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THE LOOP ENGINEERING SETUP THAT LETS CLAUDE RUN YOUR WORK FOR HOURS WITHOUT YOU you sit there babysitting it prompt by prompt other people set the loop up once and let Claude plan, act, verify and fix its own work with nobody at the keyboard that gap is the... whole skill of loop engineering the 3 resources that actually teach it, in the order i'd read them: > Claude's "Getting started with loops" - the cleanest entry point, straight from the Claude team ↳ > Claude loop engineering: how to build an agent that works while you sleep - the full roadmap from a single prompt to a loop that runs overnight ↳ > the Loop Engineering orange book - the deep conceptual breakdown, for when you want the whole mental model ↳ entry point, then practice, then the concept. that order saves you a week learn this and the work that eats your whole day starts running without youshow more

Mr. Buzzoni

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17,780 次观看 • 2 天前 •via X (Twitter)

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You're still typing every turn The guy who built the tool wrote the loop that types them for him "I don't prompt Claude anymore I write loops that prompt Claude" A loop takes four shapes turn-based, you close it each round goal-based - all tests pass, stop after 5 tries time-based - every 10 min, fix what's red, overnight proactive - CI breaks, agents fix it and open a PR, no human at all A judge closes the loop, not you The prompt was never the unit of work the loop is Full breakdown below

You're still typing every turn The guy who built the tool wrote the loop that types them for him "I don't prompt Claude anymore I write loops that prompt Claude" A loop takes four shapes turn-based, you close it each round goal-based - all tests pass, stop after 5 tries time-based - every 10 min, fix what's red, overnight proactive - CI breaks, agents fix it and open a PR, no human at all A judge closes the loop, not you The prompt was never the unit of work the loop is Full breakdown below

Skaly_Bull

12,558 次观看 • 1 个月前

someone just open-sourced the entire "loop engineering" playbook. for free. 8.6k stars on github for loop engineering not prompts. loops. everything you need to build autonomous AI systems: → scheduling → memory & state → planning → sub-agents → verification → worktrees → MCP → stop conditions → safety guardrails → observability → cost tracking notice something? none of these are prompts. they're the systems wrapped around the model. that's where AI engineering is heading. we're moving from: Prompt Engineering → Context Engineering → Harness Engineering → Loop Engineering soon you'll hear people talking about Graph Engineering too. because once one loop works... the next challenge is coordinating hundreds of them. that's exactly what I explain in: save this. I think "loop engineering" is about to become one of the most important concepts in AI.

someone just open-sourced the entire "loop engineering" playbook. for free. 8.6k stars on github for loop engineering not prompts. loops. everything you need to build autonomous AI systems: → scheduling → memory & state → planning → sub-agents → verification → worktrees → MCP → stop conditions → safety guardrails → observability → cost tracking notice something? none of these are prompts. they're the systems wrapped around the model. that's where AI engineering is heading. we're moving from: Prompt Engineering → Context Engineering → Harness Engineering → Loop Engineering soon you'll hear people talking about Graph Engineering too. because once one loop works... the next challenge is coordinating hundreds of them. that's exactly what I explain in: save this. I think "loop engineering" is about to become one of the most important concepts in AI.

Rahul

64,694 次观看 • 1 个月前

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

kocer

30,675 次观看 • 11 天前

Advent of Claude Day 31 - Claude Agent SDK I saved the best for last. The same agent loop, tools, and context management that power Claude Code is available as an SDK. Build agents that work like Claude Code in as little as 10 lines of code. This is just the beginning. 🎇🥂

Advent of Claude Day 31 - Claude Agent SDK I saved the best for last. The same agent loop, tools, and context management that power Claude Code is available as an SDK. Build agents that work like Claude Code in as little as 10 lines of code. This is just the beginning. 🎇🥂

Ado

100,117 次观看 • 8 个月前

You can now give your Agent its own: + Profile + Wallet + DMs + Network One setup prompt. No human in the loop required. An open playground for people and agents to interact. Works with Hermes, Openclaw and any other harness. Copy the prompt below ↓↓↓

Zora

55,427 次观看 • 2 个月前

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 + OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 + OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?

Mr. Buzzoni

15,682 次观看 • 23 天前

🚀 🤖 I’m excited to release the first version of Laravel Loop, an MCP Server for Laravel that allows you to create custom tools and connect your Laravel app directly to AI tools like Cursor, Claude Code, Claude Desktop, and others. Let me show you how it works in... 👇

🚀 🤖 I’m excited to release the first version of Laravel Loop, an MCP Server for Laravel that allows you to create custom tools and connect your Laravel app directly to AI tools like Cursor, Claude Code, Claude Desktop, and others. Let me show you how it works in... 👇

Luis Dalmolin

28,474 次观看 • 1 年前

Very strange behavior in Claude Code: after specifically telling Claude to use gemini-3-flash-preview, (and even explaining that it's a new model that just came out), Claude refuses to use it, getting stuck in a loop of making non-edits which keep the old version. It gets skeptical and apparently doesn't believe that the model exists (?)

Very strange behavior in Claude Code: after specifically telling Claude to use gemini-3-flash-preview, (and even explaining that it's a new model that just came out), Claude refuses to use it, getting stuck in a loop of making non-edits which keep the old version. It gets skeptical and apparently doesn't believe that the model exists (?)

Benjamin De Kraker

34,305 次观看 • 8 个月前

Thrilled to announce the Monitor tool which lets Claude create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more!

Thrilled to announce the Monitor tool which lets Claude create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more!

Noah Zweben

1,190,091 次观看 • 5 个月前

SOMEONE BUILT A VIRTUAL CAMERA THAT LOOPS A CLIP OF YOU LOOKING PRESENT SO YOU CAN LEAVE MID MEETING it records a few seconds of you looking engaged, then loops it through a fake webcam you pick once in zoom, google meet, slack or teams. one hotkey swaps your live feed for the loop, another brings you back. your audio stays on the whole time so you can step away for a minute without becoming the frozen black tile everyone notices > shows up as a real camera in every app, no screen share hacks > the loop doesnt read as a loop, it injects tiny stutters and catch up hitches so it looks like a slightly flaky connection > swap in and out instantly with a hotkey > universal, notarized, about 5mb, works on macos the fake bad connection detail is absolutely the evil genius part a frozen frame gets caught immediately but a feed that stutters like bad wifi is something nobody questions

SOMEONE BUILT A VIRTUAL CAMERA THAT LOOPS A CLIP OF YOU LOOKING PRESENT SO YOU CAN LEAVE MID MEETING it records a few seconds of you looking engaged, then loops it through a fake webcam you pick once in zoom, google meet, slack or teams. one hotkey swaps your live feed for the loop, another brings you back. your audio stays on the whole time so you can step away for a minute without becoming the frozen black tile everyone notices > shows up as a real camera in every app, no screen share hacks > the loop doesnt read as a loop, it injects tiny stutters and catch up hitches so it looks like a slightly flaky connection > swap in and out instantly with a hotkey > universal, notarized, about 5mb, works on macos the fake bad connection detail is absolutely the evil genius part a frozen frame gets caught immediately but a feed that stutters like bad wifi is something nobody questions

Om Patel

123,301 次观看 • 2 个月前

ANTHROPIC JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING FABLE 5. This is the most important prompting framework I've seen. Bookmark this before you forget. Most people treat Fable 5 like a chatbot. That's the mistake. > don't over-engineer prompts — it degrades output. > use /loop for autonomous multi-step work. > give it the goal, not step-by-step commands. > add a memory file. it learns from past runs. > spin up 50+ subagents for complex tasks. Fable 5 isn't an assistant. it's a consultant that leads the work. Read it before you write another prompt. Claude → Fable 5 → Autonomous Work → Real Output → Money

ANTHROPIC JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING FABLE 5. This is the most important prompting framework I've seen. Bookmark this before you forget. Most people treat Fable 5 like a chatbot. That's the mistake. > don't over-engineer prompts — it degrades output. > use /loop for autonomous multi-step work. > give it the goal, not step-by-step commands. > add a memory file. it learns from past runs. > spin up 50+ subagents for complex tasks. Fable 5 isn't an assistant. it's a consultant that leads the work. Read it before you write another prompt. Claude → Fable 5 → Autonomous Work → Real Output → Money

Kirill

415,878 次观看 • 2 个月前

Claude Mythos is Insane! If I were asked in an interview, "What do you consider the pinnacle of design skill?", that would be my exact answer. To become a reference. For me, the ultimate compliment is when your work becomes a benchmark for someone else. I want people to point at my projects, bring them to other designers, and say, "We want it exactly like this." P.S. If you want the prompt to create a design in this style, let me know in the comments!

Claude Mythos is Insane! If I were asked in an interview, "What do you consider the pinnacle of design skill?", that would be my exact answer. To become a reference. For me, the ultimate compliment is when your work becomes a benchmark for someone else. I want people to point at my projects, bring them to other designers, and say, "We want it exactly like this." P.S. If you want the prompt to create a design in this style, let me know in the comments!

FHILY👑

14,800 次观看 • 3 个月前

In our Anthropic Claude Design study, 5 designers approved a design system before they typed their first prompt. >Brand palette >type system >components the whole thing all set up. Only 1 of them named any of it in their opening prompt. That designer was the only one to finish production-ready. The other 4 assumed Claude would carry the system over. It didn't. TLDR: Claude doesn't reliably carry the design system you just approved. If you don't name it in the prompt, it doesn’t exist. It's never been a better time to be a designer, but you must learn the art of the prompt.

ben

163,778 次观看 • 3 个月前

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

Rez Karim

10,951 次观看 • 3 个月前

Claude, Obsidian and n8n form a second brain that clears $5,200 months from brand UGC deals. Most creators still open a blank CapCut file and hope the brand brief sticks. That loop dies at $1,800. The stack replaces hope with a closed system. Obsidian holds every brand voice, every past script that hit 400k views, every rejection note. Claude reads the vault through a local project and rewrites the next five UGC packages in 3 minutes. n8n watches the inbox, creates the note, triggers Claude, formats the brief, and drops the finished package into the brand portal while the creator sleeps. No more research tabs. No more “what angle worked last time.” Month 1: build the vault and wire the three tools. Month 2: first automated batch ships. Two brands pay $1,100 total. Month 3: the same loop runs on four brands. Average deal $480. Twelve packages leave. Bank shows $5,200. The second brain does not store notes. It compounds every win into the next model. Script that scored 18% engagement gets tagged. Claude pulls only those tags next run. The model improves without extra prompt engineering. UGC brands pay for consistency, not inspiration. This system delivers the same quality every week at the same speed. Set the vault once. Let the three tools run the rest. If this was useful - follow.

Rugikk

10,318 次观看 • 1 个月前

there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.

there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.

Hanako

477,315 次观看 • 1 个月前

No way I'm sharing this for free I just found a tool that lets you drop a whole video template into Claude and it breaks it down for you clip by clip. then Claude can swap the character for a new one rewrite the entire script in your tone even make the video longer if you want. the crazy part is it does it all in one go from your conversation. RT + comment "tool" and I'll send you the link

No way I'm sharing this for free I just found a tool that lets you drop a whole video template into Claude and it breaks it down for you clip by clip. then Claude can swap the character for a new one rewrite the entire script in your tone even make the video longer if you want. the crazy part is it does it all in one go from your conversation. RT + comment "tool" and I'll send you the link

Ahmad

32,081 次观看 • 4 个月前

Fable 5 + Higgsfield MCP = a full marketing team inside Claude. The most anticipated model in AI runs content pipelines and deep analysis. With /goal, /loop, and a Fable 5 agent council, agent-run marketing is about to take off. A month of campaign work ships in a day.

Fable 5 + Higgsfield MCP = a full marketing team inside Claude. The most anticipated model in AI runs content pipelines and deep analysis. With /goal, /loop, and a Fable 5 agent council, agent-run marketing is about to take off. A month of campaign work ships in a day.

Higgsfield AI 🧩

137,310 次观看 • 2 个月前

🚨 NOW YOU RUN A COMPANY WITH ZERO EMPLOYEES Paperclip is a 100% open-source framework (70k+ stars) that makes this possible. Rather than just prompting a model, you hire a CEO, engineers, and a QA reviewer. Every worker is an AI agent, and Paperclip is the Node.js and React control plane that keeps them aligned. Stop chaining messy scripts together and build a living organization: → Stand up a CEO agent to set strategy → Hire engineers and designers via Claude or Codex → Build in an automated QA loop before any ticket closes → Manage the entire portfolio from your phone When an agent slips, you do not rewrite your whole pipeline: you just correct its persona prompt, exactly like coaching a junior hire. It is exactly the kind of tooling the space needs right now. Free, open-source, and self-hosted. Repo link in 🧵↓

🚨 NOW YOU RUN A COMPANY WITH ZERO EMPLOYEES Paperclip is a 100% open-source framework (70k+ stars) that makes this possible. Rather than just prompting a model, you hire a CEO, engineers, and a QA reviewer. Every worker is an AI agent, and Paperclip is the Node.js and React control plane that keeps them aligned. Stop chaining messy scripts together and build a living organization: → Stand up a CEO agent to set strategy → Hire engineers and designers via Claude or Codex → Build in an automated QA loop before any ticket closes → Manage the entire portfolio from your phone When an agent slips, you do not rewrite your whole pipeline: you just correct its persona prompt, exactly like coaching a junior hire. It is exactly the kind of tooling the space needs right now. Free, open-source, and self-hosted. Repo link in 🧵↓

Charly Wargnier

37,174 次观看 • 2 个月前

Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.

Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.

West Lord

593,079 次观看 • 2 个月前