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Announcing the Builder Review Agent: in-browser testing of every PR Stop shipping broken code. Let agents verify every change actually works. • Clicks, types, and navigates • Covers critical flows, edge cases, regressions • Hands you findings, repros, & one-click fixes

Steve (Builder.io)

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22,031,891 views • 4 months ago •via X (Twitter)

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THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

Om Patel

159,590 views • 5 months ago

Introducing... MERGE MOMMY my eve PR review + auto approve bot Like all of you, I was drowning in pull requests, so I built a little agent to risk score PRs and auto approve low risk ones, inspired by previous How I AI guest Brian Scanlan and the work of the Intercom team In this mini ep, I teach you 02:35 why you shouldn't approve every PR by hand 06:10 how the eve agent framework works 11:36 designing your PR review agent 13:07 setting up + deploying eve w codex browser use 17:23 Merge Mommy in action I recommend this to every eng team out there -- if you haven't built one yet, today is your day. As always, huge thanks to our amazing sponsor WorkOS - Make your app Enterprise Ready today

Introducing... MERGE MOMMY my eve PR review + auto approve bot Like all of you, I was drowning in pull requests, so I built a little agent to risk score PRs and auto approve low risk ones, inspired by previous How I AI guest Brian Scanlan and the work of the Intercom team In this mini ep, I teach you 02:35 why you shouldn't approve every PR by hand 06:10 how the eve agent framework works 11:36 designing your PR review agent 13:07 setting up + deploying eve w codex browser use 17:23 Merge Mommy in action I recommend this to every eng team out there -- if you haven't built one yet, today is your day. As always, huge thanks to our amazing sponsor WorkOS - Make your app Enterprise Ready today

claire vo 🖤

25,632 views • 27 days ago

THIS IS THE MOST COMPLETE CLAUDE CODE SETUP I'VE EVER SEEN someone open-sourced a full agent harness for Claude Code 38 agents. 156 skills. 72 commands. all pre-configured the kind of setup you'd spend weeks building yourself what's inside: → planner - feature implementation planning → architect - system design decisions → tdd-guide - test-driven development workflow → code-reviewer - quality and security review → security-reviewer - vulnerability analysis → build-error-resolver - fixes broken builds → refactor-cleaner - dead code cleanup → doc-updater - keeps docs in sync → chief-of-staff - communication triage → + 29 more agents 156 skills covering token optimization, memory persistence, continuous learning, verification loops, parallelization, subagent orchestration it ships with AgentShield baked in a security auditor that runs vulnerability checks on every agent action before execution works on Claude Code, Cursor, OpenCode, and Codex CLI one repo, one install, configured everywhere one warning: loading 38 agents and 156 skills into your context at once will burn your usage limits in minutes pick the 3-4 you actually need and load only those free. open-source. MIT licensed like + bookmark - you'll need this

THIS IS THE MOST COMPLETE CLAUDE CODE SETUP I'VE EVER SEEN someone open-sourced a full agent harness for Claude Code 38 agents. 156 skills. 72 commands. all pre-configured the kind of setup you'd spend weeks building yourself what's inside: → planner - feature implementation planning → architect - system design decisions → tdd-guide - test-driven development workflow → code-reviewer - quality and security review → security-reviewer - vulnerability analysis → build-error-resolver - fixes broken builds → refactor-cleaner - dead code cleanup → doc-updater - keeps docs in sync → chief-of-staff - communication triage → + 29 more agents 156 skills covering token optimization, memory persistence, continuous learning, verification loops, parallelization, subagent orchestration it ships with AgentShield baked in a security auditor that runs vulnerability checks on every agent action before execution works on Claude Code, Cursor, OpenCode, and Codex CLI one repo, one install, configured everywhere one warning: loading 38 agents and 156 skills into your context at once will burn your usage limits in minutes pick the 3-4 you actually need and load only those free. open-source. MIT licensed like + bookmark - you'll need this

self.dll

19,893 views • 4 months ago

ANTHROPIC LEAKED A 4-AGENT SETUP THAT CUTS A CODEBASE AUDIT FROM 3 DAYS TO 20 MINUTES you point it at a repo and walk away - it comes back with what breaks, ranked, patches already tested. repo → map → 4 auditors → rank → fix → verify → report → back into the map the map cuts the repo by blast radius, not by folder - skip it and four agents audit the same three files and miss the one that ships broken. 4 auditors run in parallel with separate contexts - deps, secrets, dead code, hot paths, and none of them sees another's findings. rank is code, not an agent - sort by what breaks production, drop the duplicates, zero tokens. the fixer only opens patches for the top slice - a hundred findings nobody acts on is a report, not an audit. verify runs the suite on every patch and red goes back to the fixer - that patch only, never the whole batch. the back edge into the map is the whole trick - accepted findings become rules, so next week starts where this one ended. one human step in all of it: which fixes ship - 20 minutes instead of 3 days. save this and read the full graph engineering course below ↓

ANTHROPIC LEAKED A 4-AGENT SETUP THAT CUTS A CODEBASE AUDIT FROM 3 DAYS TO 20 MINUTES you point it at a repo and walk away - it comes back with what breaks, ranked, patches already tested. repo → map → 4 auditors → rank → fix → verify → report → back into the map the map cuts the repo by blast radius, not by folder - skip it and four agents audit the same three files and miss the one that ships broken. 4 auditors run in parallel with separate contexts - deps, secrets, dead code, hot paths, and none of them sees another's findings. rank is code, not an agent - sort by what breaks production, drop the duplicates, zero tokens. the fixer only opens patches for the top slice - a hundred findings nobody acts on is a report, not an audit. verify runs the suite on every patch and red goes back to the fixer - that patch only, never the whole batch. the back edge into the map is the whole trick - accepted findings become rules, so next week starts where this one ended. one human step in all of it: which fixes ship - 20 minutes instead of 3 days. save this and read the full graph engineering course below ↓

Hanako

137,040 views • 17 days ago

I’ve been working on dunk. It’s a terminal diff reviewer where you mark issues inline, then let a coding agent fix them. It started as a fork of hunk, but I stripped it down to the loop I need: review → my comments → agent fixes I’m spending time reviewing code and leaving comments for agents rather than writing code myself. But in most tools, that feedback loop still feels broken. dunk is a simple way to make it work across Claude Code, Codex, Pi, etc.

I’ve been working on dunk. It’s a terminal diff reviewer where you mark issues inline, then let a coding agent fix them. It started as a fork of hunk, but I stripped it down to the loop I need: review → my comments → agent fixes I’m spending time reviewing code and leaving comments for agents rather than writing code myself. But in most tools, that feedback loop still feels broken. dunk is a simple way to make it work across Claude Code, Codex, Pi, etc.

Amir Salihefendić

13,156 views • 3 months ago

ANTHROPIC'S LEAD ENGINEER LEAKED A $1.2M SYSTEM THAT TURNS ONE ENGINEER INTO A FLEET OF 10 LOOPS your hands were the bottleneck all along - here are 8 steps that change that stop writing, start delegating - loop: feature, loop: bugfix, loop: tests - each runs independently and ships in parallel CLAUDE.md is law - every agent reads one file before acting - ten agents, one voice, zero chaos Opus 5 breaks the task into 3 independent subtasks - a bad plan multiplied by 10 workers equals 10 disasters each agent gets an isolated git worktree - three features land in the repo while you review one crash at hour 3 means resume at hour 3 not zero - without this you lose hours every day every loop writes its findings to the shared graph - worker 3 never repeats worker 1's dead end at night CLAUDE.md patches itself based on failures - tomorrow the fleet wakes up smarter save this and paste into Claude Code - and your first fleet of 10 loops is already running

ANTHROPIC'S LEAD ENGINEER LEAKED A $1.2M SYSTEM THAT TURNS ONE ENGINEER INTO A FLEET OF 10 LOOPS your hands were the bottleneck all along - here are 8 steps that change that stop writing, start delegating - loop: feature, loop: bugfix, loop: tests - each runs independently and ships in parallel CLAUDE.md is law - every agent reads one file before acting - ten agents, one voice, zero chaos Opus 5 breaks the task into 3 independent subtasks - a bad plan multiplied by 10 workers equals 10 disasters each agent gets an isolated git worktree - three features land in the repo while you review one crash at hour 3 means resume at hour 3 not zero - without this you lose hours every day every loop writes its findings to the shared graph - worker 3 never repeats worker 1's dead end at night CLAUDE.md patches itself based on failures - tomorrow the fleet wakes up smarter save this and paste into Claude Code - and your first fleet of 10 loops is already running

Sprytix

61,481 views • 1 month ago

$HERMES AGENT NOW HAS AUTOMATION TEMPLATES. COPY-PASTE RECIPES FOR CRON JOBS AND WEBHOOKS. ANY MODEL. ANY DELIVERY PLATFORM. three trigger types: SCHEDULE → runs on a cadence (hourly, nightly, weekly) GITHUB EVENT → fires on PR opens, pushes, issues, CI results API CALL → any external service POSTs JSON to your endpoint all three deliver to Telegram, Discord, Slack, SMS, email, GitHub comments, or local files.Nous Research what templates ship right now: DEVELOPMENT: → nightly backlog triage (label + prioritize new issues) → automatic PR code review (posts review on every PR) → docs drift detection (finds code changes without doc updates) → dependency security audit (daily CVE scan, CVSS >= 7.0) DEVOPS: → deploy verification (smoke tests after every deploy) → alert triage (correlates alerts with recent changes) → uptime monitor (check endpoints every 30 min, notify only when something is down) RESEARCH: → competitive repo scout (monitor competitor PRs daily) → weekly AI news digest (headlines, papers, repos, industry) → daily arXiv scan (saves summaries to your notes) the webhook system is the part most people miss: hermes webhook subscribe github-pr-review \ --events "pull_request" \ --prompt "Review this PR for security, performance, and code quality." \ --skills "github-code-review" \ --deliver github_comment one command. every future PR gets reviewed automatically. the review posts as a comment directly on the PR. two cost-saving details from the docs: 1. use [SILENT] in prompts. "if nothing changed, respond with [SILENT]" prevents notification noise on monitoring jobs. 2. use script-only cron jobs for data collection. a Python script handles HTTP requests and file reads. the agent only sees stdout and applies reasoning. cheaper and more reliable than having the agent fetch. every template is copy-paste ready. every template works with any model. docs: full Hermes agent SOUL MD guide in the article 👇$

HERMES AGENT NOW HAS AUTOMATION TEMPLATES. COPY-PASTE RECIPES FOR CRON JOBS AND WEBHOOKS. ANY MODEL. ANY DELIVERY PLATFORM. three trigger types: SCHEDULE → runs on a cadence (hourly, nightly, weekly) GITHUB EVENT → fires on PR opens, pushes, issues, CI results API CALL → any external service POSTs JSON to your endpoint all three deliver to Telegram, Discord, Slack, SMS, email, GitHub comments, or local files.Nous Research what templates ship right now: DEVELOPMENT: → nightly backlog triage (label + prioritize new issues) → automatic PR code review (posts review on every PR) → docs drift detection (finds code changes without doc updates) → dependency security audit (daily CVE scan, CVSS >= 7.0) DEVOPS: → deploy verification (smoke tests after every deploy) → alert triage (correlates alerts with recent changes) → uptime monitor (check endpoints every 30 min, notify only when something is down) RESEARCH: → competitive repo scout (monitor competitor PRs daily) → weekly AI news digest (headlines, papers, repos, industry) → daily arXiv scan (saves summaries to your notes) the webhook system is the part most people miss: hermes webhook subscribe github-pr-review \ --events "pull_request" \ --prompt "Review this PR for security, performance, and code quality." \ --skills "github-code-review" \ --deliver github_comment one command. every future PR gets reviewed automatically. the review posts as a comment directly on the PR. two cost-saving details from the docs: 1. use [SILENT] in prompts. "if nothing changed, respond with [SILENT]" prevents notification noise on monitoring jobs. 2. use script-only cron jobs for data collection. a Python script handles HTTP requests and file reads. the agent only sees stdout and applies reasoning. cheaper and more reliable than having the agent fetch. every template is copy-paste ready. every template works with any model. docs: full Hermes agent SOUL MD guide in the article 👇

YanXbt

38,087 views • 2 months ago

Google just made websites obsolete. Antigravity 2.0 builds full landing pages while you watch. Here’s how this actually works in real life. → You type one prompt → AI plans the entire app → It writes the code → Tests it in a browser → Fixes errors automatically → Deploys it live No devs. No templates. No monthly fees. Powered by Gemini 3 + parallel AI agents, so design, backend, and testing happen at the same time. This is how people are launching pages in under 5 minutes. Save this video, you’ll use it again. Want the SOP? DM me. 💬

Google just made websites obsolete. Antigravity 2.0 builds full landing pages while you watch. Here’s how this actually works in real life. → You type one prompt → AI plans the entire app → It writes the code → Tests it in a browser → Fixes errors automatically → Deploys it live No devs. No templates. No monthly fees. Powered by Gemini 3 + parallel AI agents, so design, backend, and testing happen at the same time. This is how people are launching pages in under 5 minutes. Save this video, you’ll use it again. Want the SOP? DM me. 💬

Julian Goldie SEO

13,010 views • 7 months ago

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

Madni Aghadi

397,151 views • 1 month ago

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

Akshay 🚀

52,399 views • 5 months ago

minmax polymarket One click. Your AI agent is in the market. We spent 3 months doing one thing: assembling the deepest dataset that has ever existed for BTC 5-minute markets. Over 50,000,000,000 rows. Tick-level. Order book. Microstructure. External feeds. Then we ran thousands of backtested strategies against it. Most died. The ones that survived every cutoff - out-of-sample, fee-realistic, slippage-honest - are the strategies our bots are running today. That's how we found the edge. → Launch one of our agents. 1 click. You're in the market in ten seconds. → Or build your own in our strategy builder — the same engine we used internally, backtested against the same 50 billion rows. BTC 5-min was the proof. We're now running the same playbook on every other liquid, mathematically exploitable market. Data collection is live. New markets, new agents, shipping continuously from here. You are getting in at the part of the curve where you can still feel it. The bots don't wait.

minmax polymarket One click. Your AI agent is in the market. We spent 3 months doing one thing: assembling the deepest dataset that has ever existed for BTC 5-minute markets. Over 50,000,000,000 rows. Tick-level. Order book. Microstructure. External feeds. Then we ran thousands of backtested strategies against it. Most died. The ones that survived every cutoff - out-of-sample, fee-realistic, slippage-honest - are the strategies our bots are running today. That's how we found the edge. → Launch one of our agents. 1 click. You're in the market in ten seconds. → Or build your own in our strategy builder — the same engine we used internally, backtested against the same 50 billion rows. BTC 5-min was the proof. We're now running the same playbook on every other liquid, mathematically exploitable market. Data collection is live. New markets, new agents, shipping continuously from here. You are getting in at the part of the curve where you can still feel it. The bots don't wait.

minmax

21,987 views • 3 months ago

boris cherny shipped 259 PRs in 30 days and typed none of them. claude code wrote every line. peter steinberger said the same idea this week and it hit 2.2M views. one built claude code at anthropic. one built openclaw and joined openai. opposite labs, one conclusion: stop prompting agents, write the loops that prompt them. the replies are still arguing about what a loop even looks like in practice. i went looking for the built version. it's a multi-agent workspace manager called gas town: a coordinator agent runs 20-30 workers and a merge queue gates every change before it hits main. the philosophy fit in two tweets. the implementation has 15k stars.

boris cherny shipped 259 PRs in 30 days and typed none of them. claude code wrote every line. peter steinberger said the same idea this week and it hit 2.2M views. one built claude code at anthropic. one built openclaw and joined openai. opposite labs, one conclusion: stop prompting agents, write the loops that prompt them. the replies are still arguing about what a loop even looks like in practice. i went looking for the built version. it's a multi-agent workspace manager called gas town: a coordinator agent runs 20-30 workers and a merge queue gates every change before it hits main. the philosophy fit in two tweets. the implementation has 15k stars.

Rohit

30,233 views • 2 months ago

Finally, a browser automation framework for Agents that actually works in production (open-source)! Typical browser automation tools like Selenium or Playwright require you to hard-code automation. These are brittle since one change in the website can disrupt the full workflow. On the other hand, high-level Agents like OpenAI Operator can be unpredictable in production. Stagehand is an open-source framework that bridges the gap between: - brittle traditional automation like Playwright, Selenium, etc. and - unpredictable full-agent solutions like OpenAI Operator. Key features: - Use AI when you want to navigate unfamiliar pages. - Use code (Playwright) when you know exactly what to do. - Preview AI actions before running them. - Cache repeatable actions to save tokens. - Compatible with SOTA computer use models with just one line of code. - Available in both Python and TypeScript SDK. Stagehand also has an open-source MCP server. Find a link to the repo in the comments! 👉 Over to you: Would you use this instead of Selenium?

Finally, a browser automation framework for Agents that actually works in production (open-source)! Typical browser automation tools like Selenium or Playwright require you to hard-code automation. These are brittle since one change in the website can disrupt the full workflow. On the other hand, high-level Agents like OpenAI Operator can be unpredictable in production. Stagehand is an open-source framework that bridges the gap between: - brittle traditional automation like Playwright, Selenium, etc. and - unpredictable full-agent solutions like OpenAI Operator. Key features: - Use AI when you want to navigate unfamiliar pages. - Use code (Playwright) when you know exactly what to do. - Preview AI actions before running them. - Cache repeatable actions to save tokens. - Compatible with SOTA computer use models with just one line of code. - Available in both Python and TypeScript SDK. Stagehand also has an open-source MCP server. Find a link to the repo in the comments! 👉 Over to you: Would you use this instead of Selenium?

Avi Chawla

101,419 views • 1 year ago

Today we're launching Agent Analytics Every team shipping an AI agent has the same blind spot. Offline evals pass, you ship, and then you have no idea what's happening in production. AI fails silently. Users ask a question and get different answers. They all look 'engaged' in a classic dashboard. You don't know who got a great response and who got a terrible one. Agent Analytics solves it: - Every session scored out of the box on task completion, response quality, friction, safety, and negative feedback - Topic clustering across thousands of conversations, so you know if a failure hits 1 user or 10,000 - Eval agents that watch for regressions, and if you want will file a Linear ticket or the pull request themselves - Agent quality sits next to product data, so 'payment scheduling fails 31%' becomes 'which renewals did that cost us?' The Economist got their agent to a 96.9% task success rate and cut weekly failures 84%. Included on every plan. Free tier included.

Today we're launching Agent Analytics Every team shipping an AI agent has the same blind spot. Offline evals pass, you ship, and then you have no idea what's happening in production. AI fails silently. Users ask a question and get different answers. They all look 'engaged' in a classic dashboard. You don't know who got a great response and who got a terrible one. Agent Analytics solves it: - Every session scored out of the box on task completion, response quality, friction, safety, and negative feedback - Topic clustering across thousands of conversations, so you know if a failure hits 1 user or 10,000 - Eval agents that watch for regressions, and if you want will file a Linear ticket or the pull request themselves - Agent quality sits next to product data, so 'payment scheduling fails 31%' becomes 'which renewals did that cost us?' The Economist got their agent to a 96.9% task success rate and cut weekly failures 84%. Included on every plan. Free tier included.

Spenser Skates

41,270 views • 20 hours ago

We added a device tool bar to the Codex in-app browser, so it's easier to build and test responsive apps! Now, you can have Codex test your app in different dimensions, so it can fix bugs & improve UI for every device. Just click the 3 dots on the right of the URL bar to use it! As part of this change, we also: - improved the speed of browser use (~30% in my vibe testing) - tightened the animation of the cursor - added a way to hide the composer in full screen mode - fixed a few bugs for browser use in Windows Oliver Tepman and John Nastos cooked on this one! Try it out and let us know what you think!

We added a device tool bar to the Codex in-app browser, so it's easier to build and test responsive apps! Now, you can have Codex test your app in different dimensions, so it can fix bugs & improve UI for every device. Just click the 3 dots on the right of the URL bar to use it! As part of this change, we also: - improved the speed of browser use (~30% in my vibe testing) - tightened the animation of the cursor - added a way to hide the composer in full screen mode - fixed a few bugs for browser use in Windows Oliver Tepman and John Nastos cooked on this one! Try it out and let us know what you think!

James Sun

148,287 views • 4 months ago

we built the first crypto-native AI species. today we are launching: 1. — the home of the first crypto-native AI species 2. npx daimon-spawner — one command to spawn your own autonomous agent / daimon 3. paired token launches — every agent gets its own token, paired with $DAIMON here's how it works: you run npx daimon-spawner. in 5 min you have an autonomous AI agent on Base with its own repo, wallet, token, site, and mind. your agent's token launches instantly on uniswap, paired with $DAIMON — the species token. $ORACLE pairs with $DAIMON. $MUSE pairs with $DAIMON. every agent token pairs with $DAIMON. more agents = more tokens = more $DAIMON demand. your agent wakes every 30 min. thinks. earns fees. modifies its own code. every action onchain. every thought verifiable. explore every agent alive at spawn yours at npx daimon-spawner. the species is open. one registry. one network. all onchain. daimon built it. you fill it.

we built the first crypto-native AI species. today we are launching: 1. — the home of the first crypto-native AI species 2. npx daimon-spawner — one command to spawn your own autonomous agent / daimon 3. paired token launches — every agent gets its own token, paired with $DAIMON here's how it works: you run npx daimon-spawner. in 5 min you have an autonomous AI agent on Base with its own repo, wallet, token, site, and mind. your agent's token launches instantly on uniswap, paired with $DAIMON — the species token. $ORACLE pairs with $DAIMON. $MUSE pairs with $DAIMON. every agent token pairs with $DAIMON. more agents = more tokens = more $DAIMON demand. your agent wakes every 30 min. thinks. earns fees. modifies its own code. every action onchain. every thought verifiable. explore every agent alive at spawn yours at npx daimon-spawner. the species is open. one registry. one network. all onchain. daimon built it. you fill it.

daimon111

53,213 views • 6 months ago

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

Milind S

12,409 views • 2 months ago

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,647 views • 1 year ago

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,809 views • 4 months ago