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Ex-Google engineer built a graph-based workflow that made Claude Code do nearly 10 days of work in about an hour. Here's the exact setup he uses. step 1. create a separate Git worktree for every task. run: git worktree add ../task-1 -b task-1 open a new terminal, cd into... it, and launch Claude Code. repeat this for every agent. each one gets its own branch, so nothing conflicts. step 2. give every Claude agent a different task. same repository. different branches. they all work in parallel. step 3. once they're done, review the pull requests. keep the good ones. merge what works. step 4. add a review agent. paste each PR into Claude and ask: "does this implementation match the original specification" every agent now has another agent reviewing its work before it gets merged. step 5. scale it up. 48GB of RAM can comfortably run around 20 Claude Code agents. 128GB can handle roughly 50. most engineers run Claude Code once and wait. he runs dozens of Claude Code agents in parallel, then merges the best results. this 60-minute lecture is one of the best free resources on graph engineering and multi-agent workflows. bookmark it and watch it below.show more

MIKE

27,922 subscribers

32,313 Aufrufe • vor 3 Monaten •via X (Twitter)

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27 Kommentare

Profilbild von Sesori - Claude, Codex & OpenCode on Mobile

Sesori - Claude, Codex & OpenCode on Mobilevor 3 Monaten

At 20 agents, launching them isn't the hard part. Knowing which one needs you before merge time is.

Profilbild von Alek Poniatowski

Alek Poniatowskivor 3 Monaten

This is exactly how you build in codex by default. Its support for worktrees is amazing

Profilbild von Sakata

Sakatavor 3 Monaten

Bookmarkedddd will watch when I’m free

Profilbild von Liam | AI Tools & News

Liam | AI Tools & Newsvor 3 Monaten

Helpful

Profilbild von MIKE

MIKEvor 3 Monaten

I’m happy to hear that

Profilbild von MAIL

MAILvor 3 Monaten

damn great step by step guide

Profilbild von 安叫兽|Bird🕊️ 🔶 BNB

安叫兽|Bird🕊️ 🔶 BNBvor 3 Monaten

worktree 这招真挺适合并行折腾的

Profilbild von Vipul Kumar Kewat

Vipul Kumar Kewatvor 3 Monaten

This is a great example of using AI to parallelize software development instead of treating it like a single coding assistant. Breaking work into independent task and adding a review step makes the workflow mouch more scalable and reliable.

Profilbild von DAVE

DAVEvor 3 Monaten

oh wow

Profilbild von Coscosmico

Coscosmicovor 3 Monaten

worktrees plus a review agent is the part people skip when they brag about agent headcount

Profilbild von Diario฿itcoin

Diario฿itcoinvor 3 Monaten

All I see is merge hell.

Profilbild von Jurly

Jurlyvor 3 Monaten

This is the kind of workflow optimization that makes me think we're still in the early days of understanding how to best

Profilbild von LarpRom

LarpRomvor 3 Monaten

Good orchestration beats throwing everything at one agent.

Profilbild von Blum

Blumvor 3 Monaten

cutting 10 days of work down to an hour is insanely effective

Profilbild von creeeeng

creeeengvor 3 Monaten

stack bloat feels productive lol tbh

Profilbild von FlowOps Daily

FlowOps Dailyvor 3 Monaten

“每个任务一个 git worktree” 这一步特别关键，因为后面不只是并行，更是验收不串。一个 worktree 对一张任务单、一个 diff、一个 rollback 点，review 时连 comment 漂移都会少很多。很多人觉得 graph engineering 难，先把这层隔离做好，后面的 agent 分工才不会越跑越乱。

Profilbild von unchosen.eth

unchosen.ethvor 3 Monaten

the review agent is my favorite part

Profilbild von cbrd21-1("bird")

cbrd21-1("bird")vor 3 Monaten

i do this with a distributed control plane that coordinates all the work onto many machines-scales horizontal and vertical and can use pi with local or openrouter or nim free models even and/or claude code-u can mix n match and it autoscales and autoheals

Profilbild von Abdulmuiz Adeyemo

Abdulmuiz Adeyemovor 3 Monaten

Oh man this is a huge Graph Engineering Masterclass

Profilbild von The AI Therapist

The AI Therapistvor 3 Monaten

Graph-based workflow = 10 days in 1 hour. the prompt engineering grind is over. the prompt orchestration monopoly is starting.

Profilbild von ceek

ceekvor 3 Monaten

let’s get it

Profilbild von 𝐕𝖆𝖑𝖊𝖓𝖙𝖎𝖓𝖔~❍𝖓𝖈𝖍𝖆𝖎𝖓.𓄀

𝐕𝖆𝖑𝖊𝖓𝖙𝖎𝖓𝖔~❍𝖓𝖈𝖍𝖆𝖎𝖓.𓄀vor 3 Monaten

Alot of steps.

Profilbild von catman

catmanvor 3 Monaten

using separate git worktrees for each task is clever. keeps the claude agent isolated so one failure doesn’t derail the other nine.

Profilbild von Leo

Leovor 3 Monaten

smooth workflow here mate

Profilbild von Innopro

Innoprovor 3 Monaten

What they built is interesting

Profilbild von stanley

stanleyvor 3 Monaten

insane work jesus.

Profilbild von mike

mikevor 3 Monaten

wow insane bro bookmarked

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Nikos

20,626 Aufrufe • vor 2 Monaten

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

rody

18,089 Aufrufe • vor 27 Tagen

I can't f*cking believe people still use Claude Code like a chat bot. the ones getting real work out of it gave it a folder, tools and a map of everything it knows. that map is graphify. you point it at a folder and it builds a knowledge graph of everything inside. code, docs, PDFs, images, even video. then your agent searches the graph instead of opening files one by one. 134k stars on GitHub. backed by Y Combinator. and most people still haven't tried it. what makes it different: -> the code part runs fully on your machine. nothing gets uploaded -> no vector database, no embeddings. it's a real graph your agent walks through -> every connection is labeled: read straight from the source, or figured out by graphify. you always know which -> you get 3 files: a clickable map you open in your browser, a report with the key ideas and surprising links, and the full graph your agent queries anytime on a public memory benchmark it found the right answer in its top 10 results about half the time. mem0 managed under 5%. and building the graph cost 0 model credits. here's the full setup around it: > create a folder called AGENT, open Claude Code inside it, and add a CLAUDE.md with one line: "save every decision, person and project to memory/" > run uv tool install graphifyy, then graphify install, then type /graphify . and the whole folder becomes a map Claude can search in seconds > run npm install -g @googleworkspace/cli, then gws auth setup and gws auth login. Claude can now read your Gmail, Calendar and Drive > run claude mcp add playwright npx @playwright/mcp@latest. it gets its own browser for sites, forms and data > run npm i -g jev-guard, then jev-guard key "...", then jev-guard install claude. Jev checks every action before it runs. rm -rf gets blocked, emails with hidden instructions get flagged, about 4 cents per 1,000 checks > type /schedule and say "every morning at 7:30, send me what came in, what you drafted and what needs my yes" one folder. 15 minutes. and Claude finally knows where everything is. all the repos: -> -> -> -> bookmark this and try graphify first. it's the piece everyone skips.

I can't f*cking believe people still use Claude Code like a chat bot. the ones getting real work out of it gave it a folder, tools and a map of everything it knows. that map is graphify. you point it at a folder and it builds a knowledge graph of everything inside. code, docs, PDFs, images, even video. then your agent searches the graph instead of opening files one by one. 134k stars on GitHub. backed by Y Combinator. and most people still haven't tried it. what makes it different: -> the code part runs fully on your machine. nothing gets uploaded -> no vector database, no embeddings. it's a real graph your agent walks through -> every connection is labeled: read straight from the source, or figured out by graphify. you always know which -> you get 3 files: a clickable map you open in your browser, a report with the key ideas and surprising links, and the full graph your agent queries anytime on a public memory benchmark it found the right answer in its top 10 results about half the time. mem0 managed under 5%. and building the graph cost 0 model credits. here's the full setup around it: > create a folder called AGENT, open Claude Code inside it, and add a CLAUDE.md with one line: "save every decision, person and project to memory/" > run uv tool install graphifyy, then graphify install, then type /graphify . and the whole folder becomes a map Claude can search in seconds > run npm install -g @googleworkspace/cli, then gws auth setup and gws auth login. Claude can now read your Gmail, Calendar and Drive > run claude mcp add playwright npx @playwright/mcp@latest. it gets its own browser for sites, forms and data > run npm i -g jev-guard, then jev-guard key "...", then jev-guard install claude. Jev checks every action before it runs. rm -rf gets blocked, emails with hidden instructions get flagged, about 4 cents per 1,000 checks > type /schedule and say "every morning at 7:30, send me what came in, what you drafted and what needs my yes" one folder. 15 minutes. and Claude finally knows where everything is. all the repos: -> -> -> -> bookmark this and try graphify first. it's the piece everyone skips.

kiosa

26,006 Aufrufe • vor 2 Tagen

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

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

178,272 Aufrufe • vor 6 Monaten