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Andrej Karpathy posted about the new programmable layer developers need to master: agents, subagents, prompts, contexts, memory, hooks, MCP. He said it came with no manual and everyone has to figure out how to hold it. This video is part of that manual. Hooks are one of the most...

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Ray Fernando

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60,884 görüntüleme • 7 ay önce •via X (Twitter)

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My friend applied to 250 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Anthropic's 2-hour course on how to become an AI engineer in 2026. Thariq Shihipar shows you exactly how to build AI agents from scratch. I watched it last night. Halfway through, I realized I could break into an AI lab in months, not years. Bookmark this and read the article below. • 00:00 - AI agent harness • 23:44 - building AI agent loops • 56:39 - AI agent context engineering • 1:33:34 - AI agent deterministic hooks • 1:50:31 - Anthropic SWE interview process

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Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

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💥NEW AI CODING TUTORIAL: 12 Months of Claude Code Lessons in 46 Minutes Claude Code just turned 1. So here's 52 lessons on how to get the most out of Claude Code, organized into 3 levels: Beginner, Intermediate, and Master. TIMESTAMPS 00:00 12 Months of building with Claude Code 02:01 LEVEL 1: Beginner 02:09 Installation and Setup 04:55 Models and Modes 09:04 CLAUDE.md (and what to put in) 10:30 Context Management 11:31 Debugging with Claude 12:22 Version control 14:12 Prompting, Just ask Claude 16:01 LEVEL 2: Intermediate 16:24 Customizations (Skills, Plugins, MCPs) 24:42 Workflows: Plan, Build, Test 29:52 Verifying and Documenting with Claude 33:10 LEVEL 3: Master 34:17 Multi-Agent Development (multi-clauding with git worktrees) 37:07 Self-improving Systems 38:45 Ralph Wiggum Agent Loops 40:11 Async Task Delegation 42:00 Agent Teams, Pre-configure permissions, Hooks 44:42 Continue your learning

Avthar

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THIS CS TEACHER CONTROLS 4 CLAUDE CODE INSTANCES WITH HIS EYES AND VOICE - AND THEY FIX THEIR OWN MISTAKES AUTOMATICALLY He runs 4 Claude terminals at once - no mouse, no keyboard - eye tracking highlights the window he's looking at and voice dictation sends the prompt the moment he stops talking. But the real setup is what happens inside those terminals. Claude Code has zero memory of what went wrong last session - same bug that took 20 minutes to fix yesterday will happen again tomorrow unless you configure it correctly. His CLAUDE.md grows every time Claude makes a mistake - specific rules Claude reads before touching a single line - and PostToolUse hooks auto-format and type-check every file the moment it's written. Stop hooks run tests automatically when Claude says "done" - if they fail, Claude reads the errors and keeps fixing without any manual input. Before: 45 minutes of back-and-forth per feature, same mistakes on repeat - after: 10 minutes, most of it unattended.
1:23

THIS CS TEACHER CONTROLS 4 CLAUDE CODE INSTANCES WITH HIS EYES AND VOICE - AND THEY FIX THEIR OWN MISTAKES AUTOMATICALLY He runs 4 Claude terminals at once - no mouse, no keyboard - eye tracking highlights the window he's looking at and voice dictation sends the prompt the moment he stops talking. But the real setup is what happens inside those terminals. Claude Code has zero memory of what went wrong last session - same bug that took 20 minutes to fix yesterday will happen again tomorrow unless you configure it correctly. His CLAUDE.md grows every time Claude makes a mistake - specific rules Claude reads before touching a single line - and PostToolUse hooks auto-format and type-check every file the moment it's written. Stop hooks run tests automatically when Claude says "done" - if they fail, Claude reads the errors and keeps fixing without any manual input. Before: 45 minutes of back-and-forth per feature, same mistakes on repeat - after: 10 minutes, most of it unattended.

Noisy

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This guy literally broke down how to master Claude Code (even if you haven't coded before): 05:28 - Level 1: Why you start with Lovable 08:04 - Level 2: The Lovable + Claude Code bridge 28:37 - Level 3: Cursor + Vercel for real production 41:17 - Level 4: Agents, skills, and CLAUDE.md 42:50 - The CLAUDE.md memory file explained 45:24 - The PM orchestrator agent pattern 53:26 - How AI-native teams spend 50% of their time 01:01:33 - Why 90% of European PMs are still non-technical 01:07:45 - The Monday morning move
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This guy literally broke down how to master Claude Code (even if you haven't coded before): 05:28 - Level 1: Why you start with Lovable 08:04 - Level 2: The Lovable + Claude Code bridge 28:37 - Level 3: Cursor + Vercel for real production 41:17 - Level 4: Agents, skills, and CLAUDE.md 42:50 - The CLAUDE.md memory file explained 45:24 - The PM orchestrator agent pattern 53:26 - How AI-native teams spend 50% of their time 01:01:33 - Why 90% of European PMs are still non-technical 01:07:45 - The Monday morning move

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.Ramp Labs (the AI unit of Ramp) has been *cooking* with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories

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67,624 görüntüleme • 2 ay önce

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.
0:22

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

118,124 görüntüleme • 1 ay önce

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper 📧

66,339 görüntüleme • 2 ay önce

A NEW AI VIDEO EDITING TOOL JUST DROPPED THAT WORKS LIKE A REAL VIDEO EDITING TEAM INSTEAD OF A CLIP GENERATOR it's called agent opus and it's not one model doing everything. it's a team of specialized agents handing work to each other like an actual production studio. you type a prompt, attach references (links, PDFs, images), pick a format, and they take it from there: > @ researcher pulls facts from your URLs and docs > @ scriptwriter drafts the transcript > @ storyboardartist breaks it into scenes > @ hookdesigner generates 3 viral hooks for you to pick from > @ assetmanager collects the visuals > @ motiondesigner builds the animations > @ voiceactor handles narration > @ videoeditor cuts the footage and polishes timings you stay in the loop the whole way. it shows you the plan, waits for your sign-off, lets you pick the hook, then assembles the final scenes. prompt box options: vertical format, voice, AI twin, and style match (so it can copy an existing video's look). the bigger picture: the multi-agent pattern that took over coding tools (claude code, codex) just landed in video production. each agent owns one role and only its role. the real read: the bottleneck for AI video was never the clip generation. it was the research, scripting, hooks, and editing decisions. agent opus is the first tool that breaks those into named specialists instead of stuffing them all into one bloated model. link in replies 👇
0:44

A NEW AI VIDEO EDITING TOOL JUST DROPPED THAT WORKS LIKE A REAL VIDEO EDITING TEAM INSTEAD OF A CLIP GENERATOR it's called agent opus and it's not one model doing everything. it's a team of specialized agents handing work to each other like an actual production studio. you type a prompt, attach references (links, PDFs, images), pick a format, and they take it from there: > @ researcher pulls facts from your URLs and docs > @ scriptwriter drafts the transcript > @ storyboardartist breaks it into scenes > @ hookdesigner generates 3 viral hooks for you to pick from > @ assetmanager collects the visuals > @ motiondesigner builds the animations > @ voiceactor handles narration > @ videoeditor cuts the footage and polishes timings you stay in the loop the whole way. it shows you the plan, waits for your sign-off, lets you pick the hook, then assembles the final scenes. prompt box options: vertical format, voice, AI twin, and style match (so it can copy an existing video's look). the bigger picture: the multi-agent pattern that took over coding tools (claude code, codex) just landed in video production. each agent owns one role and only its role. the real read: the bottleneck for AI video was never the clip generation. it was the research, scripting, hooks, and editing decisions. agent opus is the first tool that breaks those into named specialists instead of stuffing them all into one bloated model. link in replies 👇

Creators Toolbox

25,447 görüntüleme • 3 ay önce

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):
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I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):

Matt Dancho (Business Science)

31,079 görüntüleme • 1 yıl önce

Noah Brier (Noah Brier) uses Claude Code as his second brain—it’s the coolest notetaking setup I’ve ever seen. He has Claude running on a server in his basement hooked up to a VPN. It stores, reads, and writes to thousands of notes in his Obsidian (Obsidian) vault. He does it all from his phone. I had him on the show to tell us exactly how he’s pulling this off. We get into: - The nuts and bolts of the Claude Code-Obsidian setup: Noah set up Claude Code on top of his Obsidian root directory, and he walked me through how he uses it to prep for an upcoming speech—creating a project folder, pulling in relevant research from his notes, saving transcripts from chats with other LLMs, and generating daily progress updates. - The “thinking partner” that lives inside Noah’s second brain: Noah points out that in the hype around AI’s ability to write, the fact that it can read is overlooked. That’s why he has an agent inside Claude Code with strict guardrails to stay in “thinking mode.” It logs his questions, tracks insights, and catches him up on research if he returns to a project after a few days away. - How Noah does deep work on his phone: Noah rigged a home server in his basement, put his Obsidian vault in it—and then runs Claude Code on top. Noah says that being able to think, write, research, and ship code from his phone has fundamentally changed the way he works. This episode of Every 📧’s AI & I is a must-watch for anyone curious about who wants to learn how to use Claude Code to build a true second brain. Watch below! Timestamps: Introduction: 00:01:19 How you can do deep work on your phone: 00:04:28 Why Noah thinks Grok has the best voice AI: 00:06:14 The nuts and bolts of Noah’s Claude Code-Obsidian setup: 00:11:39 Using an agent in Claude Code as a “thinking partner”: 00:23:59 Noah’s Thomas’ English Muffin theory of AI: 00:35:07 The white space still left to explore in AI: 00:44:04 How Noah is preparing his kids for AI: 00:50:41 How he brought his Claude Code setup to mobile: 01:01:54
1:11:52

Noah Brier (Noah Brier) uses Claude Code as his second brain—it’s the coolest notetaking setup I’ve ever seen. He has Claude running on a server in his basement hooked up to a VPN. It stores, reads, and writes to thousands of notes in his Obsidian (Obsidian) vault. He does it all from his phone. I had him on the show to tell us exactly how he’s pulling this off. We get into: - The nuts and bolts of the Claude Code-Obsidian setup: Noah set up Claude Code on top of his Obsidian root directory, and he walked me through how he uses it to prep for an upcoming speech—creating a project folder, pulling in relevant research from his notes, saving transcripts from chats with other LLMs, and generating daily progress updates. - The “thinking partner” that lives inside Noah’s second brain: Noah points out that in the hype around AI’s ability to write, the fact that it can read is overlooked. That’s why he has an agent inside Claude Code with strict guardrails to stay in “thinking mode.” It logs his questions, tracks insights, and catches him up on research if he returns to a project after a few days away. - How Noah does deep work on his phone: Noah rigged a home server in his basement, put his Obsidian vault in it—and then runs Claude Code on top. Noah says that being able to think, write, research, and ship code from his phone has fundamentally changed the way he works. This episode of Every 📧’s AI & I is a must-watch for anyone curious about who wants to learn how to use Claude Code to build a true second brain. Watch below! Timestamps: Introduction: 00:01:19 How you can do deep work on your phone: 00:04:28 Why Noah thinks Grok has the best voice AI: 00:06:14 The nuts and bolts of Noah’s Claude Code-Obsidian setup: 00:11:39 Using an agent in Claude Code as a “thinking partner”: 00:23:59 Noah’s Thomas’ English Muffin theory of AI: 00:35:07 The white space still left to explore in AI: 00:44:04 How Noah is preparing his kids for AI: 00:50:41 How he brought his Claude Code setup to mobile: 01:01:54

Dan Shipper 📧

30,792 görüntüleme • 10 ay önce

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch
58:55

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

375,365 görüntüleme • 4 ay önce

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base
1:47

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Malte Ubl

124,713 görüntüleme • 7 ay önce

Everyone and their mom is using claude code, so how do you stand out? Well, you learn how to use claude code like a senior software engineer. John Lindquist (AI DX Vercel) joins this week at How I AI to walk us through his flows, hooks, and tools that supercharge his cc instance. You'll learn how to - use mermaid diagrams for context - alias your favorite AI commands - write hooks for code quality We also debate TUI vs IDE, discuss esoteric file formats, and build a couple things live. Whether you're an advanced user or CC or wanting to get there, this is the ep for you. ty ty ty to our awesome sponsors 💼 WorkOS- Make your app enterprise-ready today 🔀 Tines - start building intelligent workflows today Watch it here:
1:06

Everyone and their mom is using claude code, so how do you stand out? Well, you learn how to use claude code like a senior software engineer. John Lindquist (AI DX Vercel) joins this week at How I AI to walk us through his flows, hooks, and tools that supercharge his cc instance. You'll learn how to - use mermaid diagrams for context - alias your favorite AI commands - write hooks for code quality We also debate TUI vs IDE, discuss esoteric file formats, and build a couple things live. Whether you're an advanced user or CC or wanting to get there, this is the ep for you. ty ty ty to our awesome sponsors 💼 WorkOS- Make your app enterprise-ready today 🔀 Tines - start building intelligent workflows today Watch it here:

claire vo 🖤

26,050 görüntüleme • 6 ay önce