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Shen Sean Chen

@ShenSeanChen • 4,217 subscribers

founder @auto_manus (funded by character vc) • pokémon/onepiece fan • ex-google/mit • youtube(26k) https://t.co/rMP0jaxHK1 • git https://t.co/sPSpu8yPx6

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I dug into Pi because the greatest projects are usually built on top of one very simple building block. 𝗣𝗶 𝗵𝗮𝘀 𝟳𝟳,𝟬𝟬𝟬 𝗚𝗶𝘁𝗛𝘂𝗯 𝘀𝘁𝗮𝗿𝘀 𝗮𝗻𝗱 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗳𝗼𝘂𝗿 𝘁𝗼𝗼𝗹𝘀: 𝗿𝗲𝗮𝗱, 𝘄𝗿𝗶𝘁𝗲, 𝗲𝗱𝗶𝘁, 𝗯𝗮𝘀𝗵. 𝗦𝘂𝗽𝗲𝗿 𝘀𝗶𝗺𝗽𝗹𝗲. No MCP, no sub agents, no plan mode, no to-dos, no permission popups. Their site has a whole section called "what we did not build." So here's the whole logic by Mario Zechner: prompt in → agents.md + system prompt become one forkable JSON → loop starts → read / write / edit / bash → session saved as a tree → loop ends agent-loop.ts has only 792 lines. I think it's beautiful. Initially I was wondering if it's gonna be a little bit too basic. And yes out of the box it's weaker than Claude Code. But skipping MCP isn't purity, it's a context budget. One MCP can sit tens of thousands of tokens in your window every single turn, for a tool you use maybe 10% of the time. Pi moves that weight from always on to on demand. A skill is just markdown. An extension is one .ts file. A package ships both. I ran it in the terminal, then again as a coding sub agent inside my own harness Waku-Agent ( through a delegate_task tool. Same binary both times. You're paying setup time for a harness that stays yours. Full 22 min breakdown in the comments. Save the diagram 🔖 You Can Build Anything. You Can Learn Anything. 💪

I dug into Pi because the greatest projects are usually built on top of one very simple building block. 𝗣𝗶 𝗵𝗮𝘀 𝟳𝟳,𝟬𝟬𝟬 𝗚𝗶𝘁𝗛𝘂𝗯 𝘀𝘁𝗮𝗿𝘀 𝗮𝗻𝗱 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗳𝗼𝘂𝗿 𝘁𝗼𝗼𝗹𝘀: 𝗿𝗲𝗮𝗱, 𝘄𝗿𝗶𝘁𝗲, 𝗲𝗱𝗶𝘁, 𝗯𝗮𝘀𝗵. 𝗦𝘂𝗽𝗲𝗿 𝘀𝗶𝗺𝗽𝗹𝗲. No MCP, no sub agents, no plan mode, no to-dos, no permission popups. Their site has a whole section called "what we did not build." So here's the whole logic by Mario Zechner: prompt in → agents.md + system prompt become one forkable JSON → loop starts → read / write / edit / bash → session saved as a tree → loop ends agent-loop.ts has only 792 lines. I think it's beautiful. Initially I was wondering if it's gonna be a little bit too basic. And yes out of the box it's weaker than Claude Code. But skipping MCP isn't purity, it's a context budget. One MCP can sit tens of thousands of tokens in your window every single turn, for a tool you use maybe 10% of the time. Pi moves that weight from always on to on demand. A skill is just markdown. An extension is one .ts file. A package ships both. I ran it in the terminal, then again as a coding sub agent inside my own harness Waku-Agent ( through a delegate_task tool. Same binary both times. You're paying setup time for a harness that stays yours. Full 22 min breakdown in the comments. Save the diagram 🔖 You Can Build Anything. You Can Learn Anything. 💪

59,186 views • 1 month ago

𝗜 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝗺𝘆 𝗹𝗼𝗰𝗮𝗹 𝗔𝗜 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁: 𝗔𝗴𝗲𝗻𝘁 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 & 𝗟𝗼𝗼𝗽 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗰𝗼𝗱𝗲. Everything in real code you can read in an afternoon: Harness, Loop Engineering, Memory, Eval, Tracing. 100% local on your laptop. 💻 Github Repo: ☕️ Buy me a coffee: The whole system is one loop: message in → retrieval gate → agent run → tools fire → trace → eval → memory saved → skills grow - The loop is ~95 lines of plain Python - Your memory is one SQLite file. Open it. It's yours. - Evals built in: deterministic + LLM-as-judge, with a release gate - Telegram gateway, voice wake word, Apple Calendar tools In the video it books the World Cup QF on my calendar, remembers who my friends are, and answers me on Telegram. Clone it, star it, break it, PR it. Repo + 20-min walkthrough in the first reply 👇 Watch it, then save the architecture diagram below. 🔖 You Can Build Anything. You Can Learn Anything. 💪

𝗜 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝗺𝘆 𝗹𝗼𝗰𝗮𝗹 𝗔𝗜 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁: 𝗔𝗴𝗲𝗻𝘁 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 & 𝗟𝗼𝗼𝗽 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗰𝗼𝗱𝗲. Everything in real code you can read in an afternoon: Harness, Loop Engineering, Memory, Eval, Tracing. 100% local on your laptop. 💻 Github Repo: ☕️ Buy me a coffee: The whole system is one loop: message in → retrieval gate → agent run → tools fire → trace → eval → memory saved → skills grow - The loop is ~95 lines of plain Python - Your memory is one SQLite file. Open it. It's yours. - Evals built in: deterministic + LLM-as-judge, with a release gate - Telegram gateway, voice wake word, Apple Calendar tools In the video it books the World Cup QF on my calendar, remembers who my friends are, and answers me on Telegram. Clone it, star it, break it, PR it. Repo + 20-min walkthrough in the first reply 👇 Watch it, then save the architecture diagram below. 🔖 You Can Build Anything. You Can Learn Anything. 💪

44,017 views • 1 month ago

I'm open sourcing a template I built to launch a full-stack SaaS with Stripe Payment, NextJS, Supabase and Google OAuth, in ONE SHOT. It took me some time so I decided to share this to you all. The goal is to help everyone save time when launching your next SaaS, with EVERYTHING YOU NEED. You can build anything. You can learn anything. 💪 Github Repo: Try The App: Video Link: Template Features: 🔐 Authentication with Supabase 💳 Stripe payment integration 🌓 Dark mode support 📱 Responsive design 🎨 Tailwind CSS styling 🔄 Framer Motion animations 🛡️ TypeScript support 📊 Error boundary implementation 🔍 SEO optimized

I'm open sourcing a template I built to launch a full-stack SaaS with Stripe Payment, NextJS, Supabase and Google OAuth, in ONE SHOT. It took me some time so I decided to share this to you all. The goal is to help everyone save time when launching your next SaaS, with EVERYTHING YOU NEED. You can build anything. You can learn anything. 💪 Github Repo: Try The App: Video Link: Template Features: 🔐 Authentication with Supabase 💳 Stripe payment integration 🌓 Dark mode support 📱 Responsive design 🎨 Tailwind CSS styling 🔄 Framer Motion animations 🛡️ TypeScript support 📊 Error boundary implementation 🔍 SEO optimized

189,715 views • 1 year ago

You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG. They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems. 🎬YouTube: Here's the whole thing in one picture. An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running. The harness is the set of tools you put on that horse so it runs where you want. Memory gives it context: who you are, what happened before, how to act. The loop lets it call tools again and again, with guardrails so it knows when to stop. Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself. Master these four and you can read almost any AI agent repo or paper and actually know what's going on. You Can Build Anything. You Can Learn Anything. 💪 Chapters: Intro: the 4 AI agent buzzwords What an AI agent run actually is The memory system: procedural, semantic, episodic What "harness" really means (the horse) Storing and updating memory (databases, skills, summarizer agent) Retrieval: RAG, SQL vs semantic search Tool calling and why agents loop Loop engineering and end-loop guardrails A Claude Code hooks example Eval and LLM Ops: why you need them Tracing every run (Langfuse, LangSmith) Evaluation: LLM as a judge Diagnosing what broke The gate: ship the fix or fix the bug Zoom out: the full system

You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG. They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems. 🎬YouTube: Here's the whole thing in one picture. An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running. The harness is the set of tools you put on that horse so it runs where you want. Memory gives it context: who you are, what happened before, how to act. The loop lets it call tools again and again, with guardrails so it knows when to stop. Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself. Master these four and you can read almost any AI agent repo or paper and actually know what's going on. You Can Build Anything. You Can Learn Anything. 💪 Chapters: Intro: the 4 AI agent buzzwords What an AI agent run actually is The memory system: procedural, semantic, episodic What "harness" really means (the horse) Storing and updating memory (databases, skills, summarizer agent) Retrieval: RAG, SQL vs semantic search Tool calling and why agents loop Loop engineering and end-loop guardrails A Claude Code hooks example Eval and LLM Ops: why you need them Tracing every run (Langfuse, LangSmith) Evaluation: LLM as a judge Diagnosing what broke The gate: ship the fix or fix the bug Zoom out: the full system

16,257 views • 2 months ago

I made probably the simplest MCP demo to show you what it actually does, which was open sourced by Anthropic in Nov last year (Yes I'm a little late to the game). I think this is a cool tech and will potentially save us a lot of time coz it allows tools and agents to communicate much more conveniently. Github Repo: YouTube: Buy Me A Coffee: You can build anything. You can learn anything. 💪

I made probably the simplest MCP demo to show you what it actually does, which was open sourced by Anthropic in Nov last year (Yes I'm a little late to the game). I think this is a cool tech and will potentially save us a lot of time coz it allows tools and agents to communicate much more conveniently. Github Repo: YouTube: Buy Me A Coffee: You can build anything. You can learn anything. 💪

26,066 views • 1 year ago

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