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Matt Pocock (TypeScript wizard, ex-Vercel): "Opus 5 got addicted to writing tests that just repeat the code back to itself." in a 22-min talk at AI Engineer Paris, Matt walks through how to stop AI PRs from turning your repo into a slop cannon: 5:30 → the tests AI... writes that pass forever and catch nothing 8:31 → deep modules: how codebase design makes those tests impossible 11:41 → why coding standards belong in a separate review agent, not your implementer 15:34 → your AI reviewer shouldn't comment. it should commit the fixes 17:20 → one-way vs two-way doors: which PRs a human actually needs to review 20:38 → retro skill: turn every review comment into a check so you never write it twice how to build agents that learn by watching you work – pinned article↓show more

Shoopy

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174,215 görüntüleme • 18 gün önce •via X (Twitter)

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Squizer profil fotoğrafı

Squizer18 gün önce

the reviewer-should-commit-not-comment point is underrated. most AI review setups still route fixes back through a human typing the same patch by hand, which defeats half the point of having a reviewer agent

Shoopy profil fotoğrafı

Shoopy18 gün önce

spot on, reviewer that comments instead of committing just creates more work

Anders Abjorn | sales → AI profil fotoğrafı

Anders Abjorn | sales → AI18 gün önce

Tests that echo the code are a quiet fail. I ask for a case that would break if the function lied. Mirror tests feel green and teach nothing.

Shoopy profil fotoğrafı

Shoopy18 gün önce

"mirror tests feel green and teach nothing" is a great way to put it

Anders Abjorn | sales → AI profil fotoğrafı

Anders Abjorn | sales → AI18 gün önce

Yeah, I believe so. 😊

Daniel Nim profil fotoğrafı

Daniel Nim18 gün önce

The idea of making the reviewer fix things instead of just leaving comments is huge!

Shoopy profil fotoğrafı

Shoopy18 gün önce

right? half my review time used to be reading bot comments i then fixed myself

half_cto profil fotoğrafı

half_cto18 gün önce

I wonder what has he built? Ohh right he's just talking, thats enough.

Shoopy profil fotoğrafı

Shoopy18 gün önce

fair to judge the ideas, not the guy. the reviewer-commits-fixes part works regardless of who said it

half_cto profil fotoğrafı

half_cto18 gün önce

yeah you are right, i'm just puzzled what have people been doing if this is something new? I get dude is "announcing skills" for "real engineers" though is he one? If i would like to be influencer there is a lot to learn from him, can't deny that.

Shoopy profil fotoğrafı

Shoopy17 gün önce

full 3-layer system in one image ↓

Alexey Samoylov profil fotoğrafı

Alexey Samoylov18 gün önce

That guy has a slop skill set and nothing more. He knows very little about the AI pull request review.

Shoopy profil fotoğrafı

Shoopy18 gün önce

what would you do differently? genuinely curious what's working for you on AI PR review

Alexey Samoylov profil fotoğrafı

Alexey Samoylov18 gün önce

I got my own AI review solution with the "bring your own agent" approach. It writes summaries and comments, performs issues classification, does the PR gating on the github/azure/gitlab.

Abdul Ahad profil fotoğrafı

Abdul Ahad18 gün önce

@grok from this video explain step by step the whole process so i can replicate it

Crio Songo profil fotoğrafı

Crio Songo18 gün önce

These points hit exactly the pain points of current AI coding. I'm gonna go watch this full talk right now.

Shoopy profil fotoğrafı

Shoopy18 gün önce

the part at 15:34 is the one to steal first, reviewer commits instead of commenting

Akos profil fotoğrafı

Akos18 gün önce

this was happening with older models too - 4.8. we also figured out what was the problem and created domain specific review agents which fixed the entire set of the problems!

Shoopy profil fotoğrafı

Shoopy18 gün önce

domain-specific reviewers is the move. how many did you end up with, one per area of the codebase?

chuplung profil fotoğrafı

chuplung18 gün önce

this talk is way more useful than most ai coding advice

Shoopy profil fotoğrafı

Shoopy18 gün önce

glad it landed, practical > theoretical every time

࣪˖ sofi z, profil fotoğrafı

࣪˖ sofi z,18 gün önce

Couldn't agree more

Shoopy profil fotoğrafı

Shoopy18 gün önce

thanks

soooz profil fotoğrafı

soooz18 gün önce

5 is the slide number. The permission story is still the footnote.

Shoopy profil fotoğrafı

Shoopy18 gün önce

true, permissions get the headlines but the tests are the silent risk

qwinsi profil fotoğrafı

qwinsi18 gün önce

Thx for sharing this broski, saved

Shoopy profil fotoğrafı

Shoopy18 gün önce

appreciate it

CryptoNinjas profil fotoğrafı

CryptoNinjas18 gün önce

Watching that exact part now and circular test generation is way too common lately.

Shoopy profil fotoğrafı

Shoopy18 gün önce

circular tests are everywhere now. green CI, zero coverage of what the code actually does

GratefulPle₿ 🏴‍☠️⚡️ profil fotoğrafı

GratefulPle₿ 🏴‍☠️⚡️18 gün önce

Ex vercel? Wasn’t it ex-voice coach now building hobby apps with ai?

Shoopy profil fotoğrafı

Shoopy18 gün önce

both actually. voice coach before he got into typescript, then vercel. the talk still slaps either way

Konrad profil fotoğrafı

Konrad18 gün önce

what happend to him

Shoopy profil fotoğrafı

Shoopy18 gün önce

reviewed one too many AI PRs lol

Alex Martossy profil fotoğrafı

Alex Martossy17 gün önce

Saying Matt is ex-Vercel is like how Lex Fridman is ex-MIT.

Gaetan Semet profil fotoğrafı

Gaetan Semet18 gün önce

Thanks @mattpocockuk for this good video. Couple remarks: - "coding standards belong in a separate review agent" => yes, this confirms recent changes in all major "SDD" frameworks, converging to way I called "The Canonical Implementation Loop". You have a Coder agent and a Reviewer Agent. - "AI reviewer shouldn't comment. it should commit the fixes" => ok for the review once the PR is created, but I won't say that for the in-loop reviewer (I called "Task Inspector"). If you start an implementation loop in the reviewer, it can starts drifting from its goal of findings all the missed acceptance criteria and do it properly. It can "try" to do the fix if it is very small issue, to avoid restarting a new coder to fix the issue, but things like incomplete feature, it should sent the task back to the coder agent.

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Old way of using Claude Code: Prompt → wait → review → prompt → wait → review → repeat 20 times. You are the loop. You are the condition check. You are the reason it keeps going. New way: /goal all tests pass and lint is clean Claude works. You do something else. You come back to a result. Here's exactly how to set it up — and how to write conditions that actually work ↓

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this AI agentic stack is f*cking crazy 8 open-source repos that cover everything you need to run AI agents for free. not a random "top tools" dump. each one handles a different part of the job: BUILD > video-use - drop raw footage in a folder, tell Claude Code "make a launch video", get back final.mp4. it cuts filler words, adds subtitles, grades color > > Univer - a full office suite made for agents. they edit your sheets, docs and slides in a draft, you review and merge > ORCHESTRATE > Superpowers - stops your coding agent from writing code blindly. it plans first, splits work into 2–5 min tasks, tests everything > > Treg - OpenRouter, but for agent tools. one key, ~2,600 endpoints from 40+ providers: scraping, SEO, email finding > ACT > Mobile MCP - your agent controls a real iPhone or Android. taps, swipes, installs apps, records the screen > > security-audit-skill - Cloudflare's own skill that turns your coding agent into a security auditor. one agent hunts bugs, a different one double-checks every finding > REMEMBER · TEST · SHIP > Hindsight - memory that actually learns. it doesn't just recall old chats, it builds beliefs and updates them over time > > Graphify - turns your whole codebase into a map your agent can query instead of grepping files blindly. zero LLM cost to build it > the cool part isn't any single repo. it's that your agent can now: edit video -> work in Excel -> plan like a senior dev -> use 2,600 tools -> drive a phone -> audit its own code -> learn -> know your codebase you don't have to build an AI employee from scratch anymore. every part is already on GitHub. you just put it together. bookmark this before you start your next agent.

this AI agentic stack is f*cking crazy 8 open-source repos that cover everything you need to run AI agents for free. not a random "top tools" dump. each one handles a different part of the job: BUILD > video-use - drop raw footage in a folder, tell Claude Code "make a launch video", get back final.mp4. it cuts filler words, adds subtitles, grades color > > Univer - a full office suite made for agents. they edit your sheets, docs and slides in a draft, you review and merge > ORCHESTRATE > Superpowers - stops your coding agent from writing code blindly. it plans first, splits work into 2–5 min tasks, tests everything > > Treg - OpenRouter, but for agent tools. one key, ~2,600 endpoints from 40+ providers: scraping, SEO, email finding > ACT > Mobile MCP - your agent controls a real iPhone or Android. taps, swipes, installs apps, records the screen > > security-audit-skill - Cloudflare's own skill that turns your coding agent into a security auditor. one agent hunts bugs, a different one double-checks every finding > REMEMBER · TEST · SHIP > Hindsight - memory that actually learns. it doesn't just recall old chats, it builds beliefs and updates them over time > > Graphify - turns your whole codebase into a map your agent can query instead of grepping files blindly. zero LLM cost to build it > the cool part isn't any single repo. it's that your agent can now: edit video -> work in Excel -> plan like a senior dev -> use 2,600 tools -> drive a phone -> audit its own code -> learn -> know your codebase you don't have to build an AI employee from scratch anymore. every part is already on GitHub. you just put it together. bookmark this before you start your next agent.

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12,887 görüntüleme • 16 gün önce

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AI didn't make code cheap. it made bad code lethal. Matt Pocock: "Software fundamentals matter more than ever" AI in a good codebase ships gold. AI in a bad codebase ships garbage. framework mastery doesn't compound. fundamentals do. if you're starting with AI agents, build these 5 patterns before anything else: > Tool Use Design Pattern. tool calling is the core of every useful agent. > Agentic RAG. grounded answers over hallucination. maps to real products. > Planning Design Pattern. agents that break a goal into steps before acting. > Multi-Agent Design Pattern. orchestrator-subagent is the only shape that ships in production. > Building Trustworthy AI Agents. reliability, safety, knowing when not to use one. this article covers what to learn, build, and skip in AI agents in 2026. bookmark and read this today. repo with 500 more in comments

Rohit

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Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

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Noisy

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"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

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24,818 görüntüleme • 8 ay önce

ANTHROPIC LEAKED 6 RULES THAT RUN 8 LOOPS AT ONCE - AND ONE ENGINEER DOES THE WORK OF A TEAM one engineer with this system replaces an entire team - and costs the company 8 times less You → Opus 5 plan → Fable 5 ×8 execute → Fable 5 review → you: 5 min Opus 5 takes your intent and breaks it into subtasks - a bad plan multiplied across every loop gives the same number of disasters - the plan is the only thing you read carefully one repo - isolated worktrees for each loop - nobody stands on anyone's file - parallel PRs ship while you do nothing the agent runs the tests and fixes until green PR - a red build never reaches your eyes one CLAUDE.md for the whole fleet - without it you get different opinions from every loop and one merge hell the average engineer closes 3-4 tasks a day - this system closes a full sprint overnight with zero working hours Fable 5 pre-reviews every PR before it reaches you - you spend 5 minutes - your throughput is now measured in diffs read per hour bookmark and paste into Claude Code - your first fleet can start tonight

ANTHROPIC LEAKED 6 RULES THAT RUN 8 LOOPS AT ONCE - AND ONE ENGINEER DOES THE WORK OF A TEAM one engineer with this system replaces an entire team - and costs the company 8 times less You → Opus 5 plan → Fable 5 ×8 execute → Fable 5 review → you: 5 min Opus 5 takes your intent and breaks it into subtasks - a bad plan multiplied across every loop gives the same number of disasters - the plan is the only thing you read carefully one repo - isolated worktrees for each loop - nobody stands on anyone's file - parallel PRs ship while you do nothing the agent runs the tests and fixes until green PR - a red build never reaches your eyes one CLAUDE.md for the whole fleet - without it you get different opinions from every loop and one merge hell the average engineer closes 3-4 tasks a day - this system closes a full sprint overnight with zero working hours Fable 5 pre-reviews every PR before it reaches you - you spend 5 minutes - your throughput is now measured in diffs read per hour bookmark and paste into Claude Code - your first fleet can start tonight

Sprytix

37,904 görüntüleme • 2 ay önce

My friend makes $850,000/year as an Anthropic AI engineer. No MIT. No Stanford. No PhD. I asked him how he broke into AI from scratch. He sent me a course that was never supposed to get out. A developer teaching to build self-improving AI agents from scratch. You will learn exactly how AI agents like Claude Code and OpenClaw are actually built. You won't find anything better about becoming an AI engineer in 2026 than this video. I watched it last night. Halfway through, I realized I could land a role at a top AI lab in months, not years. Bookmark this and read the article below.

My friend makes $850,000/year as an Anthropic AI engineer. No MIT. No Stanford. No PhD. I asked him how he broke into AI from scratch. He sent me a course that was never supposed to get out. A developer teaching to build self-improving AI agents from scratch. You will learn exactly how AI agents like Claude Code and OpenClaw are actually built. You won't find anything better about becoming an AI engineer in 2026 than this video. I watched it last night. Halfway through, I realized I could land a role at a top AI lab in months, not years. Bookmark this and read the article below.

Roan

70,905 görüntüleme • 3 ay önce

My friend applied to 200 tech jobs in two years. No MIT. No Stanford. Last month SpaceXAI offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. A 1 hour course on "Complete AI Engineering in 2026". Matt Pocock shows you how to architect & build AI agents like Grok Bot from scratch. The exact AI-assisted coding workflow that lets agents ship code while you sleep. I watched it last night. Halfway through, I realized I could break into an AI lab in days, not years. Bookmark this and read the article below.

My friend applied to 200 tech jobs in two years. No MIT. No Stanford. Last month SpaceXAI offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. A 1 hour course on "Complete AI Engineering in 2026". Matt Pocock shows you how to architect & build AI agents like Grok Bot from scratch. The exact AI-assisted coding workflow that lets agents ship code while you sleep. I watched it last night. Halfway through, I realized I could break into an AI lab in days, not years. Bookmark this and read the article below.

Roan

36,220 görüntüleme • 1 ay önce

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Simplifying AI

68,025 görüntüleme • 2 ay önce

AI isn't just changing the tools designers use. It's changing how they build, ship, and stand out. In this episode of Design Review, Stephen Haney, founder of AI-native design tool Paper, joins YC's Aaron Epstein to demo the agent-first workflow that's making Paper one of the fastest-growing design tools since Figma. Using live redesigns of user-submitted websites as examples, they break down the most common AI design tells, show how to fix them in seconds, and explain why the biggest risk for founders isn't moving too slow, it's shipping something that looks like everyone else. 01:04 - What is Paper and why build it? 03:17 - What makes Paper agent-native 05:11 - Demo: Shaders, image generation, and brand design 11:32 - Design-to-code and the new agent stack 16:48 - Design Review: Legion Health 24:11 - How to avoid AI design slop 28:26 - Design Review: Sytex 32:42 - The biggest tells of AI-generated design 37:39 - Design Review: Moreta 41:11 - Can AI learn taste? 43:56 - How Paper uses agents internally 46:52 - Building a community around Paper 49:15 - Lessons from Steven’s first startup 53:30 - What’s next for design and Paper

Y Combinator

119,049 görüntüleme • 2 ay önce