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What I Learned from Steve Yegge's Gas Town — And a Small Tool for Solo Developers

#ai #productivity #opensource #webdev

recently discovered the most impressive project in the AI coding ecosystem.

Steve Yegge's Gas Town.

For those who don't know Steve Yegge:

  • Senior engineer at Google for 10+ years
  • Famous developer blogger ("Stevey's Blog Rants")
  • Worked on Grok
  • Recently co-authored "Wiring the Winning Organization" with Gene Kim

Gas Town, which he released on New Year's 2026, felt less like a tool and more like a manifesto for the future of AI coding.

The Core Insight of Gas Town

"AI agents are ephemeral. But work context should be permanent."

This matters because the biggest pain point we face with AI coding assistants is context loss.

You spend 3 hours designing architecture with Claude yesterday. Open a new session today? Blank slate. MIT Technology Review calls this one of the biggest limitations of LLMs.

Steve Yegge tackled this problem head-on.

Gas Town's Approach

1. Using Git as a trusted state store

Work state can be recovered even after agent crashes or restarts.

2. "Mayor" AI orchestrates multiple agents

20-30 agents working on different tasks simultaneously.

3. "Hooks" persist work state to Git worktree

Embracing agent ephemerality while ensuring continuity.

4. "Convoy" bundles multiple issues for tracking

Visibility into progress on complex projects.

Looking at this architecture, I realized this isn't just an "AI tool" — it's a new development paradigm for the AI era.

GitHub logo steveyegge / gastown

Gas Town - multi-agent workspace manager

Gas Town

Multi-agent orchestration system for Claude Code with persistent work tracking

Overview

Gas Town is a workspace manager that lets you coordinate multiple Claude Code agents working on different tasks. Instead of losing context when agents restart, Gas Town persists work state in git-backed hooks, enabling reliable multi-agent workflows.

What Problem Does This Solve?

Challenge Gas Town Solution
Agents lose context on restart Work persists in git-backed hooks
Manual agent coordination Built-in mailboxes, identities, and handoffs
4-10 agents become chaotic Scale comfortably to 20-30 agents
Work state lost in agent memory Work state stored in Beads ledger

Architecture

graph TB
    Mayor[The Mayor<br/>AI Coordinator]
    Town[Town Workspace<br/>~/gt/]
    Town --> Mayor
    Town --> Rig1[Rig: Project A]
    Town --> Rig2[Rig: Project B]
    Rig1 --> Crew1[Crew Member<br/>Your workspace]
    Rig1 --> Hooks1[Hooks<br/>Persistent storage]
    Rig1 --> Polecats1[Polecats<br/>Worker agents]

    Rig2 --> Crew2[Crew Member]
    Rig2 --> Hooks2[Hooks]
    Rig2 --> Polecats2[Polecats]

    Hooks1 -.git worktree.-> GitRepo1[Git Repository]
    Hooks2 -.git worktree.->
…
View on GitHub

I'm Also Building Something Similar

Gas Town is optimized for large teams and complex enterprise projects. Environments where you'd actually run 20-30 agents.

But what about solo developers like me, working on side projects?

I didn't need 20 agents. I wanted deep collaboration with one AI that remembers "where we left off yesterday" and "why we built it this way."

I'm building a tool called CodeSyncer.

Core idea:

  • Gas Town stores state in Git
  • CodeSyncer stores context in code comments themselves 
/**
 * Payment processor
 *
 * @codesyncer-decision [2026-01-15] Chose sync processing (UX priority)
 * @codesyncer-inference Minimum amount $1 (PG policy)
 * @codesyncer-todo Add refund logic
 */
async function processPayment(amount: number) {
  // @codesyncer-why Idempotency key prevents duplicate charges
  const idempotencyKey = generateKey(amount);
  // ...
}
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Next session, AI reads the code and instantly knows "why" it was built this way.

Start in 5 minutes, no complex setup.

npx codesyncer init
npx codesyncer watch
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GitHub logo bitjaru / codesyncer

AI-powered multi-repository collaboration system

CodeSyncer CLI

Is your AI coding dumb? Make it smarter - Persistent project context, controlled inference, and live architecture sync for Claude Code

npm version License GitHub stars GitHub issues

한국어 문서 | English

🎬 Demo

CodeSyncer Demo

🤔 The Problem

Working with AI on real projects? You face these issues:

1. Context is lost every session 😫

  • New AI session = Start from scratch
  • Explain the same architecture again and again
  • "What's the API endpoint?" "How does auth work?" - Every. Single. Time.

2. Multi-repo chaos 🤯

my-saas-project/
├── api-server/      (backend)
├── web-client/      (frontend)
└── mobile-app/      (mobile)
  • AI only sees one repo at a time
  • Missing context from other repos → Fragmented code
  • "Add login" needs backend API + frontend UI, but AI doesn't know both

3. AI makes dangerous assumptions ⚠️

  • "I'll set the timeout to 30 seconds" - Wait, should be 5!
  • "Using /api/v1/..." - Wrong endpoint!
  • Guesses business logic, security settings, pricing rules

Result…

View on GitHub

Summary

Gas Town CodeSyncer
Slogan Scale your agents Remember your context
Target Teams/Enterprise Solo developers
Agents 20-30 simultaneous 1, deeply
State storage Git worktree Code comments

The philosophy is the same: "AI is ephemeral, but context should be permanent."

Just different targets and approaches.

Wrapping Up

In 2026, the real differentiator in AI coding won't be "smarter models" — it'll be "better context management".

Steve Yegge showed the way with Gas Town. I'm trying to make a small contribution for solo developers.

Both are open source. If you're interested in AI coding, check them out.

If you've been struggling with this too, share your experience in the comments!

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