{recommending it.}

HowToken’s relay directory lists Fable-class prices in Chinese yuan at about ¥7 to one US dollar. It also shows offers with multipliers as low as 0.01×. This means that they charge only 1% of Anthropic’s standard rate.

I found the same relay market on Bilibili, where a video promoted a service at 0.01×.

Then there’s the second-hand market.

On July 20, a someone offered ¥5,000 for Claude accounts created in 2024 and ¥3,000 for accounts created in 2025. The buyer described themselves as a Chinese AI company that was ‘‘numb from having so many accounts banned’’.

None of the examples I shared today can tell us what an entire country thinks. Together, though, they show how the same global release can become a very different product story depending on where it lands.

And part of critical AI literacy is recognising a pattern without assuming it represents millions of individual people.

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Does Pangram Work on Substack? I Ran a 159,002-Word Experiment During Its First Month

Karo (Product with Attitude) — Thu, 27 Aug 2026 06:57:46 GMT

I blind-tested 159,002-word authorship dataset.

I’ve been running a secret experiment on everything I write (and some of what I say in meetings) since announced the roll-out of Pangram on July 21, 2026.

AI-assisted or entirely mine, every piece of writing went into the experiment.

A total of 159,002 words.

38,911 input words (what I typed or said)
120,091 output words (AI responses)

That, by the way, is a discovery on its own. At my current pace, roughly 1.91 million words will pass through my AI tools this year.

The Experiment Setup

The four AI authorship scenarios I tested

I ran the experiment across four points on the human-to-AI authorship spectrum:

AI-generated: The model wrote it.
Human-written: I wrote it.
Human-spoken: I said it.
AI-generated, human-edited: The model wrote it, and then I changed it.

Experiment questions

Can Pangram correctly identify each scenario?

Pangram AI detector experiment setup. I tested five points across the human-to-AI authorship spectrum: fully AI-generated text, human-written text, human-spoken meeting transcripts, AI-generated text after one human tweak, and AI-generated text after five to seven rounds of human editing. For each condition, the experiment asks whether Pangram assigns the expected authorship class correctly.

Tools I used

Substack captures the public version of my writing. The experiment needed the private and spoken versions too, so I tested not only through the Substack-Pangram integration but also directly in Pangram and through the Pangram API.

Limitations

It’s a deep single-author study, not a benchmark of all writers. It shows how Pangram behaves across my language.

I Know You Came for the Results. You’ll Get Them.

But first, let me borrow your attention for three things I think deserve it more.

1. Pangram Incentives

Do you know why students optimize for test scores instead of understanding? Why customer-support teams optimize for tickets closed?

Because that’s what they’re measured on.

That’s classic Goodhart’s law: once a measure becomes a target, people start optimizing for it. That’s just what we do.

So tell me.

What exactly did we think was going to happen when “human-written” became the target?

What I see on Substack after one month of Pangram:

some writers try to protect themselves by making their writing worse.
some are even advising each other to add in a few typos so their work looks “less polished and more human.” (It doesn’t work, by the way.)

We started with “protect readers from slop” and somehow arrived at “please add slop so you look human.” Incredible journey.

2. Pangram Myths

Now let’s deal with the claim that Pangram will protect us from slop.

It won’t.

Pangram’s technology cannot do that for one very simple reason: it doesn’t evaluate writing quality.

I said it in the AI-Assisted Craft Manifesto, and I’m nowhere near done saying it. Pangram can answer one narrow question: Does this text resemble patterns associated with AI-generated writing?

That’s it.

3. Pangram Paternalism

I’m also unconvinced that I’d want this kind of protection. Just like I don’t want my aunt deciding that I shouldn’t order a dish because she didn’t like it. Bring me the weird fermented thing.

Plus, like you, I already have a solid moderation system.

Reading.

If I don’t like the writing, I stop reading. If I do, I keep going. Brilliantly simple. Never failed me.

I don’t need seatbelts on my sofa.

Experiment Results

After 159,002 words, I have reached the least exciting scientific conclusion possible:

It’s complicated.

Pangram works. Pangram also doesn’t work.

Scenario 1: AI-generated outputs

Pangram was right 100% of the time. If I had stopped here, I could have declared that Pangram works perfectly. I’m glad I didn’t.

Scenario 2: Human-written

Pangram was right 21% of the time. It misclassified 79% of my human writing as AI.

Scenario 3: Human-spoken

Pangram correctly recognized 78% of the meeting transcripts as human. Which raises an important scientific question: what exactly was I during the other 22%?

Scenario 4: AI-generated, human-edited

Pangram was right 98% of the time after one tweak. Good.

But it was wrong 93% of the time after 5-7 rounds of tweaks.

Did the text become more human? And if it did, what exactly does human mean here? AI-generated, then edited 5-7 times?

Pangram AI detection experiment results. Across the tested samples, Pangram detected pure AI text with 100% accuracy but correctly classified my human-written text only 21% of the time. Human-spoken transcripts reached 78% accuracy. For AI-origin text, detection fell from 98% after one human edit to just 7% after five to seven rounds of editing, a 91-percentage-point drop. These percentages describe this experiment’s samples, not Pangram’s overall statistical accuracy.

Experiment Conclusion

Pangram appears much better at detecting untouched model output than tracing authorship after substantial human intervention.

One additional finding came out of this experiment. Pangram on Substack and Pangram’s native tool sometimes gave different results for exactly the same text. Fine-tuning is one possible explanation. Different thresholds, or text handling could also explain it. I can't tell which from this experiment.

But I can tell you this: Saying that Pangram doesn't work is not accurate. Saying that Pangram works is not accurate either.

And most importantly, if anything, this experiment proved that READING is still the best way to evaluate whether something is worth reading. As my grandma used to say, if you taste the soup and still can’t tell if it’s any good, we have a bigger problem than the soup.

Hey, I’m Karo Zieminski 🤗.

AI PM and builder. I write Product with Attitude, an AI newsletter for tens of thousands of readers across 146 countries, helping them develop critical AI literacy the only way it sticks: through practice.

If you’re new here, welcome! Here’s what you might have missed:

Subscribe for free, and I’ll keep bringing you deeply researched,
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Kimi K3: I Designed One Newsletter Hero in 11 Stunning Ways

Karo (Product with Attitude) — Tue, 25 Aug 2026 06:19:55 GMT

11 ways to design the same newsletter hero. Built with Kimi K3 from essentially the same UX copy and subscription structure. Read the article to learn how to copy and adapt every asset, prompt, and codebase.

After my Claude Design review, four PwA members have independently asked about design styles that don’t look blatantly AI-ish.

Or, more precisely: how to brief AI when the visual is clear in your head but not on your tongue.

I’ll know it when I see it, I just don’t have the words to describe what I really want.
—

I can picture exactly how I want it to look, but I struggle to translate that vision. I eventually adapted an existing design, and while the result is fine, it doesn’t feel like something I’m particularly proud of.
— PwA Reader

We usually learn to like before we learn to explain why. And that's okay.

Don’t ever let it make you feel like you’re not ready to start experimenting. None of us were born with a design vocabulary.

We are not covering centuries of visual design in one Substack post. Instead: hero sections with prompts and code, plus a workflow for translating every possible I’ll know it when I see it into design direction for your AI.

We’ll do that 11 times, using the exact same website in 11 different design styles.

You’ll also enjoy this article if you’re curious what Kimi K3 can do while the internet continues its Claude-and-GPT hype cycle.

Or, if your brain likes looking at beautiful things.

AI-ish Design and Claude-ish Design

AI-ish design (also: Claude-ish design) is the readily identifiable default aesthetic of interfaces generated by AI coding tools without clear human art direction.

Side-by-side comparison of two AI-ish newsletter landing page designs showing the recognizable default aesthetic of AI-generated websites.

Some telltales: the indigo-to-purple gradient style that Claude is terribly fond of. Identically rounded cards with soft shadows. Badge pills everywhere. Tailwind, Tailwind, Tailwind.

But I think that definition misses its most important tell: we look at it and feel nothing. Annoyance, maybe. But mostly nothing.

I saw far more indigo websites in 2025 than I do now. So: progress. But plenty still leave you with that same blank feeling.

What Changes When YOU Direct it

The table below shows what changes when we guard our taste like a hawk:

AI-assisted craft vs AI model defaults comparison table: 13 dimensions from typography to feeling, and why intentional art direction wins.

Why I Chose Kimi K3 as the Model For this Experiment

Because Kimi K3 is shockingly good at design even before you give it a single Skill. That’s because it can see its own work.

Kimi K3 arrived in July 2026 from Moonshot AI: 2.8 trillion parameters, open weights.

It runs an agentic visual loop. Which basically means it iterates between code and live screenshots. On July 17, that capability helped put it at #1 on Arena’s Frontend Code Arena, ahead of Claude Fable 5.

Yes. Fable 5 got beaten at frontend design by a Chinese open-weight model.

July 2026 Arena Frontend Code leaderboard snapshot showing Kimi K3 at #1, ahead of Claude Fable 5, GPT-5.6 Sol and GLM-5.2.

Hey, I’m Karo Zieminski 🤗.

Welcome to the 399 of you who have joined us since last week! Here’s what you might have missed:

Subscribe for free, and I’ll keep bringing you deeply researched,
human-written articles like this.

And if you share this article with 3 friends or colleagues,
I’ll thank you with 3 months of a premium subscription.

Refer a friend

The Experiment: Kimi K3, the Same Words, 11 Hero Sections

Eleven hero sections, one newsletter website, identical copy and subscription structure, built 11 times in 11 different design styles. I wanted the visuals (and textures!), not the words, to do all the talking.

Eleven visual directions for designing the same newsletter hero section with AI, from Conceptual Editorial Minimalism and Punk Editorial Collage to Botanical Luxury Minimalism and Alpine Cinematic Editorial. Each design style is defined by a distinct concept, typography treatment, image language, texture, composition, and color palette. Proof that better AI website design starts with specific art direction.

Design Style 1: Conceptual Editorial Minimalism

Conceptual Editorial Minimalism. Combines one surreal photographic idea with disciplined typography, extreme negative space, and a restrained black, cream, and yellow palette. Built with Kimi K3, the hero uses a black cat seemingly emerging through a frosted surface as the focal concept, pairing Swiss-inspired editorial structure with contemporary art direction.

Copy assets, prompt, and code

^{Find the password here.}

Design Style 2: Nocturne Editorial Surrealism

Nocturne Editorial Surrealism combines luminous natural-history illustration with dark luxury minimalism and cinematic editorial composition. An oversized glowing hummingbird, near-black negative space, peach serif accents, and restrained UI combine to create a website that feels like a nocturnal museum plate turned into a contemporary digital publication. Built with Kimi K3.

Copy assets, prompt, and code

^{Find the password here.}

Design Style 3: Cinematic Frosted-Glass Editorial

Cinematic Frosted-Glass Editorial combines conceptual portrait photography, condensation textures, oversized typography, and atmospheric minimalism. The hero reveals only the subject’s eyes through a strip of frosted glass, turning concealment itself into the composition while keeping the surrounding newsletter UI deliberately restrained. Built with Kimi K3.

3 Steps to Direct Your Next Redesign: How to Build Stunning Websites When Your Taste Is Clear but Your Vocabulary Isn’t

Visual Prompt Engineering

Visual prompt engineering is the practice of pairing images with written instructions to give AI clearer context and guide its output.

The visual prompt engineering workflow: find a design reference, save it while you still remember why it caught your eye, then pair it with written instructions in your AI design or coding tool.

I recommend reading Figma’s Visual Prompt Engineering Guide.

First rule: if the design vocabulary isn’t coming, don’t force it. Switch to visual language instead.

The visual reference-to-prompt workflow turns an 'I'll know it when I see it' into design direction your AI can execute.

Browse sites like Cosmos or Behance and scroll until you see something that, well, stops the scroll. Save it for later. I mean it, I’ve made this mistake often enough to know better: save that thing immediately (that’s rule number 2). You can use tools like Cluing, bookmark it, anything.

If one of the saves feels close to what you want for your homepage, use it as a visual reference in the prompt. I’ve seen people paste it into ChatGPT first and asking it to describe the design in precise language. They were happy with the results. But in my tests, giving Kimi K3 the image AND the prompt worked even better.

Rule #3: prepare your UX copy (the words people see on your site) before you prompt.

Using today’s example, this is the copy I gave Kimi K3:

UX copy specification table for a newsletter hero section, showing the exact wordmark, kicker, headline, subhead, email field, subscribe CTA, microcopy, and credibility stats used in the design.

Now hand it over to your AI. Just don’t paste in one of those giant “ultimate web design prompts” from the internet. Instead, ask the agent to interview you, one question at a time, until it has enough context to understand what you want. Until you both do.

You are a team of senior product designers, art directors, UX experts, and design engineers. Interview me one question at a time until you are at least 95% confident you understand my requirements.

That’s AI-assisted craft in practice: let the model do the assembly work, but keep the standard. (And please read the manifesto!)

If you choose to use Kimi K3, I accessed it via Perplexity Computer. Here are some other options:

5 ways to access Kimi K3 in 2026. A comparison table with Kimi K3 access routes: Perplexity Pro and Max, Kimi.com membership credits, the official Moonshot/Kimi API or OpenRouter, Kimi Code integrations for Claude Code, OpenCode, Codex and Roo Code, and self-hosting the 2.8T-parameter open weights from Hugging Face.

I hope this helps!

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This Second Brain Will Change How You Write Your Newsletter: Cluing Review + AI Agents Preview

Karo (Product with Attitude) — Wed, 19 Aug 2026 12:04:21 GMT

TL;DR: Cluing is an AI second brain that saves what you read, links it in a knowledge graph, and feeds it to Claude, ChatGPT, Codex, and Perplexity Computer over MCP. It started life as Collabwriting and carries a 4.9 rating from nine Product Hunt reviews. Pricing runs $0 on the free tier, $12/month on Professional billed yearly, $120/month on Superhuman. Below: nine workflows I kept, two custom agents, one skill, and a 20-30% discount for PwA readers.

I’m preparing for an AI Architect exam, which means I spend several hours a week asking AI to teach me how to AI, then searching the web for more AI, so I can pass an exam about AI.

A few weeks back, I was doing exactly that. Things were going well until I searched for a page and discovered I already had it open.

Twice.

Around the same time, from Cluing reached out and asked me to test their newest features. I already knew about Cluing and had been meaning to try it, so this was cosmically super well-timed.

A, I needed help. And B, I’m obsessed with second brains. I’ve written about them, built them, and tested them.

Which is how I discovered, that for a second brain to be useful long term, it needs to self-maintain with minimal upkeep from me, and connect to everything else I use. Cluing does exactly that. I know, because I tested it for three weeks before writing this article.

What’s Inside

What Is Cluing? An AI Second Brain for All Your AI Tools
What Cluing costs in 2026
My PM read: Additional notes from a PM point of view
Get Cluing running in three minutes across all your AI tools

What Is Cluing? An AI Second Brain for All Your AI Tools

^{Today’s article (and discount for my readers) is sponsored by}^Cluing^{.
My opinion isn’t.}^{Learn how I evaluate sponsorship requests}^.

Cluing is a research and knowledge management platform that saves what you find, organizes and visualizes it for you, and turns it into context for the AI tools you already use.

It sits in the Personal Knowledge Management category, alongside tools like Mem, Tana, Notion, and Obsidian.

The big difference: Cluing is much easier to use. You don’t need to understand markdown or be an AI expert to make any of this work. I’ll show you exactly how below.

Who is Cluing for?

If you research first and create second, Cluing makes sense. That includes the obvious suspects (consultants, students, journalists, marketers, researchers), but also artists, chefs, curators, screenwriters, trend forecasters, and investigators. Teams can work together inside it too.

And for Substack writers, it can support the entire newsletter workflow: idea, research, draft, publish, repurpose.

How much does Cluing cost?

Cluing costs $0 on the free tier, $42 per month on Pro. Teams start at $135 per month. These are the new pricing plans starting from today (19 August 2026).

Cluing pricing: the Free plan covers personal knowledge capture, while Pro adds frontier AI models, unlimited clusters, deep research, and shared memory. Team adds collaboration, guest access, roles, and model controls.

Paid PwA subscribers get 30% off Cluing during the launch week (until 26.08.2026) and 20% off afterwards.
Grab the code from the Discounts page and use it at checkout.

Cluing vs Obsidian, Notion, Mem, and Recall

Hey, I’m Karo Zieminski 🤗.

Welcome to the 514 of you who have joined us since last week! Here’s what you might have missed:

IMPORTANT:

Paid members get an exclusive 30% off Cluing during the launch week (until 26.08.2026) and 20% off afterwards. The code is here, use it at checkout.
I also have 10 offers for 2 months of LinkedIn Premium to give away. The next 10 people who upgrade to paid will get one, on top of all other discounts. Upgrade to a paid membership, then send me a DM asking for the LinkedIn Premium link. First come, first served, so be quick.

Subscribe and get your gifts

And if you share this article with 3 friends or colleagues,
I’ll thank you with 3 months of a premium subscription.

Refer a friend

All The Things I Did with Cluing

4. I made newsletters sort themselves before I read them

I don’t read newsletters the proper way (as in, when they come out). I read them in obsession chunks.

So I gave each obsession its own Cluing inbox: Critical AI Literacy, Logic, Claude, Perplexity, you name it. Every time I see something interesting in my inbox, I forward it to the relevant email address. Newsletters wait there until my brain decides we’re no longer casually interested. We’re researching.

And when that happens, the newsletter snippets I curated are ready to go.

My PM read:

This way, the benefits compound almost invisibly. Something I saved six months ago can suddenly become useful because of something I added today.

But saving the source is only the beginning. Below is the system: two agent builds, one scheduled Cowork task, the testimonial prompt, and the reader-question skill.

Part 2: Putting Cluing Context to Work

5. I built custom AI Agents that work directly in Cluing.

Cluing agents run inside Cluing, learn from the topics you feed them, and update their own instructions when the underlying source changes.

This feature is brand new.

Agents can do pretty much anything agents in Claude Code can, but you can build them inside Cluing, without the need to switch context.

Every tool that self-learns earns my attention right away. And Cluing agents are dynamic, they learn. Let’s say you have a skill that helps you with Substack SEO. As soon as Google publishes new guidance, you can add it to the topic and the SEO agent updates itself for future tasks.

I built a GA4 analyst agent. It patrols my Google Analytics data. I gave it an actual investigative method (from finding the baseline, to dissecting the deviations). If you’d like to build your own Cluing Agent, I recommend you read this article first.

Paid members: copy my complete Google Analytics 4 Cluing Agent instructions + daily analysis prompt and customize the metrics, thresholds, property, reporting cadence, and destination for your own site.

Copy my GA4 Cluing Agent Instructions

6. I scheduled a Cowork task that builds a weekly shortlist of content ideas from my saves.

What it does each week: reads my new Blog Ideas topic in Cluing, keeps only snippets created in the last 7 days, picks the 5 strongest, emails them to me, and archives the whole thing as a snippet in Weekly Content Shortlists. Every email closes with a line reminding me that I can ask Claude to pull up previous shortlists, with a link to the archive.

7. I turned saved customer testimonials into website CTAs

Testimonials are customer-language datasets.

Back in 2014, Joanna Wiebe from Copyhackers published a rather spectacular example. While writing copy for Beachway, an addiction treatment center, she mined 500+ Amazon reviews of books about addiction for the language people actually used. One phrase became the homepage headline. Tested against the original, it drove 400%+ more CTA clicks and 20%+ more lead submissions.

I used the same underlying method on my own website. If you’d like to squeeze more value out of your testimonials, connect Claude to Cluing MCP, and run this prompt in Cowork. You’ll get suggestions for headlines, section headers, button text, and CTAs.

Side note: For something more playful, try my subscribe button that runs away from your visitors. Code is here.

8. I built a Cluing Skill that turns reader questions into post ideas

I get hundreds of comments and DMs every week. I save them in Cluing, then run this skill weekly.

It scans the reader questions I’ve saved in Cluing from Substack, spots patterns, then turns them into newsletter ideas worth writing.

Paid members can skip the setup and simply copy my Skill to mine real reader language for newsletter ideas, Substack Notes, FAQs, and conversion opportunities.

Copy Skill

A few more Cluing Skill and Agent ideas to try next:

9. I wrote this article in Cluing

I used Cluing to write the article you’re reading now. I researched, sourced, and outlined inside Cluing, then wrote the piece myself. My usual process needed minimal adjustments.

My research-to-Substack workflow in Cluing

Once the article is done, Cluing finds new ways to reuse the same material through links, quotes, and Notes. The snippets from research get more than one life.

Final PM Notes About Cluing

Additional things I appreciated

Cluing places tiny bits of UX copy exactly where my brain is about to come up with a problem: “Creating this agent adds nothing to your bill.’’

What could be better:

I wish I could password-restrict links I share. That would turn Cluing into an ideal tool for paid subscriber resources. In fact, if Cluing ever builds this feature, I’d be interested in white-labeling the tool as an extension for my website.

The good thing is that users can request new features directly from the Cluing team.

Try Cluing Today (Discount for PwA Readers)

Starting with Cluing takes less than a minute: register, install the extension, save your first idea. After 2-3 days of saving things as I found them, I started doing it subconsciously. I barely notice the habit now, but the research keeps compounding.

In a way, my second brain is learning faster than I am. It remembers where everything is, and why I saved it, so my first brain doesn’t have to.

Try Cluing for yourself

Reminder: Paid members get an exclusive 30% off Cluing during the launch week (until 26.08.2026) and 20% off afterwards. The code is here, use it at checkout.

And if you’re in a builder mood, support on Product Hunt. I did.

WHY SUBSCRIBE ・YOUR PERKS・CRITICAL AI LITERACY・TOOLS

CLAUDE HUB・PERPLEXITY HUB ・VIBE CODING HUB

LinkSwap・StackShelf ・Attitude Vault

Discounts and Access Codes for Premium Members (Q3 2026)

Karo (Product with Attitude) — Wed, 19 Aug 2026 06:23:41 GMT

Updated August 2026 · Actively maintained

TL;DR: Product with Attitude is the only newsletter discount bundle that combines AI builder tools, community‑made tools, and curated newsletter discounts for $10/mo instead of inflated enterprise pricing. Every discount is for a hands‑on‑tested product (not just a paid partnership) or a tool built by PwA community members, backed by editorial deep‑dives.

I’ve been stacking value until Premium membership is a no-brainer and not subscribing means leaving money on the table.

This discount page is just one of my Substack premium membership perks.

Over time, the initial humble discounts page evolved into something bigger than a simple list of deals.

It became a bridge:

between the tools I use/test/build and the people who need them,
between what our members build and the people it’s built for,
between the newsletters I recommend and the readers who’d love them.

First, the Math Behind the Discounts

I’m regularly contacted by brands and tool providers with offers, and I make sure my community members get access to the useful ones.

I decided to translate those credits into equivalent subscription value to see what they’re actually worth.

Your PwA membership pays for itself, repeatedly.

At $9.99/mo, a PwA membership delivers $918.70 in first-year tool value. That’s $76.56/mo in real, usable discounts and credits across the tools you’re already using: recurring software discounts, one-time credits, community tools, and newsletter deals.

You’re getting a 7x return on every dollar you spend, starting month one.

Upgrade and claim the discounts

AI Tool Discounts 2026:
Tools I Use Or Have Tested Hands-on

For the full list of links and discount codes, scroll to the bottom section of this page (paid members only).

In random order:

Recraft AI

🎨 AI design platform for creating and editing illustrations, icons, and graphics.
Your PwA perk: 200 free credits plus my guide to Recraft.

Cluing

A research and knowledge management platform that saves what you find, organizes and visualizes it for you, and turns it into context for the AI tools you already use.

Your PwA perks:

Launch week discount: 30% off

After launch week: 20% off
My review and guide to Cluing and making it work with Claude, ChatGPT, Gemini, and more.

Replit

🛠 The go-to vibe coding tool to quickly spin up, edit, and ship apps. One of the most popular vibe coding tools in the builder community.
Your PwA perk: $10 in credits.

Magic Patterns

🎨 AI design tool for generating product-ready UI layouts and components.
Your PwA perk: 100 free credits.

Manus AI

🤖 AI agent that can do most of what Claude and Perplexity can.
Your PwA perk: 500 credits.

Ovren

🛠 Connect your project. Assign tasks. Your AI engineers read the codebase and deliver production-ready updates. #4 Product of the Day on Product Hunt on 15 April 2026.
Your PwA perk: 50% discount for new users.

Lovable

🛠 AI app builder that turns natural-language prompts into working web apps.
Your PwA perk: 10 free credits.

Atoms

🛠 AI-first coding environment where you build software through conversation.
Your PwA perk: 10 free credits plus my deep-dive on Atoms.

Granola AI

📝 AI notetaker that records calls and delivers tight, searchable summaries. Free trial available.
Your PwA perk: 100% off your first month.

Cal.com

📅 Scheduling platform that removes the back-and-forth from booking meetings.
Your PwA perk: 20% off for 12 months.

Tally

📝 Lightweight form builder for surveys, feedback, and lead capture. I use it for all my surveys.
Your PwA perk: 50% off for 30 months on any plan. This is the single most valuable deal on this page.

Wispr Flow

🎙 Voice-first AI workspace for capturing ideas and workflows hands-free.
Your PwA perk: Free month of Pro access.

Tella.tv

📹 Video tool for fast, polished screen-and-camera walkthroughs and demos.
Your PwA perk: 20% off for 3 months.

Best AI Tools For Substack Writers.
Built By The PwA Community Members.

For the full list of links and discount codes, scroll to the bottom section of this page (paid members only).

StackContacts

Created by a fellow builder , it’s the only CRM I use for managing my subscribers.
Your PwA perk: 20% off.

CarouselBot

Created by a fellow builder . Paste a link. Get a week of visual content for social media.
Your PwA perk: 40% off for monthly and annual options.

Subflow AI

Created by a fellow builder , Subflow AI has been making waves on Substack recently, and many PwA members are already using it.
Your PwA perk: 10% off.

Writestack

Created by a fellow builder, , who I’ve had on PwA before as a guest writer, Writestack is one of the most popular scheduling and analytics tools for Substack writers.
Your PwA perk: 10% off.

Drippery

Created by a fellow builder , Drippery helps authors turn their best content into automated drip email series that onboard, nurture, and sell to new subscribers on autopilot.
Your PwA perk: 14 days of Pro access, completely free.

PwA Design Resource Library

Scroll down for access code.

Every Product I’ve Launched,
Plus Everything I’ll Build Next

Every product I launch is included in your premium membership. Past, present, and in progress. You use them from day one.

LinkSwap

Trust-based backlink swaps with other writers. Three minutes a week, real SEO value. Backlinks still matter in 2026 - they help search engines build topic authority and increase the odds your work surfaces in AI-generated answers.
🔗 linkswap.productwithattitude.com

StackShelf

A discovery platform for Substack creators. One link, everything you build, in one place.
🔗 stackshelf.app

Vault

AI workflows, coding prompts, and automation templates, not a generic prompt dump. Every resource is built by community members who use them in their own work. Named authors. Clear descriptions of what each one solves.
🔗 vault.productwithattitude.com

Exclusive Substack Newsletter Deals. Newsletters I Personally Recommend.

These are newsletters I read every week, trust deeply, recommend constantly, and always learn from.

From design to cybersecurity, from quantum computing to leadership and product building, there are so many aspects of AI we can learn from each other.

Prompt-Led Product by

Newsletter on using AI as a core product capability, from an AI product leader.
Your PwA perk: 50% off an annual subscription.

Slow AI by

Newsletter on critical AI literacy.
Your PwA perk: 20% off an annual subscription.

Robots Ate My Homework by

Newsletter on using AI to think sharper.
Your PwA perk: 30% off forever.

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Newsletter for designers, product managers, builders, and founders navigating the future of digital products and experiences.
Your PwA perk: 50% off forever.

Into AI by

Newsletter on AI and Quantum Computing.
Your PwA perk: 30% off an annual subscription

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Newsletter on leading teams through AI-era change. Premium members get 60% off an annual subscription.

Lifelong Learning Club by

Newsletter on translating learning science into strategies to remember more and use AI to augment cognition.
Your PwA perk: 25% off an annual subscription

AI Meets Girlboss by

Newsletter for turning AI into a cohesive, branded growth engine.
Your PwA perk: 30% off an annual subscription

Wondering About AI by

Newsletter for AI builders and creator of CarouselBot, a tool that turns your posts into social media carousels.
Your PwA perk: 25% off an annual subscription.

AI blew my mind by

Newsletter on learning to use AI to advance in your career, grow your business, or become irreplaceable in your role.
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Build to Launch by

Newsletter for shipping-focused builders who want concrete launches, not just ideas.
Your PwA perk: 20% off forever.

ToxSec by

Cybersecurity breakdowns from an expert who’s seen it from all sides: Amazon, NSA, defense contracting, and the USMC.
Your PwA perk: 30% off an annual subscription.

New to PwA? Start with these 3 tools. Your membership pays for itself on just these: Tally (50% off for 30 months), Granola (100% off your first month), and LinkSwap (included with Premium).

Here are the links and codes for everything listed above

I Build AI Products for a Living. Here Are 5 Things All Model Comparisons Miss.

Karo (Product with Attitude) — Tue, 11 Aug 2026 12:08:33 GMT

This is perhaps not the finest evidence of my fiscal restraint: I pay separately for ChatGPT, Claude, and Perplexity (roughly $320 in subscriptions a month).

Yes, I know there’s overlap.

Yes, I know I can access some of these models through Perplexity.

But for me it makes perfect sense. I pay for the intelligence underneath and the product experience sitting on top.

By this point I’ve read so many comparison articles that I’ve grown tired of rankings that miss one important thing: the product is more than just a model.

The product surrounding it is there for a reason. To build it, entire teams made different bets about who we are, and we expect AI to handle. And part of critical AI literacy is understanding these mechanisms.

Whenever someone asks me which model is best, I give the same answer: it depends. On the job. On your taste. And on how you access it (chat interface or the API?).

After The Builder-Parent Paradox, many of you started asking me something new: what assumptions are AI companies making about us, the users? ChatGPT Work, Claude Cowork, and Perplexity Computer share plenty of capabilities, but they start from different assumptions about how we work.

Today we’re going to dig into those assumptions through what I call perceived AI intelligence.

Perceived AI intelligence is the capability we attribute to the model after the surrounding product has supplied context, selected tools, organized the work and presented the result.

What’s Inside

This piece runs on two sources of truth: prolonged firsthand use and reading the official documentation. Both taken well past the point of casual curiosity.
I’ve also thrown in a few product development theories. Fun ones. Specifically, I’m looking at five assumptions these products make about us:

Happy reading.

Hey, I’m Karo Zieminski 🤗.

AI PM and builder. I write Product with Attitude, an AI newsletter for tens of thousands of readers across 146 countries, helping them develop critical AI literacy the only way it sticks: through practice.

Welcome to the 818 of you who have joined us since last week! Here’s what you might have missed:

AI-assisted Craft: A Manifesto

Subscribe for free, and I’ll keep bringing you deeply researched,
human-written stories like this.

Subscribe for free

And if you share this article with 3 friends or colleagues,
I’ll thank you with 3 months of a premium subscription.

Get 3 months of Premium free

What the Frontier AI Products Assume About Us, the Users

#1 We want to be understood

ChatGPT, the product, does something I’ve grown rather fond of: it learns how lazy I intend to become. My ideal AI workflow is basically: I explain myself properly once, become progressively less articulate, and expect the machine to keep up. Let me show you.

At turn one I use a full sentence: Change the background on this infographic to #f7f7f8.

Then, 2-3 rounds later: Background to #f7f7f8.

Then, 3-5 rounds later: #f7f7f8.

That’s barely communication, yet ChatGPT correctly infers my intent from the conversation. It follows shorthand I never turned into an official command.

That experience depends on continuity, and ChatGPT does not own it. Perplexity does it too (very well). Claude Cowork does it too. What differs is how we get to experience it.

My PM read

And there it is: the reason I keep objecting to comparisons that stop at the model layer.
The team built far more around it. Conversation history. Custom instructions. Memory, context, apps, tools, task state and all that backstage jazz. Yes, we can recreate the immediate context through the API (so shorthand would still work). But we don’t automatically get the rest. We either lose those layers or need to rebuild them ourselves.

AI for Crime Junkies: When ChatGPT Prompts Become Evidence

Karo (Product with Attitude) — Tue, 04 Aug 2026 11:45:29 GMT

I need to give you a heads-up. This story, describing the documented human-rights crisis inside Cambodia’s scam compounds, contains descriptions of human trafficking and abuse, including abuse of children. Please read with care or skip reading.

The story I have for you today is unlike anything I’ve ever covered. It’s also unlike any crime podcast I’ve listened to. Because usually, I can point at the map and say: here’s our victim, and here’s our perpetrator. Two dots. A line between them. This story has thousands of dots. Some of the dots are victims AND perpetrators, at the same time.

I’ll explain. But first I need you to meet someone.

Part 1: AI, the Villain?

The Social Ad

I need you to imagine a 27-year-old man. We’ll call him Adi. Professional, Jakarta-based.

One Friday night in 2025, he’s horizontal in bed, thumbing through Instagram, when he sees an ad. It’s good. Excellent, actually. A yacht deck glazed in sunset. A beautiful couple, roughly his age, laughing over dinner. The ad is for something called Klub Romantis: a luxury dating service.

So Adi clicks. A receptionist greets him in the chat: warm, unhurried. They type for a while and he has her undivided attention. The rarest commodity on the internet. She asks what kind of woman he’s looking for, what kind of relationship he wants. Then she sends him to Telegram with a special invite code. It feels exclusive.

Fact number one, and I need you to hold onto it for the rest of this story: that receptionist Adi is falling for is not a woman. She is not even a person. She’s an AI persona.

In OpenAI’s threat intelligence report in February 2026, its investigators called this exact scam Operation “Date Bait”. The scammers had used ChatGPT to generate the ads, design the Klub Romantis logo, even generate images of the fake women. The “receptionist” was a bot instructed to be flirtatious and route men to Telegram. OpenAI estimated this one operation was likely defrauding hundreds of victims a month.

Hundreds. A month.

The Funnel

Once Adi’s on Telegram, the machine hands him back to humans.

Ish.

They’re copy-pasting his messages into ChatGPT and pasting the answers back.

The investigators found the whole operation had an org chart like a sales department. There was a “Lead Generation” team, a receptionist, a “Supervisor Team.” The reception persona keeps things romantic (increasingly explicit, actually) and then introduces Adi to the platforms. One is called “LoveCode.” I’m not going to share the name of the other one, but trust me, subtlety was not the point.

And on LoveCode, there’s this scrolling feed of announcements. Congratulations to this member for completing a mission! Congratulations to that member for unlocking a bonus! All fake.

Adi gets handed to a “mentor.” And the mentor assigns daily tasks. Support your chosen girl in a competition. Pay a hotel deposit for your meet-up. Every task costs a tiny bit more than the last. Every nudge is generated and translated by ChatGPT; emotionally manipulative on purpose, in fluent Indonesian.

So he keeps paying. He’s already invested. But mostly because, by now, he’s in love, or something that feels enough like it.

Hey, I’m Karo Zieminski 🤗.

If you’re new here, welcome! Here’s what you might have missed:

Subscribe for free, and I’ll keep bringing you deeply researched,
human-written stories like this.

And if you share this article with 3 friends or colleagues,
I’ll thank you with 3 months of a premium subscription.

Refer a friend

The Kill Value

This is where I have to tell you the word that made me put my laptop down and walk around my house. Inside the operation, the final extraction has a name. The moment the scammers take everything they think they can get. They call it the “kill.”

Every day, ChatGPT helped the network produce a status report. Each victim had a row, an assigned staff member, and a number in Column C. They called it the “kill value”: an estimate of how much money they thought they could drain from him before they blocked him. Because eventually, they would. Until then, his kill value is updated daily in a process that got operationalized using ChatGPT.

In the report there’s also a copy of an “official” letter one of the scammers pasted into ChatGPT, telling a victim he needed to pay twenty million five hundred thousand rupiah, about $1,250, to fix a fictitious “data processing error” in the LoveCode system. And don’t worry, after he pays, he’ll get a 35% bonus back.

Which never comes. Per the scammers’ own logs: once the victim sends the maximum possible amount, they block him on Telegram and mark the case closed.

A fraudulent Love Code Entertainment “data correction” letter demands Rp 20.5 million to repair a nonexistent processing error and promises the victim a 35% bonus. The scammers shared the letter with ChatGPT; OpenAI investigators later recovered and redacted it.

^{Side note:}^{OpenAI’s report}^{seems to contain an error. It prints “$12,000,” but Rp20.5 million worked out to roughly $1,247 using the official March 11, 2025 exchange rates. That’s a tenfold conversion error.}^{OpenAI report}^,^{official EU exchange rates}^.

I learned that investigators have a name for this type of scam: pig-butchering, from the Chinese phrase sha zhu pan (杀猪盘). A pig-butchering scam fattens a victim with weeks of fake romance and rewards before the slaughter, the final extraction of everything they have. I wish I could tell you the metaphor felt excessive by this point in the report.

The Question

At this point, you’re probably starting to imagine the people on the other side of those keyboards. Some criminal mastermind. But let me show you one detail from OpenAI’s report, before you give them that much credit. At one point, one of these workers asked ChatGPT for tax advice. And when the chatbot asked for their occupation, they typed: “scammer.”

To investigators, who by now traced this operation to Cambodia, this was a clear sign that the operators were likely not the people in charge. They also noticed that ChatGPT was being used to translate messages between Chinese-speaking supervisors and Indonesian-speaking scam center workers. Supervisors and the workers didn’t speak the same language, which begs the question why. Why would you take ‘‘scale’’ an operation internationally, just to hire a few teams copying and pasting words from ChatGPT.

That question — why don’t they speak the same language? — is the door into the second half of this story.

There is a very real chance Adi’s supervisor was kidnapped.

The Workers

Many of the workers were recruited from other countries. And a staggering number of them, according to the United Nations and Amnesty International, did not choose to be there.

So how does that happen?

It starts with a job ad. The same way Adi got a too-good-to-be-true ad about luxury dating, workers get a too-good-to-be-true ad about a job. Customer service. IT. Marketing. Great salary, free accommodation, flight included. The UN Human Rights Office and Amnesty have documented this recruitment pattern over and over, and OpenAI has separately banned Cambodia-linked accounts generating exactly these kinds of recruitment messages, offering high pay for trivial tasks, translated from Chinese into English, Spanish, Swahili, Kinyarwanda, German, and Haitian Creole.

The scam compound recruits its own workforce with the same scam. Different bait, same hook.

The Job Hunt

When the job hunt is going badly, a customer service job in Thailand doesn’t sound like danger. It sounds like finally. That was the position Duncan Okindo found himself in mid 2024. He was 26, Kenyan, and desperate enough for work to board his first international flight. His story would later mirror those of about a dozen other men interviewed by Reuters.

At the airport, Duncan was effectively kidnapped, driven across the border into Myanmar, and taken to KK Park, a fortified compound guarded by heavily armed men. Inside, he said, hundreds of coerced workers sat at desktop computers, running scams assigned by their supervisors.

He’s a scam victim, and now his job is scamming.

Images: © Amnesty International, 2024 and 2025. Composite by the author.

Many of these workers were using the free version of ChatGPT to convince Americans to put money into fake crypto investments. His ‘‘campaign’’ targeted U.S. real estate agents. His quota: keep at least ten “clients” on the line and convince at least two people a day to put money in. He spent his days trawling property websites like Zillow.com, to find potential victims. If he managed to make contact, he posed as a wealthy Texas cattle rancher and an Alabama soybean farmer, with ChatGPT supplying the local slang.

You need to feel familiar. If you miss any point, the realtor will know that you are a scam.
— Duncan Okindo

The Escape

His eventual release began in a remarkably unremarkable way: the power went out.

Thailand cut electricity to the compounds and forced his captors to release some workers. He was among Kenyans freed from the compound.

The Scale

After eighteen months of investigation, Amnesty International found at least fifty-three scam compounds across Cambodia. The UN estimated that around 100,000 people were being held in Cambodia alone, with at least another 120,000 in Myanmar.

That’s 220,000 people.

73 of them agreed to be interviewed by Amnesty. All, without a single exception, were victims of human trafficking.

They described their life behind the perimeter walls topped with razor wire. Armed guards were everywhere. People got sold between compounds. Children as young as fourteen. “Dark rooms” were used to punish workers who missed quotas, tried to escape or contact the authorities. Electric shock batons documented in at least 19 centers.

The AI

When investigators banned one of the Myanmar scam centers and looked at how the operation used ChatGPT, it wasn’t just scam scripts. The operators were using it to run the facility. Organizing schedules, drafting internal announcements. Managing the money in and out. And assigning desk and …dormitory allocations.

Some operators must have felt guilty, or at least anxious, as they kept asking ChatGPT about the criminal penalties for people caught running scams.

Which brings me to Poipet.

The Twist in Poipet

Poipet is a casino town pressed against Cambodia’s border with Thailand. It’s also where I have to complicate the story I just told you.

In February 2025, Thai and Cambodian police raided a building there and freed 215 foreign nationals. Four more compounds in Poipet have been linked to deprivation of liberty.

In late 2025, South Korean prosecutors indicted 13 members of a fraud ring operating out of the town. Their repertoire was ambitious: from romance scams to a fake SpaceX app. They stole about $1.4 million. The ring considered ChatGPT “effective” for the work.

Except these particular scammers had prepared a cover story, in advance, for if they ever got caught.

And the cover story was: we had no choice, we were trafficked.

So how is anyone — police, courts, us — supposed to tell the difference?

Case by case. Phone log by ChatGPT log. The joint investigation unit used mobile-phone forensics and IP tracing and concluded these specific people participated voluntarily.

Trafficking is so well documented, that criminals learned to use it as a smokescreen. Real victims like Duncan get treated as criminals, and detained in immigration centers, while willing scammers were rehearsing the word “victim”.

U.S. authorities indicted Prince Group chairman Chen Zhi and seized roughly $15 billion in Bitcoin on October 14, 2025. Three months later, China secured his arrest and extradition from Cambodia.

Cambodia says it has closed nearly 200 scam operations. Its government claims hundreds of thousands of workers were rescued and repatriated. Aid workers documented the part after “rescued”: thousands of foreign workers stranded in Phnom Penh without money, shelter, free food, or water. Visa-overstay fines kept accumulating while embassies tried to get them home.

Four Rules

If you take nothing else from this story, take this.

One: the romance is the weapon. If someone you met online ushers you to Telegram or WhatsApp, and asks for money, (any amount, any reason), you’re being scammed. No exceptions. Leave.

And if a law firm later offers to recover your losses for an upfront fee, leave too. The FBI has warned that fake law firms contact victims to offer to recover their money. Surprise, surprise, they’d do it only for an upfront fee. OpenAI found one operation producing forged bar-association membership cards with ChatGPT.

Two: the job can be the weapon too. More money, a fresh start, sunshine, no paperwork. Verify it like your life depends on it. For the people in Cambodia, it did.

Three: the person scamming you could be chained to their very desk. Report the scam, but save your rage for the people who own the compound.

Four: check on your people. The lonely ones especially.

Part 2: AI, the Hero?

So far, AI has sat beside the scammers in this story. Fair enough.

But you’re only able to read my post because I could read OpenAI’s report. The police everywhere can read it too and understand the inner works of the scam groups. AI-era crime is self-documenting, and it’d be easy to see AI as the hero.

Part 3: Which one is it, then?

You tell me. What we know is that AI helped run the scam, but didn’t cause it. Cartels ran romance scams before ChatGPT. It kept producing evidence, but didn’t solve the crime. Authorities did.

The way I see it, the judgment remained, as always, human.

Where to Report Pig-Butchering Crimes

^{If you’re being scammed in the US:}^{report it to the FBI at ic3.gov and the FTC at}^{reportfraud.ftc.gov}^{. File the report even if shame has you by the throat. That shame is engineered.}

^UK:^Contact^{Report Fraud}^{, the national reporting centre that replaced Action Fraud in December 2025, or call 0300 123 2040.}

^EU:^Europol^{maintains a directory linking to the cybercrime reporting portal for every member state.}

^{If you suspect trafficking, or a job offer makes your stomach tighten:}^{in the US, call the}^{National Human Trafficking Hotline}^{at 1-888-373-7888 or text 233733. It operates around the clock in more than 200 languages. Outside the US, contact your country’s foreign ministry and embassy first. They can act across borders.}

^{If you’ve already lost money:}^the^{Global Anti-Scam Organization}^{offers victim support and is run largely by former pig-butchering victims.}

WHY SUBSCRIBE ・YOUR PERKS・CRITICAL AI LITERACY・TOOLS

CLAUDE HUB・PERPLEXITY HUB ・VIBE CODING HUB

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Google Analytics 4 Cluing Agent Instructions + Daily GA4 Analysis Prompt

Karo (Product with Attitude) — Sat, 01 Aug 2026 11:58:00 GMT

The GA4 Cluing agent checks Google Analytics 4 every morning and reports only when a metric crosses a threshold you set. It compares the last seven days against the previous seven, matched by weekday. Weekday matching matters more than it sounds. Compare a Monday to a Sunday and you invent anomalies that were never there.

The prompt handles the diagnosis too. Which source moved, which landing page carried it, which conversion event followed. “Sessions down 18%” is a number. “Referral collapsed because one syndication partner dropped the link” is an answer.

This is for solo publishers and PMs who open GA4, get mildly furious at the four-click path to a comparison view, and close the tab again.

Below: the full agent instruction block, the daily analysis prompt, the threshold defaults I run, and the two metrics I stopped tracking.

This Cluing AI agent instruction is a companion resource to my article on using Cluing for AI-powered research, knowledge management, and content workflows.

Read the article first for the full setup and context.

Agent Instructions

Skill: Weekly Reader Question Review

Karo (Product with Attitude) — Sat, 01 Aug 2026 10:05:00 GMT

Weekly Reader Question Review reads seven days of comments, replies and DMs, then returns five themes in your readers’ own words. Each theme carries a real count and two verbatim quotes. The verbatim requirement is the anti-hallucination check: a quote that is not in the input means the theme was invented, and you can see it in seconds.

The output splits reader confusion from missing coverage. Confusion means people misread something you already published, so retitle it. Missing coverage means write it. Merging the two is how content calendars fill with posts nobody was asking for.

This is for writers who feel tired of staring at an empty calendar while their comments section is stuffed with briefs.

Below: the full skill instruction.

The Skill was created in Cluing and tested in ChatGPT and Claude Cowork as a companion resource to my article on using Cluing for AI-powered research, knowledge management, and content workflows.

Read the article first for the full setup and context.

Your Readers Already Wrote Your Content Calendar. In the Comments.

Weekly Reader Question Review reads the last seven days of comments, replies and DMs, then hands back five themes in your readers’ exact words, with real counts and real quotes.

It ranks them by frequency, upgrade intent and search reach, marks the top three to write this week, and drafts the Note, the FAQ and the newsletter angle for each.

It also does the thing other idea prompts won’t: it separates confusion (retitle the old post) from gaps (write a new one), and flags every upgrade signal that no current offer answers.

Tested with

Claude Cowork, ChatGPT Work, Cluing

What Is the CCAR-F Exam, and Is It Worth It? A Decision Guide for Claude Builders

Karo (Product with Attitude) — Fri, 31 Jul 2026 20:41:00 GMT

Last verified: September 1, 2026

CCAR-F, or Claude Certified Architect – Foundations, is the Foundations certification in Anthropic’s Architect track. It tests production architecture decisions across Claude Code, the Claude Agent SDK, the Claude API, agentic systems, Model Context Protocol (MCP), prompt engineering, structured output, context management, and reliability.

The exam has 60 multiple-choice and multiple-response questions, lasts 120 minutes, and costs $125. Pearson VUE delivers it online or at a test centre. Anthropic currently limits access to people at organizations in the Claude Partner Network.

My first tip: before you open a mock exam, check whether you are eligible.

Can anyone take the CCAR-F exam?
No, not right now. You cannot register with a personal email; your email domain must match an organization in the Claude Partner Network.
Can an individual or freelancer join the network?
Not automatically. A freelancer can apply through a business, and Anthropic says Partner Network membership is free. But forming a company does not guarantee exam access. Anthropic’s current Services Track update says new applicants enter at Registered with a minimum commitment to ten certified practitioners. If you are solo, confirm your eligibility with Anthropic before building a study plan around the exam.

Hey, I’m Karo Zieminski 🤗.

I studied for the CCAR-F exam for several weeks before discovering I was not eligible to take it. An impressively efficient system, if the goal was personal growth.

Today, I’m sharing what I learned along the way.

CCAR-F Exam Quick Overview

These details come from the current certification page, official FAQ, Pearson VUE programme page, and July 2026 exam guide.

Last verified: 1 September 2026.

CCAR-F has 60 multiple-choice and multiple-response items, allows 120 minutes, costs $125 before any eligible partner discount, requires a scaled score of 720 on a 100–1,000 scale, and remains valid for 12 months. Pearson VUE delivers it online or at a test centre.

Where to Find the Official CCAR-F Exam Guide

Open the CCAR-F page in Anthropic Partner Academy. Eligible candidates register and pay there, then receive instructions for scheduling through Pearson VUE.

You can also:

Check whether your account has access to the CCAR-F exam.
Download the official PDF exam guide.
Review exam policies and testing requirements.
See the six core production scenarios used in the exam.

Your exam will include questions based on four of these six scenarios.

Where the Exam Guide Differs from Current Claude Documentation

Here are some differences I noticed while studying:

July 2026 exam-guide language: Task spawns subagents.
Current Agent SDK subagent docs: Tool-use blocks use Agent; older SDK versions and the system:init tool list may still report Task.

July 2026 exam-guide language: allowed-tools limits a skill’s tool access.
Current skills docs: It pre-approves matching tools. Use disallowed-tools to remove access.

July 2026 exam-guide language: Use @import to include another instruction file.
Current memory docs: The syntax is @path/to/file; use /context to inspect what loaded.

July 2026 exam-guide language: Tool use plus JSON Schema is the structured-output pattern.
Current structured-output docs: APIs also provide strict: true for tool inputs and output_config.format for a schema-conformant final response.

July 2026 exam-guide language: A batch request cannot run a multi-turn tool workflow.
Current batch docs: A batch can contain multi-turn history and run Anthropic server tools. It still cannot complete a client-side tool loop inside one request.

Know a freelancer, or AI architect considering CCAR-F? Send them this guide.

What CCAR-F tests

Claude certification exam domain weights table: five tested domains, their weights, and the reasoning skills each one targets.

Scroll down for a plain-English map of the July 2026 CCAR-F exam blueprint: every domain, subdomain, skill, tool, and concept it names.

Where to Find Free CCAR-F Practice Questions

The July 2026 official guide includes 12 sample questions, but Anthropic does not offer a full-length official practice bank.

Several community resources are free:

Szymon Paluch’s practice exam gives you a full 60-question mock exam based on the official blueprint.
The AI Booklet offers free CCAR-F study questions and mock exams by domain.
CertSafari also provides free CCAR-F practice questions and exam-preparation material.
You can also use this GitHub study guide, which includes six full mock exams with 360 questions.

These resources are unofficial. If they disagree with the official Anthropic exam guide, Anthropic wins.

How Long Does It Take to Prepare for the CCAR-F Exam?

Self-reported candidate experiences vary wildly:

Matthew Purcell reported about six hours of preparation on top of daily Claude use and scored 811.
Akshay Kokane studied for almost two weeks, built a small Agent SDK project, and raised his practice-exam score from 677 to 910 before passing the real exam.
Prasad Rao studied for two weeks with an architecture background and advised beginners to allow much longer.
One experienced cloud architect reported failing with 598 after three weeks and high mock scores.
One YouTuber says he passed after two days of study.
Yeah. I know. That’s not the benchmark I would plan around.

CCAR-F Exam Experiences: What Passed Candidates Are Saying

Here is what professionals who have passed the CCAR-F exam have shared about their testing experience and study strategies:

On the scenario-based difficulty:

Each [question] was well-constructed and based on real production concerns rather than theoretical edge cases. [...] Hands-on experience with the Agent SDK, MCP tools, and Claude Code is not optional — it is how you develop the judgment the exam tests.
— Ihor Sasovets (Score: 839/1000)

On learning “the Anthropic way”:

In the end the exam expects you to answer the questions how Anthropic wants you to. There are some things which are always wrong - like choosing the bigger model.
— Reddit Users UnfortunateHurricane & parav01d89

On exam strategy and structure:

The official exam guide names all six scenarios, and your exam form draws four of them at random with fifteen questions hanging off each one. That makes your study plan obvious: build real projects against all six, not four.
— Exam Candidate / Tech Creator

On the pressure of the retake policy:

The review process: I used the full 105 minutes, including a deliberate review pass. [...] The 6-month retake window adds real pressure — you know going in that there is no casual attempt.
— Ihor Sasovets

CCAR-F Misconceptions

Use the right name

CCAF and CCA-F are common community shorthand, but Anthropic’s current official code is CCAR-F: Claude Certified Architect – Foundations. Do not confuse it with CCAO-F, Anthropic’s separate Associate – Foundations certification.

Pay the right price and take the right exam

Anthropic launched the certification in March 2026. Exam delivery moved to Pearson VUE on 30 June 2026. That date made several older guides stale at once: the listed price changed from $99 to $125, the old official practice exam was retired and replaced by sample questions in the guide, and earlier six-month credentials were extended to 12 months.

If a guide still tells you to use ProctorFree, pay $99, or take the old practice exam, ignore it.

Understand the score

One detail is easy to misread. A scaled score of 720 does not mean you need 72% of the questions correct. Anthropic does not publish the raw-score conversion.

Is CCAR-F Worth It?

The CCAF exam is worth it when you already work with Claude and the credential can affect a decision within its 12-month validity period. The strongest cases are partner-tier requirements, project staffing, client bids, and jobs that name CCAR-F.

Its learning value is also credible because the blueprint covers production architecture decisions.

It is a weaker choice for beginners or job seekers expecting a guaranteed salary return. Anthropic publishes no pass rate, hiring effect, or salary data.

Take it if you already build production systems with Claude and the badge can change a specific work decision within the next 12 months. It can help your employer reach a partner tier. It can support staffing for a Claude project. A few current jobs also list it as preferred.

Skip it if you want a first AI certificate, a job guarantee, or proof that you can ship. Anthropic has published no pass rate, salary effect, or hiring outcome. Its own partner tiers ask for certified people, production deployments, and customer references.

The CCAF exam is worth taking for:

consultants and architects whose company belongs to the Claude Partner Network
builders who already use Claude APIs, Claude Code, the Agent SDK, or MCP at work
people who need a named credential for an upcoming Claude project, bid, promotion, or staffing decision

It is a weaker purchase for:

beginners looking for their first AI course
job switchers hoping one badge will replace a portfolio
product managers who have not implemented agent systems
founders whose product must work across several model providers

How to prepare for the CCAR-F Exam?

Start with the official guide. Treat third-party mock exams as question-format practice. Do not treat their answer keys as scripture. Zawanah’s exam report warns that some mock answers are ambiguous or wrong. A candidate who passed on a second attempt also blamed conflicting and stale preparation material for wasted study.

Map each task statement to evidence from your own work. If you cannot point to code, a configuration file, a trace, or a design decision, build a small example.

Then work in this order:

Architecture: Build one agent loop with clear stopping rules, state, escalation, and human review.
Claude Code: Test CLAUDE.md scope, commands, skills, hooks, plan mode, and CI use.
Structured output: Add a schema, validation, retries, and a failure path.
MCP and tools: Design narrow tool interfaces. Trigger timeouts, bad arguments, and partial results.
Context: Practise selecting, summarizing, and tracing context instead of stuffing everything into one prompt.
Scenario review: For every answer, state why the other plausible choices are weaker.

Verify disputed answers against current Anthropic documentation. The product and the exam have already changed since launch.

If you take the exam online, run Pearson’s system test on the exact computer and network you will use. OnVUE requires one screen, a private room, a webcam, a microphone, speakers, and a stable connection. Pearson prohibits VPNs, corporate networks, and public or shared networks. Use a permitted private network or take the exam at a test centre.

CCAR-F Exam Domains

The official exam guide divides CCAR-F into five domains:

Agentic Architecture & Orchestration
Tool Design & MCP Integration
Claude Code Configuration & Workflows
Prompt Engineering & Structured Output
Context Management & Reliability

For every domain, I separate what you need to know from what you need to be able to do.

Domain 1: Agentic Architecture & Orchestration

1.1 Design Agentic Loops

Know
- An agentic loop sends a request to Claude and checks stop_reason.
- tool_use means Claude wants to use a tool.
- end_turn means Claude has completed the task.
- After a tool runs, its result must be added to the conversation history.
- Claude uses the updated context to decide the next action.
- Model-driven tool selection is different from a fixed decision tree.
Be able to
- Continue the loop when stop_reason is tool_use.
- Stop the loop when stop_reason is end_turn.
- Add tool results to the conversation before the next model call.
- Use stop_reason as the main completion signal.
- Do not depend on natural-language phrases such as “done.”
- Do not use assistant text as the completion signal.
- Do not use an arbitrary iteration limit as the main stopping rule.

If agentic loops are new to you, start with How to Build Your First Agent. One That Works. It turns the loop into a working beginner build.

1.2 Orchestrate Multi-Agent Systems

Know
- A hub-and-spoke system uses one coordinator agent.
- The coordinator receives the main request, divides the work, selects subagents, sends context to them, receives their results, handles errors, combines the final answer.
- Subagents have isolated context.
- They do not automatically receive the coordinator’s conversation history.
- Overly narrow task decomposition can also cause problems. A coordinator can divide a broad research problem into tasks that are too small and miss important areas.
Be able to
- Select subagents based on the task.
- Do not run every subagent for every request.
- Give different research areas to different subagents.
- Reduce duplicate work between subagents.
- Route communication through the coordinator.
- Let the coordinator check the combined result for missing information.
- Send targeted follow-up work to subagents when information is missing.
- Repeat this process until the required coverage is sufficient.

1.3 Configure and Spawn Subagents

Know
- The July 2026 exam guide calls the subagent-spawning tool Task. Current tool-use blocks call it Agent; older SDK versions and the system:init tool list may still report Task.
- Current Agent SDK examples include Agent in the coordinator’s allowedTools. Each AgentDefinition uses its own tools field to define the subagent’s tool set and disallowedTools to remove tools.
- Ordinary subagents do not automatically inherit the parent’s context, previous agent results, or shared memory. Claude Code conversation forks are the exception.
- You must give the required information to each subagent.
- A current AgentDefinition can specify the agent’s description, prompt, model, and tools allowlist.
- The exam guide uses fork_session for Agent SDK session branching. On the CLI, use --resume --fork-session. This creates a new session from an existing one; it is separate from both Claude Code conversation forks and a skill’s isolated context: fork.
Be able to
- Include Agent in the coordinator’s allowedTools when using the Agent SDK.
- Put all required context in the subagent prompt.
- Pass previous findings directly to the next agent.
- Keep content and source metadata separate.
- Preserve URLs, document names, and page numbers.
- Start multiple subagents in parallel with multiple Agent calls in one response.
- Give the coordinator goals and quality criteria.
- Avoid unnecessary step-by-step instructions that reduce agent flexibility.

Why the names disagree: tool-use blocks changed from Task to Agent in Claude Code v2.1.63. Detection code should match both names. For the exam, recognise the blueprint’s Task terminology. For current implementations, use the live Agent SDK subagent documentation.

1.4 Enforce Multi-Step Workflows

Know
- Prompt instructions are not deterministic.
- Use programmatic controls when a rule must always apply.
- Use structured handoffs when a human must continue the work.
Be able to
- Block a tool until required earlier steps are complete.
- Use prerequisite gates for important business rules.
- Split requests with several concerns into separate work items.
- Investigate independent items in parallel.
- Combine the findings into one response.
- Create a complete handoff for a human who cannot see the full conversation.

1.5 Use Agent SDK Hooks

Know
- Hooks can intercept tool activity.
- A PreToolUse hook can inspect, modify, deny, defer, or request approval before a tool runs.
- A PostToolUse hook can add context or replace the output after a tool runs.
- Use hooks for deterministic client-side enforcement. A hook is not, by itself, a blanket compliance guarantee.
Be able to
- Use PostToolUse to normalize tool output.
- Normalize Unix timestamps and ISO 8601 dates.
- Normalize status codes.
- Use PreToolUse to inspect outgoing tool calls.
- Block actions that break policy before they execute.
- Route blocked actions to another workflow.
- Choose hooks instead of prompt-only rules when deterministic client-side enforcement is required.

1.6 Decompose Complex Tasks

Know
- Use different decomposition methods for different tasks.
- Use a fixed sequence when the workflow is predictable.
- Use dynamic decomposition when later work depends on earlier findings.
Be able to
- Select prompt chaining for predictable workflows.
- Select dynamic decomposition for open-ended investigation.
- Split large code reviews into local file reviews and cross-file reviews.
- Map a system before making a large change.
- Update the work plan when new dependencies appear.

1.7 Manage Sessions

Know
- --resume to continue a named session;
- fork_session to create an independent branch.
- A resumed session can contain stale information.
- If files changed after the first analysis, tell Claude what changed.
- Sometimes a new session with a fresh summary is safer than resuming an old session.
Be able to
- Resume a named session.
- Fork a session to compare different approaches.
- Tell a resumed session which files changed.
- Request targeted re-analysis of changed files.
- Start a new session when old tool results are no longer reliable.
- Give the new session a structured summary of the valid findings.

Domain 2: Tool Design & MCP Integration

2.1 Design Clear Tool Interfaces

Know
- Claude uses tool descriptions to decide which tool to use.
- Weak or overlapping descriptions can cause wrong tool selection.
Be able to
- Give each tool a clear purpose.
- Explain the difference between similar tools.
- Rename tools when their names overlap.
- Replace generic tools with more specific tools when this improves selection.
- Define clear input and output contracts.
- Check system prompts for instructions that can cause incorrect tool selection.

2.2 Return Structured MCP Errors

Know
- MCP tools can use the isError flag to report failures.
- Different failures need different responses.
- Common error categories include transient errors, validation errors, permission errors, and business-rule violations.
- A generic message such as Operation failed does not give the agent enough information.
- The agent also needs to know if retry can help.
Be able to
- Return isError: true. Put application-defined fields such as errorCategory and isRetryable in structuredContent or serialised result content, with a human-readable recovery explanation.
- Identify retryable errors.
- Identify non-retryable errors.
- Distinguish a failed request from a valid empty result.
- Let subagents retry transient errors locally.
- Send unresolved errors to the coordinator.
- Include partial results when useful.
- Explain what was already attempted.

MCP note: isError is the standard MCP result flag. errorCategory and isRetryable are useful conventions, not standard top-level MCP properties. The Claude API uses the separate tool-result field is_error.

2.3 Control Tool Access and Tool Choice

Know
- Too many tools can reduce tool-selection accuracy.
- An agent should usually receive only the tools that match its role.
- On supported models and settings, Claude accepts these current tool_choice objects:
  - {"type":"auto"}: Claude can use a tool or answer with text.
  - {"type":"any"}: Claude must call at least one tool.
  - {"type":"tool","name":"tool_name"}: Claude must call the named tool.
  - {"type":"none"}: Claude cannot call a tool.
- Current restriction: any and named tool choices are unavailable with manual extended thinking and on Fable 5.1 or Mythos 5.1; auto and none remain available.
Be able to
- Give each agent only the tools it needs.
- Remove tools that are outside the agent’s role.
- Replace broad tools with constrained tools when useful.
- Give limited cross-role tools when there is a clear need.
- Use {"type":"auto"} when tool use is optional.
- On a supported model and setting, use {"type":"any"} when at least one tool call is required.
- On a supported model and setting, force a named tool when a specific first step is required. If exactly one call is required, also set disable_parallel_tool_use: true.

2.4 Integrate MCP Servers

Know
- Claude Code supports different MCP scopes.
- Use project scope for shared team configuration: .mcp.json
- Use user scope for personal or experimental configuration: ~/.claude.json
- You can use environment variables in .mcp.json.
- MCP resources can expose catalogs such as: issue indexes, documentation structures, database schemas.
Be able to
- Configure a shared MCP server in .mcp.json.
- Configure a personal MCP server in ~/.claude.json.
- Use environment variables for secrets.
- Write detailed MCP tool descriptions.
- Help Claude select an MCP tool instead of a weaker built-in tool.
- Use an existing MCP server for standard integrations when possible.
- Build a custom MCP server for team-specific workflows.
- Use MCP resources to expose available data before the agent searches it.

2.5 Use Claude Code Built-In Tools

Know
- Grep to search file contents;
- Glob to find files by path or name pattern;
- Read to read files;
- Write to write complete files;
- Edit to make targeted changes;
- Bash to run shell commands.
Be able to
- Use Grep to find function names, imports, errors, and callers.
- Use Glob to find file patterns such as **/*.test.tsx.
- Use Read before making changes when more context is required.
- Use Write when targeted editing is not reliable.
- Start codebase exploration with search.
- Read only the files that become relevant.
- Trace exported functions across wrapper modules and callers.

Domain 3: Claude Code Configuration & Workflows

3.1 Configure `CLAUDE.md`

Know
- Claude Code supports several instruction levels.
- User level: ~/.claude/CLAUDE.md
- User-level instructions are personal. They are not automatically shared through version control.
- Project level: .claude/CLAUDE.md or:
  CLAUDE.md
- Directory level:
  a CLAUDE.md inside a subdirectory.
- Use @import to include external instruction files.
- Use .claude/rules/ to split a large instruction file into smaller topic-specific files.
Be able to
- Find configuration hierarchy problems.
- Put team rules at project level.
- Keep personal rules at user level.
- Use @import for modular instructions.
- Split large instruction files into focused rule files.
- Use /memory to check which memory and instruction files are loaded.

3.2 Create Commands and Skills

Know
- Project commands: .claude/commands/
- Personal commands: ~/.claude/commands/
- Project skills: .claude/skills/
- A skill uses a SKILL.md file.
- Useful skill frontmatter includes: context: fork, allowed-tools, argument-hint.
- context: fork runs the skill in isolated context.
Be able to
- Create project slash commands for shared workflows.
- Create personal commands for personal workflows.
- Create skills with SKILL.md.
- Use context: fork when isolated context is useful.
- Limit skill tool access with allowed-tools.
- Use argument-hint to show required arguments.
- Use skills for task-specific workflows.
- Use CLAUDE.md for instructions that must always apply.

3.3 Apply Path-Specific Rules

Know
- Files in .claude/rules/ can use YAML frontmatter with a paths field.
- Claude loads the rule only when it works with matching files.
- Path rules are useful when one convention applies to files in several directories.
Be able to
- Create path-specific rule files.
- Use glob patterns in paths.
- Apply rules by file type.
- Apply rules across multiple directories.
- Use path rules instead of directory-level CLAUDE.md when the same convention applies in many locations.

3.4 Choose Plan Mode or Direct Execution

Know
- Use plan mode for:
  - complex changes;
  - architectural decisions;
  - many-file changes;
  - tasks with several valid approaches;
  - migrations.
- Use direct execution for:
  - small changes;
  - clear bug fixes;
  - one-file edits;
Be able to
- Select plan mode for high-impact work.
- Select direct execution for simple work.
- Use Explore for large discovery tasks.
- Keep the main context focused.
- Plan first and execute after the approach is clear.

3.5 Use Iterative Refinement

Know
- Concrete examples often communicate requirements better than long prose.
- Tests can also define expected behavior.
- The interview pattern lets Claude ask questions before implementation.
- This can expose hidden requirements.
Be able to
- Give two or three input/output examples.
- Write tests before implementation when useful.
- Include expected behavior and edge cases.
- Give Claude test failures during iteration.
- Ask Claude to interview you before a complex implementation.
- Give interacting problems in one message when their fixes affect each other.
- Fix independent problems one at a time when that is clearer.

3.6 Use Claude Code in CI/CD

Know
- -p
- --print
- --output-format json
- --json-schema
- Use CLAUDE.md to give CI runs project-specific rules.
- A separate Claude instance can review generated code more effectively than the same session that wrote it.
Be able to
- Run Claude Code without interactive input.
- Produce structured JSON output.
- Enforce a JSON schema.
- Add review criteria to CLAUDE.md.
- Include earlier review findings in later runs.
- Report only new or unresolved findings.
- Give existing tests to Claude before asking it to create more tests.
- Use a separate review instance for independent code review.

Domain 4: Prompt Engineering & Structured Output

4.1 Write Explicit Prompt Criteria

Know
- Specific rules work better than vague instructions.
Be able to
- Define exactly which problems Claude must report.
- Define which problems Claude must ignore.
- Separate bugs and security issues from minor style issues.
- Define severity levels with clear criteria.
- Add examples for each severity level.
- Disable a noisy review category while you improve its prompt.

4.2 Use Few-Shot Examples

Know
- Few-shot examples can improve:
  - output format;
  - tool selection;
  - classification;
  - extraction;
  - handling of ambiguous cases;
  - consistency.
Be able to
- Provide two to four useful examples.
- Show correct handling of ambiguous cases.
- Show acceptable and unacceptable code patterns.
- Demonstrate the required output format.
- Include examples with different document structures.
- Use examples to reduce false positives and extraction errors.

4.3 Enforce Structured Output

Know
- The exam guide teaches schema-based tool use as a strong method for structured output.
- In the current API, strict: true enforces a tool’s input schema. Use output_config.format when the final response itself must match a JSON schema.
- Strict schema enforcement guarantees structural compliance and removes JSON syntax or shape errors; it does not guarantee that the values are semantically correct. A refusal or token truncation can also prevent a valid final structured response.
Be able to
- Define a tool with JSON schema input.
- Extract structured data from a tool_use response.
- Use tool_choice: {"type":"auto"} when Claude may answer with text instead of calling a tool.
- On a supported model and setting, use tool_choice: {"type":"any"} when at least one tool call is required.
- On a supported model and setting, use tool_choice: {"type":"tool","name":"extract_data"} when a named extraction tool must run first.
- Make fields optional when the source may not contain the value.
- Use values such as unclear when the source is ambiguous.
- Use other plus a detail field for open categories.
- Add normalization rules for inconsistent source formats.

4.4 Validate and Retry

Know
- A retry is useful when the first result contains a correctable error.
- Give Claude the exact validation error.
- A retry will not help when the source does not contain the required information.
- Schema validation and semantic validation are different.
- Schema validation checks structure.
- Semantic validation checks meaning and relationships between values.
Be able to
- On retry, provide:
  - the source;
  - the failed result;
  - the validation error.
- Also be able to:
  - identify errors that can be corrected by retry;
  - stop retrying when the source lacks the required data;
  - compare calculated totals with stated totals;
  - add fields such as conflict_detected;
  - record detected_pattern to study repeated false positives.

4.5 Use Batch Processing

Know
- The Message Batches API gives a 50% cost reduction.
- Processing can take up to 24 hours.
- There is no guaranteed low-latency SLA.
- Use batch processing for work such as:
  - overnight reports;
  - weekly audits;
  - nightly test generation.
- Do not use it for blocking tasks such as pre-merge checks.
- A single batch request cannot run a multi-turn tool workflow.
  Use custom_id to match each request with its result.
Be able to
- Use the synchronous API for time-sensitive work.
- Use batch processing for latency-tolerant work.
- Set submission frequency based on the required SLA.
- Identify failed requests by custom_id.
- Resubmit only failed items.
- Modify failed requests when required.
- Test and refine prompts on a sample before processing a large batch.

4.6 Use Independent and Multi-Pass Reviews

Know
- A model that reviews its own work in the same session still has the context that produced the original decision.
- A separate review instance has less of this bias.
- Large reviews also work better when divided into several passes.
Be able to
- Use a second Claude instance for independent review.
- Review each file separately for local issues.
- Run a second pass for cross-file issues.
- Check data flow and integration behavior separately.
- Ask the reviewer to report confidence when this helps review routing.

Domain 5: Context Management & Reliability

5.1 Preserve Important Context

Know
- Long conversations can lose important details.
  Progressive summaries can remove:
  - exact amounts;
  - dates;
  - percentages;
  - order numbers;
  - user requirements.
- Important information can also receive less attention when it is in the middle of a very long input.
- Tool output can consume large amounts of context.
Be able to
- Keep critical facts in a separate structured block.
- Preserve exact amounts, dates, IDs, and statuses.
- Keep issue state separate from summarized conversation history.
- Remove irrelevant fields from large tool responses.
- Put key findings near the start of a combined input.
- Use clear section headings.
- Require source metadata from subagents.
- Ask upstream agents for structured facts instead of long reasoning output.

If you want the working version of this domain, read Context Engineering for Product Builders.

5.2 Handle Escalation and Ambiguity

Know
- Useful escalation triggers include:
  - the customer explicitly requests a human;
  - policy does not cover the request;
  - the agent cannot make useful progress.
- Customer frustration alone does not always require escalation.
- Self-reported model confidence is also not a reliable measure of case complexity.
- If a search returns several possible customer records, do not guess which one is correct.
Be able to
- Define explicit escalation rules.
- Add examples that show when to escalate.
- Immediately honor a clear request for a human.
- Offer to solve a normal issue when the user has not requested escalation.
- Escalate when policy is missing or unclear.
- Ask for another identifier when several records match.
- Do not select a customer by heuristic guess.

5.3 Propagate Errors Across Agents

Know
- A coordinator needs useful error information.
  Good error context includes:
  - failure type;
  - attempted action;
  - partial result;
  - possible alternative.
- A timeout is not the same as a successful search with zero results.
- Do not hide errors by returning an empty result.
  Do not stop the entire workflow because one subagent failed if useful work can continue.
Be able to
- Return structured error information.
- Distinguish access failures from empty results.
- Retry transient failures locally.
- Send unresolved errors to the coordinator.
- Include partial results.
- Explain what was attempted.
- Mark report sections where source coverage is incomplete.

5.4 Manage Context in Large Codebases

Know
- Very long exploration sessions can reduce answer quality.
  The model can start to rely on general patterns instead of the specific code it inspected earlier.
- Use:
  - subagents;
  - summaries;
  - scratchpad files;
  - structured state files;
  - /compact.
Be able to
- Give specific exploration tasks to subagents.
- Keep the main agent focused on coordination.
- Save important findings in scratchpad files.
- Summarize one investigation phase before the next phase begins.
- Give new subagents the relevant summary.
- Save agent state for recovery.
- Load saved state after a crash or restart.
- Use /compact when verbose context starts to consume the context window.

5.5 Design Human Review and Confidence Workflows

Know
- A high overall accuracy score can hide weak areas.
  For example, 97% total accuracy does not prove that every:
  - document type;
  - field;
  - customer segment
  also has 97% accuracy.
- Use stratified sampling to test different segments.
- Confidence scores also need calibration against labeled data.
Be able to
- Sample high-confidence results.
- Measure errors in different document types.
- Measure errors by field.
- Find new error patterns.
- Ask the model for field-level confidence.
- Calibrate confidence thresholds with labeled validation data.
- Send low-confidence cases to human review.
- Send ambiguous or contradictory cases to human review.
- Use reviewer capacity on the cases with the highest risk.

5.6 Preserve Provenance and Uncertainty

Know
- Source attribution can disappear during multi-step summarization.
  Each important claim should remain connected to its source.
  Useful source metadata includes:
  - source URL;
  - document name;
  - page or section;
  - publication date;
  - data collection date;
  - relevant excerpt or evidence.
- Two credible sources can report different values.
- Do not automatically select one value.
  The difference can result from different dates, methods, or populations.
Be able to
- Require structured claim-to-source mappings.
- Preserve these mappings during synthesis.
- Keep conflicting values when both are credible.
- Label the conflict.
- Preserve the original source context.
- Include publication and collection dates.
- Separate established findings from contested findings.
- Present each type of information in a useful format.

Technologies and Techniques to Know

Agent Architecture

Claude Agent SDK
Agent definitions
Agentic loops
stop_reason
PostToolUse
Tool call interception
Task
Subagent spawning
allowedTools
Prompt chaining
Coordinator-subagent architecture

Model Context Protocol

Model Context Protocol (MCP)
MCP servers
MCP tools
MCP resources
isError
Tool descriptions
Tool distribution
.mcp.json
Environment variable expansion

Claude Code

Claude Code
CLAUDE.md
.claude/rules/
.claude/commands/
.claude/skills/
SKILL.md
context: fork
argument-hint
Plan mode
Direct execution
Explore subagent
/memory
/compact
--resume
fork_session

Claude Code CLI

-p
--print
--output-format json
--json-schema

Claude API

Claude API
tool_use
JSON Schema
tool_choice
auto
any
forced tool selection
max_tokens
system prompts

Batch Processing

Message Batches API
custom_id

Built-In Development Tools

Read
Write
Edit
Bash
Grep
Glob

Prompt Engineering

Few-shot prompting
Explicit criteria
Prompt chaining
Structured output
Retry with validation feedback
Multi-pass review
Independent review

Context and Reliability

Context window management
Token budgets
Progressive summarization
Lost-in-the-middle effects
Context extraction
Scratchpad files
Session management
Provenance
Confidence scoring
Field-level confidence
Stratified sampling

CCAR-F FAQ

^{Is the CCAR-F Exam Worth It?}

^{CCAR-F is worth it when you already build with Claude and the credential can influence a specific decision before its 12-month expiry: a partner tier, client bid, project assignment, promotion, or staffing decision. The blueprint also has learning value because it covers production architecture decisions. It is weaker for beginners or anyone expecting a guaranteed hiring or salary return.}

^{Is CCAF the Same as CCAR-F?}

^{People often use CCAF and CCA-F for the certification now called Claude Certified Architect – Foundations, but Anthropic’s official code is CCAR-F. Do not confuse it with CCAO-F, the separate Associate – Foundations certification. Older study guides may use all three Architect labels, so check publication dates before following their advice.}

^{How Much Does the CCAR-F Exam Cost?}

^{CCAR-F has a $125 list price. As of 1 September 2026, eligible Select, Preferred, and Global Premier partners receive 50% off their first attempt, bringing the fee to $62.50. The amount shown at checkout is the one that applies. Each retake requires another fee at the applicable rate.}^{Pearson VUE lists}^{a 14-day wait after the first failed attempt, 30 days after the second, and 90 days after the third, with no more than four attempts per exam in a rolling 12-month period.}

^{Is the CCAR-F Exam Hard?}

^{CCAR-F can be hard even for experienced technical candidates because several options may look plausible. The exam asks you to choose the strongest production design for a detailed scenario. Preparation reports range from six hours for a daily Claude user to several weeks for other candidates. One experienced architect also reported failing after three weeks despite high mock scores. No credible average preparation time or official pass rate exists. Hands-on work with Claude Code, the Agent SDK, MCP, structured output, and failure handling is more useful than memorising isolated terms.}

^{Can Anyone Take the CCAR-F Exam?}

^{No. CCAR-F is limited to people at organizations in the Claude Partner Network. Registration requires a recognised company email; a personal email will not work, and candidates must be at least 18. Anthropic lists no formal degree, course, or prior certification requirement. It describes six or more months of practical Claude experience as the typical ideal-candidate profile, not a formal prerequisite. If you do not have partner access, you can still use its free courses and the official exam guide as a learning plan. A freelancer can apply through a business, but a company registration alone does not guarantee access.}

^{How Many Questions Are on the CCAR-F Exam?}

^{CCAR-F has 60 multiple-choice and multiple-response questions. The exam gives you 120 minutes, or an average of two minutes per question. Each multiple-response item states how many answers to select.}

^{What Score Do You Need to Pass CCAR-F?}

^{You need a scaled score of 720 on a 100–1,000 scale. That does not mean you need exactly 72% of the questions correct. Anthropic does not publish the raw-to-scaled-score conversion.}

^{Is the CCAR-F Exam Open Book?}

^{No. CCAR-F is closed book. You cannot use a browser, documentation, translator, AI assistant, notes, or a second screen during the exam.}

^{Can You Take CCAR-F Online?}

^{Yes. Pearson VUE offers online proctoring through OnVUE and appointments at test centres. If you test online, use one screen and a private room. VPNs and corporate or public networks are prohibited.}

^{What Is the Difference Between CCAR-F, CCAR-P, CCDV-F, and CCAO-F?}

^{Pearson VUE currently lists four Claude certifications}^{: CCAR-F is Architect – Foundations, CCAR-P is Architect – Professional, CCDV-F is Developer – Foundations, and CCAO-F is Associate – Foundations. Choose the role track that matches the work you actually do.}

^{How Long Is CCAR-F Valid?}

^{CCAR-F is valid for 12 months. You can renew it through a free, non-proctored Partner Academy assessment if you complete the renewal before the credential expires. If you let it lapse, you must take the full proctored exam again and pay the applicable fee. Anthropic may change the recertification requirements after material updates instead of offering the standard renewal assessment. Treat it as a versioned credential that requires current product knowledge, not a permanent line on your CV.}

Claude Hub

Karo (Product with Attitude) — Fri, 31 Jul 2026 20:14:00 GMT

^{Last updated: September 2026. Actively maintained.}

TL;DR: Claude has grown into a small ecosystem, and keeping the whole thing straight is now a job in itself. I built Claude Hub to gather every Product with Attitude guide I’ve written on Claude, including how I choose between Chat, Cowork, Design and Code; how I use Skills, connectors, memory and CLAUDE.md; and what Anthropic’s 2026 releases changed for builders. I’ve also given every release two reads: my PM read and my critical AI literacy read.

I started writing about Claude in September 2025.

Back then, Cowork didn’t exist and Skills weren’t a thing. Claude Code was a niche CLI tool that maybe a few thousands developers used daily.

Many posts and tens of thousands of words later, this page lists all my articles about Claude published Product with Attitude.

I recommend you start with official Anthropic documentation to learn what each feature does and what are the official tips. Once you’ll start using Claude, you’ll find that this is not enough.

Official Anthropic documentation gives you the features and recommended practices. Good. Start there. After a while, though, you’ll likely discover that real usage gives you edge cases and questions the docs cannot cover. That’s when practitioner guides like this one become very handy.

What’s Inside

What is Claude Hub?

I started Claude Hub after one of my readers mentioned that he was having rather a difficult time finding older articles he wished to revisit. It exists because Claude has become too big to navigate from memory. Which is annoying. Also powerful. Today, Claude Hub is Product with Attitude’s independent library for all things Claude.

The library brings together everything I learn and document while conducting my own experiments, from Claude Cowork to Claude Design and Claude Code.

Claude Guide 2026: The Fast Decision Map

In 2026, treating Claude as a single app is magnificently wrong. That said, I quite understand why finding one’s way around its many interfaces can be confusing. Chat, Cowork, Design, Code, Skills, Projects, Connectors, and CLAUDE.md all solve different problems.

Claude Cowork vs Chat vs Design vs Code: Which One Do You Need?

Claude is Anthropic’s AI assistant. Four working surfaces: Chat, Cowork, Design and Code. The right choice depends entirely on what you need Claude to accomplish.

I use Chat for a quick lookup. Cowork comes in when the work spans files and tools or needs a workflow. Design when the result works but doesn’t exactly spark joy. And Code for building, or if my work lives in a repo. Skills and CLAUDE.md make behavior repeatable, saving you from becoming the world’s most exhausted onboarding manager.

Claude Releases 2026

I’m a PM. I track product releases recreationally. And Claude changed materially in 2026. I find product unbundling to be the most significant shift. Yes, Fable 5 and Mythos moved the goalposts and rearranged our work yet again. More significant, however, was the broadening of Claude’s surfaces, which greatly expanded whom the tool could serve.

Claude’s 2026 Release Timeline

January 2026

In January, Anthropic launched Cowork as a research preview for Max users on Claude Desktop. The goal: take Claude Code-style agency and apply it to knowledge work beyond coding. Claude’s biggest non-technical adoption blocker, the black terminal window, disappeared.

_{My PM read:}_{Cowork moved Claude for non-coders beyond chat. This release gave everyone the ability to execute workflows using files and MCP integrations. From conversations about work, to execution.}

_{My critical AI literacy read:}_{This is also where risk increases. Once Claude can work around files and MCP integrations non-coders need to understand access, permissions, logging, approvals, and failure paths.}

February 2026

February proved rather busy. We gained the ability schedule tasks in Cowork. Skills, plugins and connectors were gathered into a new Customize section in Claude Desktop. Anthropic also launched a Cowork plugin marketplace, Claude for PowerPoint, improved Claude for Excel and released Sonnet 4.6 with advances across coding, computer use, long-context reasoning, agent planning, knowledge work and design. Sonnet 4.6 also received a beta one-million-token context window.

_{My PM read:}_{This release cluster was all about repeatable workflow infrastructure. Scheduled tasks remove some of the manual prompting. Customize improved how we discover and manage capabilities. PowerPoint and Excel moved it into the boring-and-expensive work where companies bleed time. The product strategy is now fairly obvious: Anthropic is shaping Claude into a}_{managed work}_layer_.

_{My critical AI literacy read:}_{Every connector, plugin, admin setting and scheduled task expands Claude’s access. Value goes up. So does the need to understand permission design, logging, failure handling and accountability.}

March 2026

What I appreciated the most in March was Cowork threads persisting and following me from mobile. Memory reached free accounts. Charts moved inline. Excel and PowerPoint got better at carrying context between them. Computer use entered research preview.

_{My PM read:}_{these updates belong in one bucket. Claude’s workspace survives the jump between chats and devices.}

_{My critical AI literacy read:}_{Better continuity = the need for better memory hygiene.}

April 2026

On April 9, 2026 Claude Cowork became generally available on macOS and Windows through Claude Desktop.

Seven days later, Anthropic launched Opus 4.7 , better at long-running coding and vision tasks, and triggered SaaSpocalypse among already-jumpy software investors.

The next day it launched Claude Design, scared Figma, and made Lovable offer users double credits.

_{My PM read:}_{This was a brilliant month for Anthropic. It was no longer adding isolated features. It was swallowing more of the workflow. Each new surface removed another reason to leave Claude.}

_{My critical AI literacy read:}_{A model generating designs doesn’t guarantee good design, nor does a market panic prove that existing tools have become obsolete. Judge the designs by how well they solves the user’s problem. Always.}

May 2026

Opus 4.8 arrived. Better agents, better governance, better work. More honesty about uncertainty. Crazy-good benchmark scores, at the time. And it wasn’t even the main event.

Anthropic was already pointing towards Claude Mythos, making Opus 4.8 both a bridge model and a rehearsal space for higher-autonomy work: effort control, parallel agents, self-verification, uncertainty signalling, and human review loops.

_{My PM read:}_{Capability + control = enterprise AI game.}

_{My critical AI literacy read:}_{We got to practice the workflows and build the habits we need before the next class of models landed.}

June 2026

June probably takes the prize for sheer chaos. Anthropic released Fable 5 for general use and restricted Mythos 5 to approved partners. 72 hours later, both models were suspended following a US government directive.

_{My PM read: The way these models were launched may be a preview of how frontier AI gets packaged from here: one model family, but different access levels. If that pattern holds, tiers will be defined by trust, not only price.}

_{My critical AI literacy read: The best model can disappear three days after launch. We suspected this, now we know for sure. Which means, that model dependency is now product risk for builders. Plan for this.}

July 2026

Fable 5 and Mythos 5 are back. Cowork moved to web and mobile, finally, with remote sessions that continue after we close our laptops. Anthropic also launched a reflection dashboard for examining how you use Claude.

_{My PM read: The Claude workspace has slipped another bolt holding it to the desktop.}

_{My critical AI literacy read: Know what Claude can change when you are not looking.}

Claude by Models

Claude by Surface

Claude Design

Stunning Hero Prompts for Claude Design: Recreate the Exact Design

Claude Cowork

Claude Code

Claude Skills

Claude Skills Libraries

I maintain two tested Claude Skills libraries: one with skills I’ve built myself, and one with skills shared by the Product with Attitude community.

You can find the community-built Claude Skills in Attitude Vault. Built one yourself? Share it there too.

Claude Connectors

Cowork Connectors

CLAUDE.md

CLAUDE.md and Rules Files

Claude Tokens

Claude Just Unlocked 1 Million Tokens For Everyone. Here Is What That Means.

Claude in Comparisons

CCAR-F Exam

What Is the CCAR-F Exam, and Is It Worth It?

Claude for Builders and Vibe Coders

Claude for Newsletter Writers

10 Tools I use to run a Bestselling Substack in 2026

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5 Protocols Now Decide How AI Agents Shop. Here's What Each One Does.

Karo (Product with Attitude) — Thu, 30 Jul 2026 13:03:56 GMT

TL;DR Agentic commerce is shopping delegated to an AI agent, and the payment rails are further along than the products they’ll pay for. The agentic commerce protocols split into layers: Stripe’s ACP runs checkout, Google’s AP2 handles spending permission, x402 and MPP move the money, and Google’s UCP now out-stars ACP on GitHub two to one. I read the x402 and MPP specs and dug through ZeroClick’s docs, where its pricing is nowhere to be found, which is its own finding. ZeroClick, this article’s sponsor, publishes any API as a machine-readable storefront agents can pay. If Gartner is right, one in five digital-commerce transactions will run through AI platforms or agents by 2030, whether or not your checkout is ready.

Agent-executed commerce is still tiny today, but if the predictions are right, it won’t stay that way much longer. Gartner expects it to become part of one in every five digital-commerce transactions by 2030. So while agentic commerce is only 3% of online transactions today, 89% of merchants say they are actively building for it.

When that happens, the products you build will need an entrance, a checkout, and an access mechanism built for agents. Your checkout redesign will meet customers who don’t even look at it. That’s if you’re a seller.

If you’re a buyer with a shopping agent, you’ll want it to understand your budget, have access to a payment method, and know when to double-check with you.

And if you’re my grandma, you’ll just shake your head.

What’s Inside

Part 1: How AI agents pay, and what ACP, AP2, x402, MPP, and UCP do
Part 2: How ZeroClick sells to agents
Part 3: Build for agents, block them, or let them buy?

Hey, I’m Karo Zieminski 🤗.

Part 1: Before AI Agents Can Shop, They Need a Way to Pay

Agentic commerce is shopping delegated to an AI agent. I’ve found three main ways this works. Sometimes a browser agent clicks the buttons (think: Perplexity Comet). Or it’s a protocol handshake with no browser involved at all. Or a hybrid.

Table 1: Three agentic commerce integration modes compared.

Today, we’re focusing on the protocol-based agentic commerce.

57% of all web traffic is already non-human. Very few of those visitors are reaching for a digital wallet. They’re AI-training crawlers, uptime checkers, Googlebot, price scrapers. They aren’t on a shopping spree. Yet.

^{Warning: Tech loves abbreviations. There are many of them ahead.
Consult the comparison table at the end of this section to avoid a headache.}

Google and Stripe are already preparing for agents to arrive en masse. They’re releasing features such as digital wallets, AP2’s signed mandates, and other infrastructure designed for agentic commerce.

^{An AP2 signed mandate is a tamper-proof digital permission slip for an AI shopping agent. It proves an agent has permission to spend and how much. Google}^{donated AP2 to the FIDO Alliance}^{, moving it from a single-vendor project into an industry standards body.}

Stripe’s Product Lead Jeff Weinstein announced in 2025 that the company had developed the Agentic Commerce Protocol (ACP), back when most of us were still trying to work out what an AI agent even was.

^{ACP lets a chat interface complete a purchase without redirecting to the merchant’s site. It handles the checkout step, sitting between discovery and settlement in the agentic commerce stack. One thing I want on record: the}^{official ACP docs}^{have no FAQ section at all, unusual for a protocol covered this widely.}

The open-source standard was co-developed with OpenAI, which launched Instant Checkout that same day, putting a purchase flow inside the chat window. (OpenAI has since phased it out, but that’s for another post).

Stripe didn’t stop at ACP. By October, it was already working with Microsoft Copilot, Anthropic, Perplexity, Vercel, Lovable, Replit, and Bolt. In December came the Agentic Commerce Suite: one integration for discovery, checkout, payments, and fraud detection.

A month later, Copilot Checkout went live. US users could buy from Etsy sellers, Urban Outfitters, and Anthropologie without leaving the chat. Then Stripe added support for Coinbase’s x402 protocol, allowing agents to pay programmatically for APIs, data, and compute using USDC on Base.

x402 is an open payment protocol that repurposes the HTTP 402 "Payment Required" status code so a server can answer a request with a price instead of a rejection, letting an AI agent pay per call and use the payment receipt itself as the access credential.

We’re laying the groundwork for an economy run not just by people, but by software; autonomous, intelligent, and always on.
— Erik Reppel, Head of Engineering at Coinbase Developer Platform

By Stripe Sessions 2026, the company had 288 new products and features to announce. One of them was MPP (Machine Payments Protocol), released by Stripe and Tempo. Another open protocol for agentic payments.

UCP (Universal Commerce Protocol), Google's answer to ACP, launched at NRF in January 2026 with Shopify, Etsy, Wayfair, Target and Walmart. By today, its GitHub repo collected 3,249 stars vs ACP's 1,497. Roughly 2.2x the community traction.

Table 2: The 5 agent payment protocols compared.

Another layer of services is appearing too. Companies such as ZeroClick add the payment and access systems to existing products, so AI agents can buy them.

Taken together, these pieces look like an operating system for what is now called ‘zero-click commerce’. ZeroClick is also the name of the company that sponsored my research for this article.

Support for today’s article comes from ZeroClick. Learn how I evaluate sponsorship requests.

How AI Agents Buy Things vs How Humans Do

Humans shop with feelings: browsing, and abandoning carts are part of our process. Agents shop with infrastructure: structured product feeds, AP2 mandates, single-use payment tokens via the ACP.

The PM in me processes market shifts by mapping user journeys. This one came out too tall to fit in your inbox without crushing it: preview below, full version here. Take a look at it before you continue. The rest of the visuals will make more sense.

Full version here.

The Post-Search Internet

Raise your hand if you’ve never abandoned a shopping cart. Or spent three days comparing two nearly identical table lamps before choosing the one that’s the super-mega-optimized version for you.

Some rather elaborate revenue machines were built around that behaviour.

Since the first day of ecommerce, the funnel assumed a human moving slowly enough to be observed and sold one more thing. The storefronts were built to charm them.

If Gartner is right, by 2030, one in five digital-commerce transactions will follow a completely different product discovery logic.

AI agents are practical creatures, with minimal interest in seductive product photography or beautifully designed search bars and filters. They’re drawn to two things: machine-readable product data and real-time inventory. Which means that the ‘storefronts’ will look very different:

Table 3: Human storefront vs agent-native equivalent comparison.

Part 2: ZeroClick

ZeroClick provides the practical layer between a product and an agent prepared to pay for it. With it, any seller's API becomes an agent-purchasable service: a machine-readable storefront with controls for pricing, identity, and agent transaction analytics. In the same way Shopify helped businesses sell to humans online, ZeroClick helps them sell to agents.

ZeroClick launched in August 2025, with $55 million in funding, as an ad network for AI chatbot products. As late as March 2026, it was still framed as an ad framework. But in July 2026, ZeroClick relaunched with x402 and MPP payments at the centre of its pitch. That’s a pivot. Most of what’s written about ZeroClick still describes the old business.

A year ago we set out to help companies sell more as AI shaped purchasing choices. Now the AI agents are ready to buy.
— Ryan Hudson, Founder at ZeroClick

Ryan Hudson knows his way around the machinery of online transactions. He co-founded Honey (the browser-extension coupon company PayPal bought for $4 billion in 2020), then founded Pie Adblock before spinning out ZeroClick.

What happens step by step when an agent buys

Sellers bring their service, ZeroClick publishes it as a machine-readable storefront that agents understand. A buyer-agent calls your endpoint, ZeroClick checks if it’s allowed to spend. If greenlighted, it logs all the consumption. The idempotencyKey prevents asynchronous retries from being billed twice. The money lands in your Stripe balance.

Table 4: Responsibility split in a ZeroClick 402 payment flow.

Table 5: ZeroClick pay-per-use flow in six steps.

Positioning against competitors

Skyfire vs ZeroClick

Skyfire is an agent-first checkout counter with its own ID system.

^{Where ZeroClick wins:}^{ZeroClick accepts more payment types, sends money straight to your regular bank via Stripe, and handles tricky}^{‘we don’t know the final price yet’}^{charges (think: pay-per-use AI where the bill depends on usage). Reserve pricing solves a real, weird problem and I couldn’t find anyone else handling it so cleanly: ZeroClick reserves the max amount, then settles for the actual one.}^{Where Skyfire wins:}^{it tells you what it costs.}

Stripe Agent Toolkit vs ZeroClick

^{Where ZeroClick wins:}^{it was built for agents from day one. It tracks who each agent is and what it’s doing. Stripe, at least today, does not.}^{Where Stripe wins:}^{Stripe is Stripe. Every developer already has an account.}

Coinbase x402 vs ZeroClick

^{Where ZeroClick wins:}^{You don’t need to build everything yourself.}^Where^x402^wins:^{It’s free. Or rather, you pay whatever building and maintaining it costs you.}

Kontext vs ZeroClick

Kontext is helping brands get found by AI (not paid by AI).

^{Where ZeroClick wins:}^{ZeroClick processes the sale.}^{Where Kontext wins:}^{It can point at real numbers, the '}^{we got you cited X% more in ChatGPT}^{' kind. ZeroClick can't yet.}

Scrunch AI vs ZeroClick

Just like Kontext, Scrunch AI is helping brands get found by AI (not paid by AI).

^{Where ZeroClick wins:}^{ZeroClick processes the sale.}^{Where Scrunch AI wins:}^{It can point at the same kind of citation-lift numbers. ZeroClick can’t yet.}

Nekuda vs ZeroClick

Nekuda offers closed, invite-only agent payment systems.

^{Where ZeroClick wins:}^{ZeroClick supports multiple payment standards, not just one. And its documentation is dramatically better.}^{Where Nekuda wins:}^{It can point at named customers and success stories. ZeroClick can’t yet.}

Payman vs ZeroClick

Payman offers closed, invite-only agent payment systems.

^{Where ZeroClick wins:}^{ZeroClick supports multiple payment standards, not just one.}^{Where Payman wins:}^{It can point at named customers and success stories. ZeroClick can’t yet.}

My PM Read

No public pricing = enterprise-only?

I reaaaaally tried to find out what ZeroClick charges sellers. I looked directly in the primary sources (zeroclick.ai, the developer docs, the Zero FAQ). ZeroClick's take rate on agent transactions is nowhere to be found. The pricing URL sent me straight back to the homepage. I found some billing models in the docs, but they explain what sellers can charge agents, not what ZeroClick charges sellers.

I see two possible explanations. Either ZeroClick hasn’t finished its homepage, or it’s going enterprise-first.

And I see mixed signals.

Its $55 million seed round and partnership strategy (Mermaid, AskCodi) suggest a company built to win large deals.

If startups, indie devs and solopreneurs are a target too (startups ARE mentioned on the homepage), they’ll need a self-serve tier and a published take rate.

Possible roadmap

If I were a PM at ZeroClick, my next priorities would likely include:

One: a public seller directory. Agents find supply, sellers get distribution. ZeroClick builds a supply-side moat. And moats are nice.

Two: dedicated landing pages for selling through every major AI platform: “Sell to Claude,” “Sell to ChatGPT”. These are almost comically obvious.

Part 3: Build for It, Block It, or Let It Buy?

For AI product builders reading this

I’d watch this space closely.

If you’re building all that infrastructure yourself: Connect MPP and x402, create agent-facing endpoints. Register them across indexes. Be prepared to manage the payment protocols, access provisioning, conversion analytics and, as a little treat, every update to the standards that follows.

If you want to sell to agents without changing your API, or want an agent-purchasable API without needing to rebuild it every time a protocol changes, ZeroClick is a solid option. Getting an x402-compatible endpoint live soon, with or without ZeroClick, is worth testing.

Which protocol is best?

Pick based on what you’re building.

Each protocol I listed in this article solves a different layer: ACP runs checkout, AP2 handles authorization, x402 and MPP move the money. I read specs for MPP and x402 and they use different mechanics. MPP is session-based and bank-native, x402 is per-call and crypto-native.

For sellers who don’t want it

Can I block AI shopping agents? I’m afraid I don’t have great news. You can’t, at least not entirely. Not even with robots.txt. Robots.txt is non-binding and non-enforced. It basically asks crawlers to stay out, but doesn’t stop a determined one.

From what I can see, courts and sellers are all confused. Amazon blocked Perplexity's shopping agent, but the Ninth Circuit heard the appeal on June 11, 2026 and hasn't ruled. Every deeper how-to I read on this turns out to be content marketing from a company selling agent detection. Which tells you plenty about how solved it isn't.

Can I opt out from agentic storefronts on marketplaces like Shopify? I found a Reddit r/shopify thread where merchants ask outright “what are the specific steps we can take to disengage from this nonsense”. Feel free to explore it. It seems possible, but confusing.

For buyers

Analyst forecasts for agentic commerce by 2030 run from $144 billion to $5 trillion. But according to Gartner, consumers disagree. Only 11% of US consumers would let AI make the purchase decision. As usual, the tech is sprinting, trust is walking.

I’m collecting real transaction stories for a follow-up. If an agent has already bought something for you, or you’ve built something an agent can buy, I’d love to chat. And if you’ve watched a shopping agent do something that made you shake your head like my grandma, let me know too.

WHY SUBSCRIBE ・YOUR BENEFITS・CRITICAL AI LITERACY・TOOLS I BUILT・CLAUDE HUB・PERPLEXITY HUB ・VIBE CODING HUB

Attitude Vault ・LinkSwap ・StackShelf

1 Community, 164 builders, 100 methods, Y Combinator, and OpenAI Build Week 2026.

Karo (Product with Attitude) — Mon, 27 Jul 2026 15:24:10 GMT

164, I counted.

That’s how many builders I’ve featured on Product with Attitude. We could build a small techie town.

We tried several formats along the way, including one I adored, which responded to my affection by underperforming and causing people to unsubscribe. The current format keeps finding me new readers. And my readers new readers.

And sometimes, this happens:

I made my first dollar with the help of Karo.
It was her shoutout, through which I found initial testers of my product. It was her post through which I gained my first customer.
Highly recommend her community!
— , Founder of Donna MCP

People in this community are building remarkable things. This series gives them a stage. Join them. At the risk of repeating myself, which I fully intend to do, you can build with AI too. We’re all learning as we build.

This article is a community showcase. No sponsorships are involved, and none of the builders paid PwA to be featured. If you’d like to be mentioned in the next edition, read to the end to find out how.

Art & Design

UX + AI MCP

At this point, is becoming a recurring character in these showcases. Her builds are stunning, she refuses to stop making them, and today she’s launching an MCP server with 100+ design methods.

Physical Web Studio

This one is mine. My current obsession is giving interfaces texture and enough physical presence to make you want to touch them. I’m building a library of components that you’ll soon be able to copy and make your own.

Persona

We spend plenty of time building the agent’s brain. built Persona, a tool that builds its face. It lets you create agentic front-end experiences for the web using VanillaJS.

Productivity

VoiceOS

Two firsts for this showcase: VoiceOS is the first voice app I’ve featured and the first community build backed by Y Combinator.

My future of work has arrived. In my kitchen. I report bugs by voice, the agent handles the admin, and I keep making dinner for the kids.

If you’d like to test it, send a message.

Right Click Prompt

Right Click Prompt is no longer in beta. has officially launched it. The Chrome extension keeps your prompt library two clicks away, wherever you happen to be typing. Early-bird pricing is live right now.

Media

New Media

created New Media, a curated directory of 395 independent newsletters, podcasts, channels, and shows, plus a tools marketplace. I’m already on it, under the PRODUCT category.

Education

The Tutor That Refuses to Give Answers

created Thinking Buddy for OpenAI Build Week 2026. The AI tutor uses Socratic questions to help students think through a problem instead of handing them the answer.

Apps for Substack Writers

Fika

Are you familiar with fika? It’s the Swedish ritual of pausing for coffee, cake, and actual human company. has brought that same coziness to Substack. Her app lets you thank a writer you enjoy reading by sending them a piece of digital cake. Go on. Add crunch to someone’s day.

Retro RSS feed

is another builder I’ve featured many times because she too doesn’t stop building. She’s just launched a fully open source retro-themed RSS feed reader you run directly on your laptop.

Donna MCP

is building Donna MCP, an MCP server that connects to your AI tool with one click, scans your Substack, finds the gaps, and helps you fix them.

CP has been recruiting beta testers through the PwA community. If a one-click connection between your Substack and your AI stack sounds useful, let CP know you want in.

Essays-to-reels

is building Reads in Motion, an essay-to-reel automation for Substack writers. He needs testers. If you’ve ever wondered if reels could bring new readers to your publication, this is your invitation to find out.

Substack Archive to PDF

is working on a tool that lets you download your entire Substack archive as PDFs.

Substack recommendation graph

The relationships created by Substack recommendations are surprisingly difficult to see. is building a visual graph that maps them and helps writers find potential collaborators. Reach out if you’d like to beta test it.

Logo builder

built a workflow for Substack creators who want a more recognizable identity for their publication. It guides you through the thinking, too: positioning, style, visual direction, and refinement.

Product Development

IdeaGrit

built IdeaGrit. It pressure-tests your business idea before you build the wrong thing. It’s also live on Product Hunt if you want to give it a boost.
I did.

Other Builds

is building a platform that puts creators in charge of making games and interactive stories. Consider this the teaser. More soon.

is building his company’s custom CRM that runs on Claude, draws its knowledge from Notion, and automatically distributes tasks across the team. If you’d like to learn more about the Claude + Notion combo, he’s your person.

is two minutes away from finishing a social media habit stacker. It’s a workflow automation.

is learning to use Claude in tandem with Obsidian. Follow him if you’d like to learn how.

is experimenting with AI image generation, one for each story in his book (20 stories). He’s doing it without a paid stack, using free Grok and Stable Diffusion’s daily free tries.

is working on his image-generation skills too. And I mean skills literally: he’s building custom Skills to support his creative process.

To escape the usual subject matter, he also built a mini-app that gives him three unrelated Wikipedia articles and challenges him to write his way back to his niche.

is working on a tool that lets you download your entire Substack archive as PDFs.

built an app that tracks her subscriptions (paid Substack newsletters, streaming services, courses).

launched a well-built micro-app ecosystem for her Substack publication.

What “Build with AI” Means in 2026

Let me be clear: more things count as “building with AI” than launching a startup. Building with AI means using AI to make or improve something useful: a workflow, system, agent, tool, or product. It might serve thousands of people or just you. Both count. Whatever it is, the product muscles are the same: notice the problem, test the solution with a human (even if that human is you), and fix what breaks.

The Wednesday Build Board

Every Wednesday, I ask the same question in our community chat:

What are you building?

The Build Board gives unfinished work somewhere useful to go. People ask questions, they spot what you missed. Sometimes, they become your first users.

We do it asynchronously, every Wednesday, no need to book a flight.

How to Get on the Next Build Board

The next builder showcase goes out in about 6 weeks, and the door is open.

If you’re building a product, a workflow, an automation, or a weird AI experiment, post it in the Wednesday Build Board thread. That’s it.
Looking for beta testers in exchange for discounts? Even better. The community is full of people who will try your thing, break it, and tell you what’s wrong before your first real user does.

Hey, I’m Karo 🤗
AI PM and builder. I write Product with Attitude, an AI newsletter for tens of thousands of readers across 146 countries, helping them develop critical AI literacy the only way it sticks: through practice.

Support my mission by sharing this article with 3 friends or colleagues, and I’ll thank you with one free month of a premium subscription.

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AI-assisted Craft: A Manifesto

Karo (Product with Attitude) — Wed, 22 Jul 2026 15:06:39 GMT

The AI slop debate has been circling the same drain since 2024. By now, you’d think we might have found a name for the position somewhere between “artistic crime” and “the machine as the ultimate collaboration partner.” We haven’t.

One side calls every use of AI creative fraud. That side is especially active on Facebook, where Meta’s algorithm happily reinforces the belief. The other presses “Generate” and calls it collaboration. A life-changing, millionaire-making meeting of minds. One of those minds typed six words and the other predicted the rest, but let’s not ruin the hype.

Meanwhile, many of us are over here using AI to do solid work.

There’s a way to create with AI without watering down your claim to the work. And that practice needs a name.

AI-assisted craft.

What Is AI-Assisted Craft?

Before attempting a small intellectual land grab of naming this type of work, I searched for the phrase, fully expecting someone to have used it before me. They had. But in a more narrow context and without a definition sturdy enough to be useful.

So here is mine.

AI-assisted craft means the human sets the intention and the standard for the work, then directs how it gets made. AI gets a defined supporting role. Supporting, as in: it does not get to make the decisions. The practice applies across knowledge work and digital creation, from writing and research to coding and design.

Look. LLMs for research, coding, learning, challenging your thinking, or drafting aren’t radioactive contraband. They’re tools. Use them.

Hey, I’m Karo Zieminski 🤗

If you’re new here, welcome! Here’s what you might have missed:

How Is AI-Assisted Craft Different From AI Slop?

The same way hand-painted Chinese porcelain is different from a plastic cup on Temu: by the amount of care and skill that went into making it.

Slop begins where human care ends, regardless of whether AI was involved. Indifference, not AI, is the breeding ground for slop. It happens when the maker doesn’t care enough about the human audience to offer them quality.

I first encountered the term through in 2024. His definition was brutally efficient: unwanted AI-generated content.

Back in 2022, when AI slop looked deranged, it was easy to spot. Twelve-fingered toddlers stared from portraits. By 2025, slop had learned to code and ship apps in purple gradients. Now, in 2026, it comes with an AIO strategy: pages answering FAQs no human has ever asked, but the bots might. I bet that by 2028, it will have weaponized personalization. It will arrive custom-made for you.

If you’re reading this newsletter, you can probably spot slop from a mile away. Can your loved ones?

The Two Axes of AI-Assisted Craft

Axis 1 asks who produced the primary substance: human-made, AI-assisted, AI-directed, AI-generated, or AI-autonomous. It doesn’t grade the result.

Axis 2 asks how much craft the work shows: deliberate, competent, negligent, or slop. This describes quality. It doesn’t tell us which tools were used.

Production labels (Axis 1)

Human-made. A person produces the work. The choices, mistakes, revisions, and final form are theirs. The label doesn’t tell us which tools they used.

AI-assisted. The human is the primary maker. The choices, mistakes, revisions, and final form are theirs. AI is one of the tools they use.

AI-directed. The model produces the work; the human directs it. That direction must be consequential enough to shape the result. One prompt followed by a shrug is generation with supervision theatre.

AI-generated. AI-generated content is a neutral term. It simply means that the model produced the primary substance of the work. It says nothing about quality. Nano Banana images are AI-generated content. They can be stunning, or they can prove that photorealism and taste aren’t the same thing. The label only tells us how the pixels arrived.

AI-autonomous. You don’t work on tasks, and instead design the conditions under which the work is produced. NIST AI Risk Management and Human-AI Interaction describes a similar spectrum, from fully manual to fully autonomous. There can be plenty of craft in that setup. There can also be a tangle of defaults that nobody checked because the system seemed to be working. “Autonomous” doesn’t settle the question.

Quality labels (Axis 2)

Deliberate. Planned and executed with care. Nothing is there by accident.

Competent. Correct, solid, unremarkable. It does the job.

Negligent. Someone should have checked and didn’t.

Slop. AI or no AI, the work lacks of quality, judgment, and care.

How Can You Tell if the Work Shows Craft?

I wanted to design a test that works equally well for an article, a product specification, a piece of code, a research report, or a visual design. So the questions are deliberately tool-agnostic.

The prompt test: This one is my favorite. Can I guess, just by looking at the work, what prompt could have produced it almost exactly as it appears? If I can, it isn’t personal or specific enough. More AI than craft.

Facial expression test: Can I tell who the work is for? Or is it hotel-lobby music trying to satisfy the entire internet? Does it avoid saying anything that might cause a facial expression? AI is brilliant at measured opinions. Humans, less so.

Verification: Are all the claims factually correct? Basics, really.

Presence test: Did I learn anything new about you?

One failed check doesn’t automatically make the work slop. Several failures, however...

The 6 Rules of AI-Assisted Craft

This manifesto is deliberately small. Six rules are enough to test any workflow.

Bring Your Own Opinion (BYOO)

Don’t ask Claude what you believe. Start with something that belongs to you: an opinion, a question, an observation, or a problem that refuses to leave you alone. It can be scruffy. Mine usually is.

AI can help you wrestle it into shape. A perfectly adequate job for a machine.

I have one skill that turns unfinished thoughts into a research plan and another that does the same for my dictated Wispr notes. Lovely tools. Neither gets to decide what I mean.

Let AI organize the mess. But bring the opinion yourself. We'll know if you don't.

Give AI a Bounded Job

“Improve this” is not a bounded task.

Give the model a job with sharp edges: do this, not that.

Research for me, BUT bring me facts, not conclusions. Challenge my assumptions, BUT do that through Socratic questions so I have to do the thinking. Explain this code block, BUT test whether I understood it. Suggest design fixes BUT don't bleach my personality out of it. Whatever it is, leave space for my judgment.

In this context, a bounded AI task has three parts: a specific purpose, a BUT that limits the model’s role, and a final decision that still belongs to me, the human. When Microsoft Research surveyed 860 of its own developers, they wanted the same arrangement: for AI to absorb “the assembly work surrounding their craft, never the craft itself.”

Remember Your Person

Are you creating for someone (that someone can be you) or for $$$? We can tell.

I show you exactly how to find your person in this article.

Verify

Check everything. An invented citation can sit beside a real one without so much as twitching. Code that you ran successfully once has proved exactly one thing: it ran once.

Everything Fits. Do You?

AI has tells. Familiar turns of phrase, predictable visuals. There is nothing reaaaaally wrong with them, but are they yours? Would you have chosen this metaphor, or put that visual on your site? If the answer is no, why is it still there?

Your creativity has tells too. They echo one unique source: your lived experience. Can we see what it taught you? Or have AI’s defaults sanded every trace of it away?

Own It.

With your name and your reputation. All of it.

Keep Reading

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How to Build Your First Agent. One That Works.

Karo (Product with Attitude) — Tue, 21 Jul 2026 06:57:00 GMT

TL;DR: Building your first AI agent takes one narrow job, a folder of markdown files, and a loop. This guide explains how to build your first agent from zero: what agents are and are not, how agents differ from skills, automations, and prompts, 15 beginner questions answered straight, and a complete file-by-file build of a PRD critic agent in Claude Code. Co-authored with Dheeraj Sharma, who has built 30+ agents for his content operation. The core principle comes from Anthropic and OpenAI: workflows follow predefined code paths, agents decide their own.

Before we even start, let me say this: I doubt, quite seriously, that anyone alive has built a first agent without making a few mistakes. Not even Boris Cherny.

Since agents are new, it follows as a matter of plain logic that we’re also new to them.

I’m starting here because I’ve talked to people in my community who treat their mistakes as evidence they don’t belong here. They do. We all do. We’re all early adopters.

, whom I invited to co-author today’s article has built upwards of 30 agents and arrived at a conclusion I share entirely: the best way to avoid getting frustrated when building your first agent is to start small. Scrambled eggs before soufflé.

Or, in Dheeraj’s words:

The first agent worth building is not a genius that does everything. Build a critic that does one thing exceptionally well: it reviews your work and tells you what is wrong with it before anyone else sees it.

We wrote this article primarily for the complete beginner.

Those with an agent already ticking along will still find plenty of value here, but the mental model itself is built for someone who has never once done this. A category most of us occupied not so long ago.

What’s Inside

Hey, I’m Karo Zieminski 🤗.

I’m an AI PM and builder. I write Product with Attitude, an AI newsletter for tens of thousands of readers across 146 countries, helping them develop critical AI literacy the only way it sticks: through practice.

Today’s article was written by 2 people, but shaped by many more. Before we drafted it, I asked my readers what questions they had. And wow, did they deliver. Thank you to everyone who contributed!

Before You Read The Rest

We split this article into two parts.

Part one, written by me, focuses on making sure you understand the core concepts. They are not as scary or as technical as they seem, but you do need to understand them.

Part two, written by Dheeraj, walks you through the actual build process.

Read both.

Part 1: AI Agent Basics, Explained Once and for All

What are Agents vs Skills vs Automations vs Prompts

An agent is software that can operate semi-independently on your behalf, with your approval for key steps and decisions. It can take a goal, decide the next steps, use tools, and produce the outcome you asked for.

Anthropic defines agents as systems where LLMs dynamically direct their own processes and tool usage. OpenAI defines them as systems that independently accomplish tasks on your behalf.

I’ve been searching for a way to explain all of this in one coherent metaphor. The train metaphor holds the entire argument together rather neatly.

Agent = the train driver

He can make decisions during the journey. There are only two decision-makers in this system: the agent and his boss (you!)

Skill = the driver’s specialist training

It’s the occupational knowledge. The driver knows how to drive through the mountains, or how to handle dangerous cargo.

Automation = the train schedule

At 8:00, the train leaves. It stops at Station A and Station B. It always drives on the same track, to the same destination.

Prompt = the instruction handed to the driver for this specific journey

Things like:

Go!
Pause the driving schedule for today.

In Dheeraj’s words:

An agent does three things a skill cannot or I shall say should not.
It reads context from your files, instead of waiting for you to paste it.
It remembers across runs, so it gets sharper instead of starting from zero every time.
And it acts, taking a real step in a real tool, instead of just returning text you then have to do something with.

What Is NOT An Agent

OpenAI's guide explains what fails to qualify: simple chatbots, single-turn LLM calls, and sentiment classifiers are not agents. Neither does a prompt with 40 rules crammed into it, a contraption I confess to having built myself, with some pride at the time. If the model never controls what happens next, we’re looking at a workflow.

15 Questions About AI Agents You’re Absolutely Allowed to Ask

Non-technical language is not the same as non-technical explanation.

It’s easy for people in tech to assume that explaining technology simply means replacing technical words with friendlier ones.

But after talking to my readers, I know that simpler words don’t automatically create a working picture in your head. When people learn a new concept, they look for a familiar container. A website is familiar. A chat window is familiar. “Agent” is not.

1. Does it have an interface, like an app?

It can have one, if you decide to build it. But most of the time it really is like our train driver: you don’t need to see them to know the train is moving.

2. How do I give it instructions?

You can give an agent instructions through a chat window, a terminal, Slack, iMessage, or an API. All of these are design decisions you can test later.

3. Where does it live?

An agent is just software. It lives wherever that software runs. That can be your laptop, a rented server, or a cloud environment. For your first agent, do not overthink this. Your laptop will do perfectly well.

4. Can I move an agent after I’ve built it?

Yes, you can. It’s called migration. But don’t worry about it today.

5. Can it communicate back to me?

Yes! It can give you updates on the projects, or let you know when it gets stuck.

6. Can it make decisions?

Small ones, yes. Big ones should always belong to you. OpenAI's guidance is this: human approval before high-risk, irreversible actions. Payments, deletions, anything you can't undo.

7. Can it do things on my computer?

Yes, but only if you give it access. It can work with your files, apps, browsers etc.

8. Can it delete stuff?

Technically yes. It all depends on the access level you give it. Your first many agents should absolutely not be allowed to delete anything. Anthropic's trustworthy agents framework sorts every tool into three buckets: always allowed, needs approval, or never available. Deletes live in "never" until your agent has earned your trust. Mine still haven't.

9. How do I know what it did?

You can look at the logs, at what changed in the files, and ask for summaries.
In fact, do all three things until you trust it.

10. Will it make mistakes?

Yes. Build assuming it will.

11. Does it need to be online all the time?

Only if you want it to keep working when your laptop is asleep. Today, we’re going to build one that doesn’t.

12. Do all agents run on schedule?

No. Some of the agents I have built run when I prompt them (trigger = prompt).

Others wake up when a new file appears in a folder (trigger = file change).

My Linear agent begins work once I change a task’s status to “For Agent” (trigger = status change).

And yes, scheduled agents exist too. My email summary agent, for example, starts every morning at 6 a.m. (trigger = schedule).

13. Can I test different models for my agent?

Yes, you can, and it’s a fun task to do.

14. What do I have to do, so my agent doesn’t drift?

I think we should write a whole post about this topic.

15. What makes a good candidate for agents?

These three things: a task that happens repeatedly, has a clear goal, and can be judged against a standard.

Dheeraj suggests a four-question delegation framework:

3 Last Things Before We Build

What are agent tools?

In our train metaphor, the agent is the driver. Tools are everything within the driver’s reach: the switches, the signals, the doors, the brakes, and the radio.

They are what let the driver do more than just thinking, do anything at all: switch tracks, read railway signals, open doors, stop the train, or send a message to the next station.

What confuses many beginners is that, in everyday language, tools are usually things (nouns). A switch. A control.

But in agent language, tools often show up as actions: read, write, search, send messages, call APIs.

Side note: Anthropic wrote a whole post about how badly described tools are a primary source of agent failures. I recommend that you read it and learn to describe each tool like you're explaining it to someone smart who has zero context. Because that's exactly who's reading it.

What is an agent loop?

Your agent in action is simply a loop. The model decides what to do next, uses a tool, checks what happened, and repeats until the task is complete. This pattern has an academic name: ReAct, short for Reason + Act. LangChain, CrewAI, and the OpenAI Agents SDK all use some version of it.

What is an agent architecture?

Here’s where my train metaphor started failing me.

Architecture is easiest to understand if you think about building architecture. Someone draws where everything goes, from walls to windows.

With AI Agents, we also need to decide where everything goes:

which folders hold the instructions, which ones hold the outputs
where we file details about the agent’s tools
where we keep logs, and decisions
where the agent is allowed to read, write, or change things

In Part 2, Dheeraj will walk you through the exact architecture of the agent we’re building.

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Don’t Deploy Another AI-built App Without Monitoring Its Dependencies

Karo (Product with Attitude) — Sun, 19 Jul 2026 06:38:17 GMT

TL;DR On May 11, attackers used TanStack’s own GitHub Actions pipeline to publish 84 malicious versions across 42 npm packages, all carrying valid SLSA Build Level 3 provenance. The paperwork was real. The code was poison. Farida Khalaf shares two scripts for locking a dependency baseline, detecting changes, verifying integrity, and flagging silent updates. Her Supply Guard Deployment Gate prompt then turns the reports into a plain-language GO/NO-GO check before deployment.

On May 11, 2026. 19:20 UTC, something changed inside the TanStack project on GitHub. Theoretically, nothing broke. No warning lights flashed.

And yet.

During the 6 minutes that followed, one of the most sophisticated open-source supply chain attacks ever documented fooled even seasoned builders. 84 malicious versions appeared across 42 npm packages in the TanStack ecosystem.

The packages, trusted by many, checked every box. They had valid SLSA Build L3 provenance (their origin could be traced) and passed most standard integrity checks. By every measure builders normally use to separate legitimate files from suspicious ones, they looked legitimate.

Could these builders have avoided the attack?

, an AI and cybersecurity systems builder, analyzed the defences we can put in place beyond writing secure code and heeding warnings. And even better, she wrote and generously shared scripts and a prompt you can use today.

I’ve featured Farida before. Her guide, Vibe Coding x Cybersecurity, has been used and praised by many of my readers ever since. If you haven’t read it yet, do. My cybersecurity reading list on Substack has two non-negotiables: and . I read both greedily. You’ll learn loads if you do the same.

Hey, I’m Karo Zieminski 🤗.

I’ve been smuggling ethics into tech since 2019 and spent the last several years working at the edge of AI product development, ethical AI, and AI-assisted building.

I also build with AI myself and write Product with Attitude, an AI newsletter for tens of thousands of readers across 146 countries, helping them develop critical AI literacy the only way it sticks: through practice.

Farida’s words below.

What happened to TanStack on May 11, 2026

On May 11, 2026, a group of hackers pulled off a heist on a software toolkit used by web developers worldwide: TanStack.

Rather than breaking down the front door, they slipped in through a side entrance. They created a GitHub account, forked the TanStack repository, and submitted what looked like a normal code contribution. They even gave the fork a boring name: zblgg/configuration. Easy to overlook in a list of forks. The attacker's code ran inside TanStack's trusted environment, with TanStack's keys, even though it came from a stranger on the internet. The PR itself didn’t need to get merged, just opened. That alone triggered the next step.

Three targets: passwords, cloud keys, secret tokens. developer or CI system that installed an affected Router/Start package version during that window got malware that hunted for them. Other TanStack package families remained clean.

Twenty-six minutes later, an external researcher noticed that something was wrong and filed a bug report. A human caught what enterprise tooling missed. Sobering.

Why AI-assisted builders are more exposed

Security is often treated as a matter to be settled during the build. The code is cleaned up, and credentials are kept out of source code; after that, you ship and assume you’re done. Wrong.

Deploying gives us that pleasing air of finality. But our builds remain tied to npm libraries we used. And their automated updates. Meaning that our code can change even if we don’t touch it ourselves. So the assumption that what worked yesterday is still safe today is wrong. And that’s what supply chain ¹ attacks exploit.

^{1. When you build with AI, you don’t write everything from scratch. You borrow hundreds of pre-built components (libraries made by other developers) and your product depends on them to function. That’s the}^{supply chain}^{: not your code, but all the code your code relies on.}

The TanStack attack arrived with immaculate paperwork of valid SLSA provenance. A legitimate GitHub Actions runner published them using legitimate OIDC tokens. Every official signal said all was well.

That means that being careful at install time is no longer enough. We need a system that follows our dependencies after deployment.

The System: Lock, Monitor, Verify, Alert

You don’t need complex infrastructure. A modest setup will do, provided four parts work together: a locked baseline, change detection, integrity verification and alerts for silent updates.

Two Scripts

The code is too long to include directly in the article, so Karo has made both scripts available to copy for free from this Notion doc.

Run the script for your stack before your first deployment to establish a baseline. Then schedule it to run after every dependency update in production.

The Prompt: Supply Guard Deployment Gate

The scripts produce a technical security report. Don’t worry if you can’t understand it yourself. Copy the complete output, paste it into Claude together with the prompt, and let it explain each finding in plain language. You’ll see what needs attention, and get a clear GO / NO-GO recommendation before you deploy.

Copy the prompt from this free Notion doc.

The New Security Boundary

The largest supply-chain attacks of 2026 share a dependence on trust. We routinely accept code from Github, especially when it comes from projects we already recognize. Modern software can’t function without it.

But as we learned from the TanStack example, visibility after release now forms the meaningful security boundary.

The scripts I shared can’t make your build invulnerable. But they shorten the time between a dependency changing and somebody noticing. Supply-chain attacks thrive during that delay.

Build with your eyes open and monitor your builds as though compromise is already active.

Statistically, in 2026, it may be.

— Thank you for reading, Farida.

Keep reading

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What Is Perplexity SPACE? The Sandbox Behind Perplexity Computer

Karo (Product with Attitude) — Thu, 16 Jul 2026 08:35:03 GMT

TL;DR Perplexity SPACE is the sandbox runtime behind Perplexity Computer. It’s not Perplexity Spaces, the collaboration feature now called Projects. SPACE stands for Sandboxed Platform for Agentic Code Execution, and Perplexity says 100% of Computer sessions run on it. Its architecture uses VM-based isolation, rolling snapshots, external credential controls, and a three-layer system designed for long-running AI agents.

We build AI agents because they can do actual work. Run code. Edit and organize files, a chore we’ve wasted enough human life on since the 1980s. Search databases. Sometimes build the whole thing.

But they can get things wrong.

What Is an AI agent sandbox?

Think of a child doing a messy science experiment. You could hand them the ingredients in your kitchen and ask them to “be careful” (a phrase my kids generally interpret as background noise). Or you could put them in a playroom with a covered floor and keep the bleach locked away.

The second option is a sandbox.

A sandbox gives the agent a choreographed space to work in. You’re the one who sets the controls. Files and sensitive credentials stay protected. If something explodes, metaphorically or otherwise, the blast radius ends at the workshop door.

What Is Perplexity SPACE?

Perplexity SPACE is the sandbox platform that runs agentic workloads for Perplexity Computer. Its acronym explains precisely what it does: Sandboxed Platform for Agentic Code Execution. As of July 15, 2026, all Perplexity Computer sessions run on SPACE.

^{(Don’t confuse SPACE with Perplexity Spaces, the former name of the collaboration feature now called}^Projects^{. Projects organize information for people. SPACE isolates execution for agents. Google still mixes them up.)}

Hey, I’m Karo Zieminski 🤗.

If you’re new here, welcome! Here’s what you might have missed:

What Makes SPACE Different?

What Substack Doesn't Tell You About Cover Images

Karo (Product with Attitude) — Sat, 04 Jul 2026 11:42:12 GMT

I dare you to find one image generation model neither one of us has tried. But even with all of them at our disposal, we both spend a frankly ridiculous amount of time making our newsletter covers look good.

So when Substack takes these lovingly overthought creations and makes them look gorgeous in one place, then deranged in another, we get annoyed.

There’re at least nine preview sizes one image has to survive. And sure, if it doesn’t, a normal person says, “Good enough.” Sadly, some of us see the broken thumbnail and can’t return to peace.

So we got to work. Mostly did, while I performed the noble supporting role of asking questions. If you’re not familiar with , she’s the person behind the recent visual revolution on Substack. She’s saved countless writers from their original design choices and showed them how to stand out.

What’s Inside

The Nine Lives of a Substack Image
The Safe Zone
Inside the Safe Zone
Prompt, Figma, and Canva Templates

Hey, I’m Karo Zieminski 🤗.

I write Product with Attitude, an AI newsletter for tens of thousands of readers across 146 countries, helping them develop critical AI literacy the only way it sticks: through practice.

I want my readers to enjoy the whole experience of interacting with my publication. Assuming at least some of them are like me, and therefore perfectly capable of noticing a strangely cropped cover image, I asked AI Meets Girlboss to help map a system that, as far as I know, does not yet exist.

Today’s article was written by her, edited by yours truly, and comes with a guidebook and Figma + Canva templates you can use straight away.

If you’re new here, welcome!

From :

One day I was scrolling through my own Substack homepage and noticed that the featured post thumbnail looked nothing like the cover I’d designed. My carefully centred character had been reduced to a headless torso adrift in a pink wasteland. The logo was partially gone. The title text? Presumably off to the left.

So when Karo reached out with the same problem, I went deep into the data. I measured all nine placements, calculated the safe zone, and brought her the findings. She’s turning them into Canva and Figma templates so anyone can use them without having to do the maths themselves.

Between us, we built something that should have existed already: a proper safe zone framework for Substack covers, plus a prompt you can use to generate images that survive every crop from the start.

The Nine Lives of a Substack Image

Substack shows your image in multiple places in the app and on the web: app, web, homepage, archive, small preview, banner. Each with its own crop, each grabbing a different portion of your original image.

At first, I assumed the small square thumbnail would be the problem.

It wasn’t.

Every resized version of the original design is.

Sometimes Substack shows your image as a wide, blurred strip. Other times as a small preview. In all scenarios only a small rectangle in the centre stays readable. Everything else outside that centre zone starts moving around. It may still look pretty, in theory, but most of the time it just doesn’t.

The Safe Zone

I measured the placements and calculated the tightest window on a 1200 × 630 canvas. The safe zone is a center 345 × 195 px rectangle. Smaller than you’d expect, isn’t it?

Centre 345 × 195 px: this is where everything that matters has to live.
427 px on each side: dead zone. Use it for background, texture, colour, tiny flamingos, emotional support. Nothing important.
217 px at the top and bottom: also dead, no-no zone.

Inside the Safe Zone

It also helps to understand the composition logic within the safe zone itself:

If you have text/logo + a character:

Text and logo go left of centre. Anything readable, like your title, publication name, or logo, belongs in the left half of the safe zone: canvas x 427–600. In the tightest crops, this is what still shows. In the largest crops, it anchors the composition.
Character or focal subject goes right of centre. Your character, or main visual element belongs in the right half of the safe zone: canvas x 600–772.
Keep the face, or the highest important point, around y 250–330 so it doesn’t get decapitated. Lovely sentence to have to write.

If you only have one of them:

Everything else is atmosphere.
The dead zones are for brand colour, texture, decorative elements (flamingos!) and whatever visual nonsense makes the cover feel like yours.

Prompt, Figma, and Canva Templates

To make this easy, we prepared three resources you can use right away.

Free Sizing Prompt (ChatGPT Images 2.0) by

Substack Cover Safe Zone | The Complete Toolkit + Claude Skill
by ($9)

Figma Template by

Canva Template by

Experiment with Smart Cropping

There’s a feature on Substack called Smart Cropping. Go to your Dashboard/Website/Homepage section, and next to “Cropping” select Center or Smart. Center plays it safe and crops around the middle. Smart focuses on what the algorithm reads as the most interesting part. Test both.

Try It and Tell Us What You Get

Try any of the resources we prepared for you and let us know if they helped.

If you want to go deeper on generating consistent, on-brand visual systems with AI (not just single covers, but a repeatable workflow across all your assets), I write about exactly that over at AI Meets Girlboss. The post on how to build a visual brand system that actually holds across every asset type is a good place to start. And if you want to know where ChatGPT Images 2.0 sits in the broader AI image generation landscape, I reviewed it properly here.

— Pinkie

A note from Karo

I met here on Substack back when she had fewer than 100 subscribers. Even then, she had that exceptional writing energy where you arrive curious and leave subscribed.

Today, she’s just a few subscribers away from becoming a bestseller. It’s a great time to support her.

Perplexity Hub: Perplexity Guides for Computer, Comet, Credits & Workflows (2026)

Karo (Product with Attitude) — Tue, 30 Jun 2026 23:51:00 GMT

^{Last updated: August 2026. Actively maintained.}

TL;DR: This page is Product with Attitude's independent, hands-on library of tested guides for Perplexity Search, Deep Research, Comet, Computer, and Personal Computer. I build and maintain it from my own projects. Each guide includes the full reality of my work: what I tried, what came out, how many credits it cost, and where it broke. There’s another ‘‘Perplexity Hub’’, an official one, at perplexity.ai/hub. But what you’re reading now is created, owned, and maintained by me.

I wrote my first Perplexity article in October 2025, not that long ago when you think about it. At the time, Comet was Perplexity’s headline act. Imperfect, certainly, but capable enough to earn the company a place on my watchlist.

Since then, Perplexity has added Computer, Personal Computer, Brain, Comet Plus for publishers, mobile apps, and become the tool I use daily. Many times.

I’ve been pushing it to its limits, breaking things on purpose, and wearing different hats in the process: testing it as a builder, product manager, writer, and beginner. Each perspective revealed a different strength and weakness.

This page collects everything I’ve published about Perplexity on Product with Attitude.

My credit strategies, step by step guides, workflow and release breakdowns, product manager’s commentary, competitor teardowns, and posts where Perplexity plays a supporting role alongside Claude and Replit.

A few things I want to make clear:

Every guide is tested on real projects.
The credit estimate comes from my own usage.
Every infographic you see started as a Miro board full of scattered thoughts.

I don’t skip the parts that don’t go as planned, and if you keep reading, neither will you.

What’s Inside

Part 1: What is Perplexity and which product do you need?
Part 2: Perplexity Computer
Part 3: Common Mistakes People Make With Perplexity
Part 4: Is Perplexity useful for your role? PMs, vibe coders, writers, and finance professionals.
Part 5: What makes this library different?
FAQs

Hey, I’m Karo Zieminski,

AI Product Manager and builder.

I write Product with Attitude, an AI newsletter for tens of thousands of readers across 146 countries, helping them develop critical AI literacy the only way it sticks: through practice.

We don’t just use AI. We build workflows, agents, and products with it, while studying how AI itself is built, positioned, and woven into our work.

If you’re new here, welcome!

Part 1: What Is Perplexity, and Which Product Do You Need?

Perplexity is still best known as an AI-powered search and research platform, but I believe that framing undersells it. It’s becoming an ‘‘AI-everything’’ platform.

I use it for the whole rather crowded bench: research, design, builds, analysis, maintenance, deployment, documents, agents, task management, Github management, image generation and every tiny task in between.

As of August 2026, there are five core products:

Perplexity Search (the answer engine)
Comet (AI browser)
Perplexity Computer
Personal Computer
Perplexity API (Sonar)

The 5 Perplexity Products

Perplexity Search

Perplexity Search is for searching, as the name rather helpfully suggests. It’s the OG product and the main entry point for 45M+ monthly users. I use the standard Search only for simple queries: What’s the capital of Denmark? You ask, it answers, always with inline citations, so you can verify the source.

If I wanted to get an in-depth, sourced report on anything, that’s what I’d use. Deep Research. It scours the web, runs the findings through several leading AI models, synthesizes them into an answer, and adds inline citations so you can verify the sources.

I’ve never caught it hallucinating. Not even once. And I verify EVERYTHING.

To see where LLMs agree and where they give you different, equally confident answers, run your findings through Model Council. It’s fun.

Perplexity Search comes in three tiers: Free, Pro at $20/month, and Max at $200/month. I use Max because it gives me access to the full Perplexity lineup, including Computer.

Comet (AI browser)

My Perplexity story started with Comet, back when it was still the headline act. It's a Chromium-based AI browser with its own Assistant that you can use for multi-step in-browser tasks such as filling forms, or shopping for groceries. Since March 2026, it's free for everyone.

Perplexity Computer

This is the product I spend most of my month inside, so consider this section heavily tested and mildly biased.

Perplexity Computer is a cloud-based AI agent that orchestrates multiple frontier models (including Claude Fable 5, Claude Opus 4.8, GPT-5.6 Sol, Grok 4.5, GLM 5.2-based Preview, Gemini 3.1 Pro) to research, build, automate, and deploy from a single prompt.

The second half of this article focuses on Perplexity Computer guides.

Personal Computer

Perplexity Computer and Personal Computer sound almost identical. Easy mix-up.

Personal Computer = Perplexity Computer + Mac mini.

You get both the agentic layer and a device. Computer stays always on, follows your schedules and triggers, and can be controlled from your phone. I do exactly that, from some odd places where I have chosen, against all evidence of good sense, to get work done.

Perplexity API (Sonar)

This is the only product on this page I evaluate with my developer hat on. This is a B2D (business-to-developer) product: a full-stack, model-agnostic developer platform.

You can use it to add AI and internet-search capabilities to the apps you build. It includes four main tools:

Agent API: Lets AI complete multi-step tasks.
Search API: Finds current information on the internet.
Embeddings API: Helps software understand and compare the meaning of text.
Sandbox API: A secure environment where AI can run code and perform tasks safely.

It works across different AI models, so you avoid the usual single-provider handcuffs. It’s also compatible with OpenAI’s technical format.

Pricing: Pay-as-you-go at $1/million tokens.

Perplexity Products Comparison Table

Here’s how the five products compare: Perplexity Search (free to $200/month), Comet (free), Perplexity Computer (Pro and Max tiers), Personal Computer (Computer plus a Mac mini), and the Sonar API (pay-as-you-go at $1/million tokens):

Part 2: Perplexity Computer Guides

You got the definition in Part 1. This is the lived-in version. It's the agentic tier, and it's where the wow factor lives. It's also where most of my credits, testing hours, and an unreasonable share of my evenings go.

Computer unifies every current capability of AI into a single system.
—Aravind Srinivas

That quote, shared by Srinivas during the launch on February 25, 2026, may sound a touch overhyped, but it is accurate.

Computer can do what your Claude, Gemini, and ChatGPT can do, and I don’t mean that figuratively. It lets you choose whichever frontier model best suits the job. Or makes the choice for you when model selection is not how you wish to spend your afternoon.

Example: You’re building an app.
You work in Perplexity, but the actual build runs through Claude Code underneath, with Gemini supporting the design and ChatGPT checking your UX copy.

This orchestration, using the strongest parts of the best models for the right tasks, is why I am consistently more impressed by the results I get from Computer than by the results I get from each model separately.

But there’s more.

Perplexity Computer Brain

I’m going to write a dedicated article about Brain in the coming weeks. Until then, know that it launched on June 18, 2026, as a Research Preview for Max and Enterprise Max subscribers.

Brain is a self-improving memory system Perplexity Computer. It builds a working model of our projects, and files, then uses that context to push work forward.

I’ve had my fair share of setting up second brain systems. Notion databases with 47 views, Obsidian vaults with bidirectional links. Every time, it started with glorious clarity and ended in maintenance. The whole “second brain” paradigm - capture everything, retrieve it later - is a splendid idea, but it rarely mentions how much work it takes to maintain. A lot.

Perplexity Brain removes the system entirely. I don’t need to maintain anything. I carry on working, and it adapts to me.

What Can You Do and Build With Perplexity Computer?

These guides cover what Perplexity Computer is, what it costs, and how to use it without burning through too many credits.

Perplexity Computer Review: What I Built in One Night

I explain why Computer is novel, how it compares to Claude Cowork and OpenClaw, and what I built during my first night with it.

Perplexity Computer for Professional Finance

Perplexity Computer for Professional Finance launched May 4, 2026. I tested it on Palantir. The output was magnificent. From a Critical AI Literacy standpoint, the death of prompting worries me.

Perplexity Computer in May 2026: Updates That Changed Almost Every Serious Use Case

I shared five Perplexity Computer workflows I use every week, with the credit cost, model, time saved, PM read, and Critical AI Literacy check included for each.

How much does Perplexity Computer cost?

How to Save Credits on Perplexity Computer (Part 1)

I tested Computer hard to understand the pricing strategy and documented where credits disappear. This guide covers the credit system, a calibration experiment you can copy, and three workflow prompts I use to spend less.

Save More Credits: Advanced Patterns for Perplexity Computer (Part 2)

Seven techniques that cut credit consumption by 50%+. Covers the stacked-context tax, the fresh thread rule, splitting strategy from execution into separate threads, and when to replace conversation with scripts entirely. Part 2 of the credit-saving series.

Why Your Credits Vanish

I break down how I use Computer every single day, without smashing straight through the credit ceiling.

When Computer Wins (And When It Doesn’t)

Read the latest comparison: Perplexity Computer vs ChatGPT Work vs Claude Cowork

I’ve tested enough orchestrated workflows to have opinions with receipts.

We have five real options for orchestrated AI work today. Computer is one of them. Sending the wrong job to the wrong tool is the most expensive mistake we can make.

Where Does Perplexity Fit in Your AI Stack With Tools Like Claude or Replit?

Perplexity Computer vs ChatGPT Work vs Claude Cowork

What ChatGPT Work, Perplexity Computer and Claude Cowork assume about us, the users, and what they reveal about perceived AI intelligence.

10 Tools for Running a Bestselling Substack Newsletter in 2026

Perplexity is one of the core tools in my daily workflow alongside Claude, Recraft, and the rest. This post shows exactly how each tool fits.

Perplexity for Vibe Coders

A 16-step roadmap for building production-ready apps with AI. Perplexity serves as the research and fact-checking layer alongside Claude, Replit, and Cursor.

Part 3: Common Mistakes People Make With Perplexity

Most of these problems are habit-related. They have nothing to do with your skills.

Bringing all your Claude habits into Perplexity without adapting them was my initial mistake. Perplexity threads retain context. That’s one of the biggest differences between Computer and tools such as Claude Cowork. It also means your first prompt doesn’t have to be a complete brief that anticipates everything.
Not organizing threads into Spaces or Projects. The more I use Perplexity, the more useful I find this feature. It’s often positioned as a collaboration space, but I recommend exploring it even if you work alone.
Prompting like it’s still 2022. I wrote a dedicated article explaining how some prompting techniques can hurt the output of reasoning models.
Not prompting at all. Using Perplexity like Google. Don’t type in piles of keywords (“best AI newsletter tools 2025 comparison”, “critical AI literacy 2026”, that sort of thing). Perplexity will ask for more detail, so you’ll end up querying it multiple times and paying for every query.
Every answer comes with citations. Click them. Always verify the output. I mentioned that I’ve never caught Research hallucinating, but I can only say that because I verify everything.
Not spending enough time understanding how credits work. Computer credits burn fast. Use the bonus credits you receive at signup to learn what consumes them and how quickly.

Part 4: Perplexity for Your Role

Is Perplexity useful for product managers?

Oh, yes. The more I use Perplexity, the clearer it becomes that a product manager’s workflow maps almost directly onto its core capabilities.

Most PM work falls into three modes:

Finding out: Search, Research, and Academic help uncover market dynamics, customer problems, competitor moves, technical constraints, and evidence to support (or de-validate) your assumptions.
Deciding: Computer-built dashboards and synthesis inside Spaces pull patterns out of scattered information. You know, the material you need to make a decision instead of producing a VERY comprehensive pile of research.
Shipping the communication: Computer and connectors help translate decisions into briefs, reports, presentations, updates, and other stakeholder-ready outputs.

But here’s where PMs can get into trouble: when they outsource the judgment along with the research. Keep your product thinking close.

Is Perplexity useful for vibe coders?

At this point, it feels wrong to separate the PM role from the vibe-coder role, because 2026 has shown that they are one and the same.

The answer is yes. Perplexity is useful as the obvious vibe-coding tool and as a research and validation layer. The upfront research can save you hours of building beautiful nonsense. I also use it to manage my specs and GitHub repositories.

Is Perplexity useful for writers and newsletter operators?

I’m speaking from experience here, too. No other tool has helped me more with source discovery, fact-checking, article planning, competitive framing, and identifying adjacent questions my readers are already asking.

In theory, you can build custom research pipelines in Claude Code, and I’ve seen some writers do this with great success. I’m just not convinced that a solo build can realistically outperform the research features Perplexity has developed.

You can also use it to support your writing process, but hold on to your voice. And point of view. Otherwise, you’ll produce the same gray sludge everyone else is publishing.

Is Perplexity Computer useful for professional finance?

Yes. On May 4, 2026, Perplexity launched a dedicated feature: Perplexity Computer for Professional Finance. I documented my tests here.

Finance is a high-stakes use case, so treat outputs as analysis support. The investment decisions stay yours.

Part 5: About This Library

What is this Perplexity library?

It started with me running 45,000 credits of live testing across three weeks and documenting the workflows, the credits that vanished, and the workarounds that stopped the bleeding. Today, it's an independent collection of tested guides for Perplexity Search, Computer, Comet, and Apps. Every article comes from my own hands-on testing.

How is this library different from Perplexity’s own documentation?

I started this library with a Comet use-cases post in October 2025. Since then, it grew into one of the deepest independent Perplexity resources on Substack.

Perplexity explains what the products can do. I use between 23,000 and 70,000 credits for live testing each month, depending on how much I build. I retrace these credits. Write about decisions and consequences. Document every workflow and everything that breaks (or even stutters). If I had more time, I’d write a dozen more Perplexity Computer guides. The ones I do publish are the fossil record of my experiments. Nothing gets published here until it has survived a real project. The page you’re reading right now has been revised twelve times since the first version.

Who is this for?

I built this library thinking about the version of me who started with Perplexity in October 2025 and had no idea which product did what. Now it's the reference shelf I wish someone had handed me back then, for: writers, teachers, PMs, and builders. Free tier or a 10K-credit habit, there's something here.

Which article should I start with?

New to Computer? Start with the full review. If you’re already using it and burning credits, go straight to the credit-saving guides: Part 1 first, then Part 2 for the advanced techniques.

Frequently Asked Questions About Perplexity

What is Perplexity Search best for?

I use Search for quick queries and Deep Research when I need solid proof. Both features give me a synthesized answer with numbered citations. I click every one. If a source is an old Reddit from 2019, I downgrade it in my potential sources list. If it's a primary doc, I keep going.

What is Perplexity Comet?

I run Comet when the task lives inside the browser. Perplexity Comet is a browser-based AI assistant for productivity workflows. I gave it small jobs first, mostly to see what would break. It helps with tasks like content repurposing, and browser-context work.

Think of Comet as the layer for working inside the web, not just asking about the web. It becomes useful when the task requires navigation, comparison, summarization, or transformation across web pages and tabs.

What is Perplexity Computer?

This is the product this whole page orbits, and the one that eats most of my credits. Perplexity Computer is the agentic tier of Perplexity. It’s a cloud-based AI agent that can orchestrate frontier models to research, build, automate, deploy and maintain apps from a single prompt. I find it’s best for serious, scoped workflows where the output is valuable enough to justify agentic execution.

What can I build with Perplexity Computer?

You can use Perplexity Computer for multi-step workflows such as market research, financial analysis, product strategy, competitive teardowns, app prototyping, automation planning, and deployment-oriented tasks. I used it to build my homepage, mini apps or agents for myself, and products for my readers.

The strongest use cases I see are the ones with clear constraints. Computer can do powerful work, but it needs boundaries or it will happily send your credits up in smoke.

Some examples:

What does Perplexity Computer cost in 2026?

Perplexity Computer is available on the Pro plan at $20/month with 4,000 bonus credits and pay-per-use pricing, the Max plan at $200/month with 10,000 monthly credits and 35,000 bonus credits, and Enterprise Max at $325/seat/month.

Note: AI companies rearrange their pricing and packaging with astonishing enthusiasm. I last verified these prices in August 2026.

How do Perplexity Computer credits work? How to save them?

I learned the credit system the expensive way, which is why there are three articles below instead of one. In short, Perplexity Computer credits are consumed when Computer performs agentic work. I've documented all of it, credit by vanished credit, across several articles:

When should I use Academic focus in Perplexity?

Academic focus is my default for anything I expect readers to quote back at me. When I need peer-reviewed or research-grade sources. I lean on it hardest for AI literacy content. If readers may cite you later, the sourcing bar goes up.

WHY SUBSCRIBE ・YOUR BENEFITS・CRITICAL AI LITERACY・TOOLS I BUILT・CLAUDE HUB・PERPLEXITY HUB ・VIBE CODING HUB

Attitude Vault ・LinkSwap ・StackShelf

My free guides show which product does what and what it costs. The premium version is for readers who want advanced techniques, and the cost-saving patterns that can pay for themselves if they prevent one expensive agentic faceplant.

Human vs AI Agent Shopping Journey Comparison (Infographics)

Karo (Product with Attitude) — Tue, 30 Jun 2026 13:00:00 GMT

This infographic is a companion resource for my article on agentic e-commerce. Read the article first.

Product with Attitude

Claude Fable 5.1: What Chinese Coverage Says and English Doesn’t

The Same Launch, Read in Two Languages

What Chinese Developers Are Priced Against

What the 75% Cache Cut Buys, and What It Doesn’t

How Anti-Distillation work in Claude Fable 5.1:

The Same Model Did Not Enter the Same Market

Keep Reading

Does Pangram Work on Substack? I Ran a 159,002-Word Experiment During Its First Month

The Experiment Setup

I Know You Came for the Results. You’ll Get Them.

1. Pangram Incentives

2. Pangram Myths

3. Pangram Paternalism

Experiment Results

Experiment Conclusion

Kimi K3: I Designed One Newsletter Hero in 11 Stunning Ways

AI-ish Design and Claude-ish Design

What Changes When YOU Direct it

Why I Chose Kimi K3 as the Model For this Experiment

The Experiment: Kimi K3, the Same Words, 11 Hero Sections

Design Style 1: Conceptual Editorial Minimalism

Design Style 2: Nocturne Editorial Surrealism

Design Style 3: Cinematic Frosted-Glass Editorial

Design Style 4: Punk Editorial Collage

Design Style 5: Surrealist Watercolor Editorial

Design Style 6: Surrealist Retro Collage

Design Style 7: Futurist Glass Editorial

Design Style 8: Surreal Literary Editorial

Design Style 9: Swiss Halftone Editorial

Design Style 10: Botanical Luxury Minimalism

Design Style 11: Alpine Cinematic Editorial

The Experiment: Part 2, Your Turn

3 Steps to Direct Your Next Redesign: How to Build Stunning Websites When Your Taste Is Clear but Your Vocabulary Isn’t

This Second Brain Will Change How You Write Your Newsletter: Cluing Review + AI Agents Preview

What’s Inside

What Is Cluing? An AI Second Brain for All Your AI Tools

Who is Cluing for?

How much does Cluing cost?

Cluing vs Obsidian, Notion, Mem, and Recall

All The Things I Did with Cluing

Part 1: Saving context

1. I built a new home for my research

2. Visual saves I can think with

3. I made my context follow me into Claude, Codex, and Perplexity Computer

4. I made newsletters sort themselves before I read them

Part 2: Putting Cluing Context to Work

5. I built custom AI Agents that work directly in Cluing.

6. I scheduled a Cowork task that builds a weekly shortlist of content ideas from my saves.

7. I turned saved customer testimonials into website CTAs

8. I built a Cluing Skill that turns reader questions into post ideas

9. I wrote this article in Cluing

Final PM Notes About Cluing

Additional things I appreciated

What could be better:

Try Cluing Today (Discount for PwA Readers)

Discounts and Access Codes for Premium Members (Q3 2026)

First, the Math Behind the Discounts

Your PwA membership pays for itself, repeatedly.

AI Tool Discounts 2026:Tools I Use Or Have Tested Hands-on

Recraft AI

Cluing

Replit

Magic Patterns

Manus AI

Ovren

Lovable

Atoms

Granola AI

Cal.com

Tally

Wispr Flow

Tella.tv

Best AI Tools For Substack Writers.Built By The PwA Community Members.

StackContacts

CarouselBot

Subflow AI

Writestack

Drippery

PwA Design Resource Library

AI Tool Discounts 2026:
Tools I Use Or Have Tested Hands-on

Best AI Tools For Substack Writers.
Built By The PwA Community Members.

Every Product I’ve Launched,
Plus Everything I’ll Build Next