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Ex-Google engineer built graphs for multi-agentic loops that saved him 10 days of work - in 60 min: here's how with Claude from scratch: step 1 → run git worktree add ../task-1 -b task-1 then open a terminal, cd into it, and run Claude Code - repeat 5 times...

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codila

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18,235 次观看 • 3 天前 •via X (Twitter)

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Claude Code costs $200/month here's how to run it for free. 4 commands. 10 minutes step 1 - install Ollama runs AI models locally → download from → install. it runs in background on localhost:11434 step 2 - download a coding model → ollama pull qwen2.5-coder:14b (7b for 8GB RAM, 32b for 32GB+ RAM) step 3 - redirect Claude Code to your machine → export ANTHROPIC_BASE_URL= → export ANTHROPIC_MODEL=qwen2.5-coder:14b step 4 - launch → claude → "make a hello world website" → it reads files, writes code, runs commands. locally. free same agent loop. same tools. $0/month the $200 was for convenience the capability was always free like + bookmark - you'll need this
1:41

Claude Code costs $200/month here's how to run it for free. 4 commands. 10 minutes step 1 - install Ollama runs AI models locally → download from → install. it runs in background on localhost:11434 step 2 - download a coding model → ollama pull qwen2.5-coder:14b (7b for 8GB RAM, 32b for 32GB+ RAM) step 3 - redirect Claude Code to your machine → export ANTHROPIC_BASE_URL= → export ANTHROPIC_MODEL=qwen2.5-coder:14b step 4 - launch → claude → "make a hello world website" → it reads files, writes code, runs commands. locally. free same agent loop. same tools. $0/month the $200 was for convenience the capability was always free like + bookmark - you'll need this

self.dll

177,814 次观看 • 3 个月前

I've built a team of AI agents to run my business. Here's the exact order I follow: Step 1: Pick a role (executive assistant, head of marketing, etc.) Step 2: Open Claude Code and interview your way into context files "Ask me questions to build this out" Step 3: Build skills through daily use - Daily briefs - Meeting prep - Custom workflows Step 4: Get it all working reliably first. Step 5: THEN migrate to autonomous harnesses like OpenClaw. Most people jump straight to Step 5. That's why their agents break. Start with one agent. One role. Get it right. Then scale
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I've built a team of AI agents to run my business. Here's the exact order I follow: Step 1: Pick a role (executive assistant, head of marketing, etc.) Step 2: Open Claude Code and interview your way into context files "Ask me questions to build this out" Step 3: Build skills through daily use - Daily briefs - Meeting prep - Custom workflows Step 4: Get it all working reliably first. Step 5: THEN migrate to autonomous harnesses like OpenClaw. Most people jump straight to Step 5. That's why their agents break. Start with one agent. One role. Get it right. Then scale

The Startup Ideas Podcast (SIP) 🧃

26,691 次观看 • 4 个月前

ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.
16:29

ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.

Yarchi

460,284 次观看 • 1 个月前

Here's how to do months of SEO work in one afternoon with Claude Code: Step 1: Feed AI your site, ask for 25-50 keywords Step 2: Filter for buying intent (who's ready to call NOW) Step 3: Build a landing page for each keyword + location Step 4: Run an SEO audit ("ultra think" goes deep) Step 5: Fix technical issues (sitemap, schema, speed) Step 6: Spin up sub-agents to research competitors while you work THE KEY: One afternoon of Claude Code > 6 months with an agency. Go vibe market something.
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Here's how to do months of SEO work in one afternoon with Claude Code: Step 1: Feed AI your site, ask for 25-50 keywords Step 2: Filter for buying intent (who's ready to call NOW) Step 3: Build a landing page for each keyword + location Step 4: Run an SEO audit ("ultra think" goes deep) Step 5: Fix technical issues (sitemap, schema, speed) Step 6: Spin up sub-agents to research competitors while you work THE KEY: One afternoon of Claude Code > 6 months with an agency. Go vibe market something.

The Startup Ideas Podcast (SIP) 🧃

84,754 次观看 • 6 个月前

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

darkzodchi

841,421 次观看 • 2 个月前

How to set up Claude Skills in 3 min (for complete beginners) Step 1: Create a markdown file with your EXACT process Step 2: Add reference docs (brand guidelines, formulas) Step 3: Write deterministic scripts for calculations Step 4: Deploy once → Same output forever Result: No more prompt engineering. No more hallucinations. Tools: Claude Skills (not basic prompting) Cost: $20/month Time: 30 min setup → Save Hours of Prompting You're one Skill away from building a 10x AI Agent Use this system, you won't regret it
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How to set up Claude Skills in 3 min (for complete beginners) Step 1: Create a markdown file with your EXACT process Step 2: Add reference docs (brand guidelines, formulas) Step 3: Write deterministic scripts for calculations Step 4: Deploy once → Same output forever Result: No more prompt engineering. No more hallucinations. Tools: Claude Skills (not basic prompting) Cost: $20/month Time: 30 min setup → Save Hours of Prompting You're one Skill away from building a 10x AI Agent Use this system, you won't regret it

The Startup Ideas Podcast (SIP) 🧃

35,497 次观看 • 8 个月前

Andrew Ng built agentic knowledge graph that runs agents loops from scratch: his agents searched thousands of tools through the graph, found the "right" one by meaning, and called it agent efficiency increased by 90% here's how: step 1 → embed every tool, search by meaning - not just keywords step 2 → but "similar" ≠ "correct" - it grabs the wrong one, skips the step before it step 3 → map tools as a graph - nodes are tools, edges are what runs before what step 4 → retrieve the path, not one tool - every tool the task needs, in order step 5 → bake the process into the graph - the sequence becomes data, not a guess most agents fail here and no one knows why - 1-hour brilliant course about it watch & bookmark - then read full course about graph engineering ↓
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Andrew Ng built agentic knowledge graph that runs agents loops from scratch: his agents searched thousands of tools through the graph, found the "right" one by meaning, and called it agent efficiency increased by 90% here's how: step 1 → embed every tool, search by meaning - not just keywords step 2 → but "similar" ≠ "correct" - it grabs the wrong one, skips the step before it step 3 → map tools as a graph - nodes are tools, edges are what runs before what step 4 → retrieve the path, not one tool - every tool the task needs, in order step 5 → bake the process into the graph - the sequence becomes data, not a guess most agents fail here and no one knows why - 1-hour brilliant course about it watch & bookmark - then read full course about graph engineering ↓

codila

16,276 次观看 • 7 天前

You can build your own custom Jarvis, using Cursor, Claude Code or Codex, in less than 20 minutes. Using the new GPT-Realtime-2 API... Anything you can do on a computer can be turned into a tool that your Jarvis can use. (Here's exactly how to do it) Introduction Step 1: Cursor Setup Step 2 – Full Jarvis Prompt Step 3 – My Agent Ricky Step 4 – Second Prompt Step 5 – Add EXA API Key for Web Search Step 6 – Third Prompt Step 7 – Add Computer Use Mode Step 8 – Add AI Image Generator (Insane) Final Demo
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You can build your own custom Jarvis, using Cursor, Claude Code or Codex, in less than 20 minutes. Using the new GPT-Realtime-2 API... Anything you can do on a computer can be turned into a tool that your Jarvis can use. (Here's exactly how to do it) Introduction Step 1: Cursor Setup Step 2 – Full Jarvis Prompt Step 3 – My Agent Ricky Step 4 – Second Prompt Step 5 – Add EXA API Key for Web Search Step 6 – Third Prompt Step 7 – Add Computer Use Mode Step 8 – Add AI Image Generator (Insane) Final Demo

Riley Brown

61,931 次观看 • 28 天前

ClawTeam v0.2.0 is here. One CLI to coordinate any coding agent — Claude Code, Codex, OpenClaw, nanobot, and more — into a self‑organizing swarm that plans, builds, and ships together. What's new in v0.2.0: 1) - Gource Visualization — Watch your agent swarm’s Git activity in real time. Clear. Visual. Instant. Run: clawteam board gource --live See every commit, branch, and merge as it happens. Track what each agent is doing. 2) - Runtime Profiles — A provider‑aware configuration system. Switch between Claude, Kimi, and Gemini anytime. No need to edit environment variables. Run clawteam profile wizard. Follow the interactive setup. Done in minutes. 3) - Git-Based Context — Full worktree isolation with built‑in conflict detection and change tracking. Each agent works on its own branch, and the leader can see everything clearly in one place. 4) - Stability & Hardening — Spawn/workspace conflict fixes, improved tmux integration, message normalization, P2P liveness with lease-based detection. This release is about making the foundation rock-solid. --------------------------------------------------------- To show what a coordinated agent swarm can actually do, we ran 1 Claude Code orchestrating 8 Claude Code agents to build a robotics simulation system optimized for Apple Silicon — from scratch. 8 hours. 300+ PRs. One running simulator. Check the result: --------------------------------------------------------- Huge thanks to the open-source community for the feedback, issues, and PRs that shaped this release. ClawTeam is built in the open because we believe multi-agent coordination should be a shared primitive, not a proprietary moat. Try it: pip install clawteam Docs: GitHub: #ClawTeam #nanobot #AIAgents #openclaw #ClaudeCode #Cursor
1:23

ClawTeam v0.2.0 is here. One CLI to coordinate any coding agent — Claude Code, Codex, OpenClaw, nanobot, and more — into a self‑organizing swarm that plans, builds, and ships together. What's new in v0.2.0: 1) - Gource Visualization — Watch your agent swarm’s Git activity in real time. Clear. Visual. Instant. Run: clawteam board gource --live See every commit, branch, and merge as it happens. Track what each agent is doing. 2) - Runtime Profiles — A provider‑aware configuration system. Switch between Claude, Kimi, and Gemini anytime. No need to edit environment variables. Run clawteam profile wizard. Follow the interactive setup. Done in minutes. 3) - Git-Based Context — Full worktree isolation with built‑in conflict detection and change tracking. Each agent works on its own branch, and the leader can see everything clearly in one place. 4) - Stability & Hardening — Spawn/workspace conflict fixes, improved tmux integration, message normalization, P2P liveness with lease-based detection. This release is about making the foundation rock-solid. --------------------------------------------------------- To show what a coordinated agent swarm can actually do, we ran 1 Claude Code orchestrating 8 Claude Code agents to build a robotics simulation system optimized for Apple Silicon — from scratch. 8 hours. 300+ PRs. One running simulator. Check the result: --------------------------------------------------------- Huge thanks to the open-source community for the feedback, issues, and PRs that shaped this release. ClawTeam is built in the open because we believe multi-agent coordination should be a shared primitive, not a proprietary moat. Try it: pip install clawteam Docs: GitHub: #ClawTeam #nanobot #AIAgents #openclaw #ClaudeCode #Cursor

Chao Huang

25,101 次观看 • 4 个月前

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓
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Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓

codila

540,094 次观看 • 15 天前

How to turn Claude 4 into your AI super agent (FREE): Step 1: Download Claude Desktop Step 2: Enable MCP in developer settings Step 3: Configure browser + laptop control Step 4: Give it tasks like "research the latest news" Step 5: Watch it work for hours while you do other things Real example from today: - Asked it to check my free disk space - It responded: "91GB available, 14% used" - Then navigated to websites and gathered data This is what "AI agents" actually means. Not chatbots—full computer control. Save this video, you'll automate everything. Want the SOP? DM me. 💬
1:09:00

How to turn Claude 4 into your AI super agent (FREE): Step 1: Download Claude Desktop Step 2: Enable MCP in developer settings Step 3: Configure browser + laptop control Step 4: Give it tasks like "research the latest news" Step 5: Watch it work for hours while you do other things Real example from today: - Asked it to check my free disk space - It responded: "91GB available, 14% used" - Then navigated to websites and gathered data This is what "AI agents" actually means. Not chatbots—full computer control. Save this video, you'll automate everything. Want the SOP? DM me. 💬

Julian Goldie SEO

32,494 次观看 • 1 年前

How to analyse data in Excel using Claude Opus 4.6 Step 1: Open Excel. Step 2: Click Add-ins in the Home tab Step 3: Search "Claude by Anthropic" Step 4: Install it and open the sidebar Step 5: Select Opus 4.6 (latest model) Step 6: Upload your data (Excel, CSV, image, PDF or anything) Step 7: Prompt: 👇 "I've uploaded my messy bank statement. Here's what I need you to do: Categorise every transaction into groups: income, business expenses, personal spending, subscriptions, taxes, and transfers. Flag any recurring charges I might have forgotten about. Calculate my total income, total spend, and profit margin for each month. Highlight my top 5 spending categories. Spot any unusual or duplicate charges. Then give me everything into a clean Excel spreadsheet with a summary tab and a breakdown tab with charts." Step 8: Wait for 5 minutes.................. - Claude converts messy data (image) into a full financial dashboard
1:05

How to analyse data in Excel using Claude Opus 4.6 Step 1: Open Excel. Step 2: Click Add-ins in the Home tab Step 3: Search "Claude by Anthropic" Step 4: Install it and open the sidebar Step 5: Select Opus 4.6 (latest model) Step 6: Upload your data (Excel, CSV, image, PDF or anything) Step 7: Prompt: 👇 "I've uploaded my messy bank statement. Here's what I need you to do: Categorise every transaction into groups: income, business expenses, personal spending, subscriptions, taxes, and transfers. Flag any recurring charges I might have forgotten about. Calculate my total income, total spend, and profit margin for each month. Highlight my top 5 spending categories. Spot any unusual or duplicate charges. Then give me everything into a clean Excel spreadsheet with a summary tab and a breakdown tab with charts." Step 8: Wait for 5 minutes.................. - Claude converts messy data (image) into a full financial dashboard

Awa K. Penn

45,202 次观看 • 5 个月前

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
0:56

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

159,179 次观看 • 3 个月前

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.
4:43

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.

The Startup Ideas Podcast (SIP) 🧃

14,202 次观看 • 3 个月前

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this
0:42

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

Yarchi

54,840 次观看 • 1 个月前

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 次观看 • 1 个月前

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady
9:37

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady

Avid

635,392 次观看 • 2 个月前

Using Claude Code as a general agent part 3: Getting Claude Code to Ask me clarifying questions... inline, by creating a Clarifying-Questions .md file What's in this file is how the agent should format clarifying questions before doing a specific task. Rather than coming up with ALL of the context I need to give to the agent, I state what I want at the top of the docuemnt then i tell claude to ask me clarifying questions IN THE DOCUMENT. The agent knows to format them as empty code blocks and all i need to do is fill them in then tell Claude Code to finish the task at hand. Kinda niche, but I use this for EVERYTHING now: 1. State what I want at top (Ad Scripts) 2. Type out relevant context that's in my head 3. Tell the agent to ask @ clarifying-questions 4. Quickly answer them with wispr flow in the document 5. Tell Claude to continue This would be useful for nearly every workflow.
1:11

Using Claude Code as a general agent part 3: Getting Claude Code to Ask me clarifying questions... inline, by creating a Clarifying-Questions .md file What's in this file is how the agent should format clarifying questions before doing a specific task. Rather than coming up with ALL of the context I need to give to the agent, I state what I want at the top of the docuemnt then i tell claude to ask me clarifying questions IN THE DOCUMENT. The agent knows to format them as empty code blocks and all i need to do is fill them in then tell Claude Code to finish the task at hand. Kinda niche, but I use this for EVERYTHING now: 1. State what I want at top (Ad Scripts) 2. Type out relevant context that's in my head 3. Tell the agent to ask @ clarifying-questions 4. Quickly answer them with wispr flow in the document 5. Tell Claude to continue This would be useful for nearly every workflow.

Riley Brown

27,664 次观看 • 7 个月前

Kimi CEO Zhilin Yang: "a smart agent isn't enough - it has to run long Kimi 3 new attention makes the context 10x faster, so the agent never has to stop" here's how built it: step 1 → chase token efficiency, not compute - Muon optimizer, 2x more learning per token step 2 → make context cheap, not just long - new attention runs 10x faster, so the agent never stalls step 3 → let it run 200-300 steps alone - K2 already solves problems its own builders can't step 4 → give it a strong prior - the agent searches less and finds the answer faster step 5 → stop chasing a smarter model - chase one that never has to stop in 26-min talk is him revealing the exact architecture behind K3 and how to properly build agents on it watch & bookmark - start with the article below ↓
18:00

Kimi CEO Zhilin Yang: "a smart agent isn't enough - it has to run long Kimi 3 new attention makes the context 10x faster, so the agent never has to stop" here's how built it: step 1 → chase token efficiency, not compute - Muon optimizer, 2x more learning per token step 2 → make context cheap, not just long - new attention runs 10x faster, so the agent never stalls step 3 → let it run 200-300 steps alone - K2 already solves problems its own builders can't step 4 → give it a strong prior - the agent searches less and finds the answer faster step 5 → stop chasing a smarter model - chase one that never has to stop in 26-min talk is him revealing the exact architecture behind K3 and how to properly build agents on it watch & bookmark - start with the article below ↓

codila

11,697 次观看 • 9 天前

Boris Cherny: "My job is to write loops." 5-day version you can copy for Claude Code Day 1: repo memory - write CLAUDE.md - include stack, test commands, code style, release rules, gotchas - add .claude/settings.json with the shell commands Claude can run Day 2: verification skill - pick 1 flow Claude keeps breaking - put the browser/API test in .claude/skills/ /scripts/ - make it return: pass/fail, failing step, screenshot/log path Day 3: commands Add these files: .claude/commands/babysit.md - checks your PRs - reads CI - handles obvious review nits - surfaces design questions .claude/commands/triage-issues.md - labels new issues - dedupes against existing ones - assigns owners .claude/commands/deploy-watch.md - checks the live app - reports regressions - avoids touching production Day 4: loops > /loop 5m /babysit > /loop 15m /triage-issues > /loop 5m /deploy-watch Day 5: overnight work - schedule /morning-report - schedule /deep-audit - write results to .claude/inbox/ - let your morning loop read from that folder The rule: every code-writing loop gets a separate verifier. Builder makes the change. Verifier runs the real app. You read the diff. Skip that and you wake up to 14 broken PRs with very confident summaries. Video: "Reflecting on a year of Claude Code" with Boris Cherny & Cat Wu
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Boris Cherny: "My job is to write loops." 5-day version you can copy for Claude Code Day 1: repo memory - write CLAUDE.md - include stack, test commands, code style, release rules, gotchas - add .claude/settings.json with the shell commands Claude can run Day 2: verification skill - pick 1 flow Claude keeps breaking - put the browser/API test in .claude/skills/ /scripts/ - make it return: pass/fail, failing step, screenshot/log path Day 3: commands Add these files: .claude/commands/babysit.md - checks your PRs - reads CI - handles obvious review nits - surfaces design questions .claude/commands/triage-issues.md - labels new issues - dedupes against existing ones - assigns owners .claude/commands/deploy-watch.md - checks the live app - reports regressions - avoids touching production Day 4: loops > /loop 5m /babysit > /loop 15m /triage-issues > /loop 5m /deploy-watch Day 5: overnight work - schedule /morning-report - schedule /deep-audit - write results to .claude/inbox/ - let your morning loop read from that folder The rule: every code-writing loop gets a separate verifier. Builder makes the change. Verifier runs the real app. You read the diff. Skip that and you wake up to 14 broken PRs with very confident summaries. Video: "Reflecting on a year of Claude Code" with Boris Cherny & Cat Wu

Alpha Batcher

141,110 次观看 • 1 个月前