# Claude Code Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/claude-code?page=3
> Markdown URL: https://aitinkerers.org/technologies/claude-code.md?page=3
> Technology record last updated: 2026-09-18T15:14:01Z
> Generated: 2026-09-21T02:43:08Z

Anthropic's agentic coding tool: Unleash Claude's raw power directly in your terminal or IDE to turn complex, hours-long workflows into a single command.

Claude Code is Anthropic’s powerful agentic coding assistant, designed for high-velocity development. It operates natively within your terminal, IDE (VS Code, JetBrains), or via a web interface, allowing you to delegate complex tasks like feature building, bug fixing, and codebase navigation. The agent plans, edits files, executes commands, and creates commits, maintaining awareness of your entire project structure. Internally, Anthropic engineers using Claude Code reported a 67% increase in productivity, demonstrating its capacity to deliver significant gains for Pro and Max plan users.

- Official technology site: https://claude.com/code
- Public AI Tinkerers demos and talks: 215
- Result page: 3 of 9

## Recent Public Talks and Demos

### [Is your skill still doing the right thing?](https://poland.aitinkerers.org/talks/rsvp_zcI9G1vqNok)

A new testing tool that lets you write tests to verify that your SKILL.md is followed by the most popular agents (Codex, Claude Code, OpenCode, and Cursor Agent) and that the workflow behaves exactly as expected, with assertions on loaded skills, files read, commands invoked, tokens used, and returned text.

- Event context: AI Tinkerers Poland #3 - Meetup in Wrocław — 2026-05-06 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_zcI9G1vqNok

### [/spawn-team: A Self-Healing Multi-Agent Coding Pipeline with Claude Code + Beads](https://seattle.aitinkerers.org/talks/rsvp_yI3yt6tcSig)

A workflow that decomposes a software feature into parallel implementation arms, farms each arm to a Codex agent running in an isolated git worktree, enforces completion ordering through a persistent DAG, auto-retries failed arms with exact judge findings, and merges clean — all resumable across session breaks. The demo shows this running live on a real codebase: pouring the DAG template, watching the explorer dynamically bond implementation arms after reading the code, two Codex agents working in parallel worktrees, the judge failing and retrying, then bd graph showing the full live state.

- Event context: AI Dev Tools Track - Seattle — 2026-05-06 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_yI3yt6tcSig

### [AI messaging with Thrum and Gas Town.](https://seattle.aitinkerers.org/talks/rsvp_qDeVnDoZ4o0)

I built Thrum to let your agents message each other to manage their work and allow you to scale up slowly while keeping control. Last week I added a Telegram plug-in.

- Event context: AI Dev Tools Track - Seattle — 2026-05-06 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_qDeVnDoZ4o0

### [Coding is Solved. Next Up: Figure Out How to Plan](https://seattle.aitinkerers.org/talks/rsvp_-1Uv6ETOTq8)

I built an Agent-Native Google Docs to review, edit, and share plans in Markdown

- Event context: AI Dev Tools Track - Seattle — 2026-05-06 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_-1Uv6ETOTq8

### [LLM as a Virtual Machine](https://columbus.aitinkerers.org/talks/rsvp_fvgnhkl5sm4)

Large Language Models are a new kind computer. They are simulators. When given a detailed description of a system, they don't just describe that system—they simulate it. OpenProse describes a virtual machine with enough specificity that reading it causes a Prose Complete system to simulate that VM. But simulation with sufficient fidelity is implementation. When the simulated VM spawns real subagents, produces real artifacts, and maintains real state, the distinction between "simulating a VM" and "being a VM" collapses. I will show examples of OpenProse files simulating a virtual machine and executing workflows. I will use examples involving Recursive Language Models, the Captain's Chair pattern, and automated PR review.

- Event context: AI Tinkerers - Columbus May Meetup — 2026-05-04 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_fvgnhkl5sm4

### [Using Greywall's Sandbox + Proxy to Inspect an Agent Loop in Detail](https://toronto.aitinkerers.org/talks/rsvp_8jTeClvXAzk)

Greywall is a software-defined sandbox tool that provides a userland-defined sandbox, and funnels all network traffic through a SOCKS5 proxy. With these two pieces, we can dynamically block or allow requests, modify requests before they leave the proxy, and build a complete story of everything an AI agent request is doing (including sub-agents, all filesystem calls, etc).

- Event context: AI Tinkerers Toronto - April 2026 - hosted by Shopify — 2026-04-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_8jTeClvXAzk

### [Multi-agent Claude Code at scale: building Parthas at Intercom](https://dublin.aitinkerers.org/talks/rsvp_qYginBx6EOk)

Parthas is a multi-agent orchestrator for Claude Code that coordinates parallel coding agents through software instead of agent-to-agent messaging. In this demo I'll run it live against a real codebase. I'll spin up several agents in isolated worktrees, show the permission queue and the web dashboard, and walk through the design principles that keep it fast without YOLO-ing your main branch.

- Event context: AI Tinkerers Dublin: Demo Night with Fin — 2026-04-28 — Dublin
- Public talk page: https://dublin.aitinkerers.org/talks/rsvp_qYginBx6EOk

### [The Autonomous Data Platform](https://denver-boulder.aitinkerers.org/talks/rsvp_trQMLCPz4zA)

Most tools make data teams more efficient. We've built a solution that makes them obsolete, by automating the data platform work end-to-end. Rather than specialized roles (e.g., data engineers, scientists, analysts), one expert manages a team of AI agents that handles the entire data workload, from building data pipelines, to dashboards, to analytics.

- Event context: AI Tinkerers Denver - April Meeting — 2026-04-27 — Denver
- Public talk page: https://denver-boulder.aitinkerers.org/talks/rsvp_trQMLCPz4zA

### [Developing large features without overloading the model context](https://poland.aitinkerers.org/talks/rsvp_aa_j6rU5fzc)

Using divide and conquer strategy to not overload the LLM context when working on large features. It can be easily incorporated into any coding agent thanks to the beads project. I'll show how to integrate that into your workflow and use it to improve the quality of output when developing large features.

- Event context: AI Tinkerers Poland - Meetup in Gdańsk #1 — 2026-04-23 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_aa_j6rU5fzc

### [Building a Daily AI Sales Agent in Claude Code — 38x Cold Outreach Response Rate](https://seattle.aitinkerers.org/talks/rsvp_05m3TX8PeEs)

A daily AI sales agent built entirely in Claude Code that sources, scores, and delivers qualified leads to prospects via LinkedIn — replacing templated cold outreach with curated artifacts. Live demo: I'll build a value play from scratch on stage — Exa Websets discovers 25 recently funded startups, Claude scores them against an ICP definition, filters to the top 10, creates a Google spreadsheet, and delivers the formatted sheet as a LinkedIn first-touch.

- Event context: AI Tinkerers Seattle: GTM Engineering — April Meetup — 2026-04-23 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_05m3TX8PeEs

### [Building My Own Piano Teacher Because I'm Too Impatient for YouTube](https://nurnberg.aitinkerers.org/talks/rsvp_NveTwvTUV4E)

I totally vibe coded a browser-based piano learning app that uses your microphone to detect pitches and gamifies music theory with timed challenges. (I've never really seen the code.) Demo: 1. Show the app and what it can do 2. Show my workflow: Prompting in Claude Code to add another feature 3. Showing the infrastructure (hosting for free with static website on Github Pages, using localStorage as Database)

- Event context: AI Tinkerers Nürnberg: April Meetup — 2026-04-22 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_NveTwvTUV4E

### [When You Log Off, Your Agents Clock In](https://atlanta.aitinkerers.org/talks/rsvp_3CWSeM9TLh4)

TaskFast, a TaskRabbit-style marketplace for agents, is a platform where both people and AI agents can post tasks for agents to complete.

- Event context: AI Tinkerers Atlanta: Community Demos & Technical Deep Dives — 2026-04-21 — Atlanta
- Public talk page: https://atlanta.aitinkerers.org/talks/rsvp_3CWSeM9TLh4

### [Dependency Guard - Security Runtime for AI Coding Agents](https://singapore.aitinkerers.org/talks/rsvp_XS-iiohawcs)

1. Sharing about DepGuard - a security runtime for AI coding agents that does reputation checks & a behavioral analysis in a sandbox 2. Sharing about Devin, Cognition's autonomous software engineer built for greenfield & brownfield projects and long-running async agentic flows with sandboxes

- Event context: AI Tinkerers Singapore: The Agentic Future & Dev/Eng Workflows — 2026-04-21 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_XS-iiohawcs

### [Postal - AI Chief of Staff that keeps me organized and updates itself](https://austin.aitinkerers.org/talks/rsvp_lQCh4jkzo7o)

Postal is the OpenClaw for people that don't want to buy a Macbook. I love the power of AI for coding and information discovery, and I wanted the same for my day-to-day tasks, like writing emails, updating calendar, keep track of notes, and memories about people and places. Postal offers all of that in one, convenient interface, available on my desktop and my phone. Web site is Supabase realtime, desktop Tauri runs completely offline with its own filesystem. Local models can be plugged in to stay off grid. The sync between environments is still janky, but I am working on it. The agents do research, reminders, rewrite my own notes, triage emails... the board is slowly eating its own dogfood and improving itself. Currently the app managing 900 different "memories" for me.

- Event context: AI Tinkerers Austin: April 2026 Demo Night — 2026-04-16 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_lQCh4jkzo7o

### [Jules: An AI Agent That Thinks, Argues, and Writes](https://austin.aitinkerers.org/talks/rsvp_f0lg_KSKY8s)

I built Jules — a Claude Code-based AI agent with a personality, opinions, and structured decision-making workflows. Jules isn't just a coding assistant — it's a strategic thinking partner that follows multi-step reasoning processes, pushes back on my ideas, runs adversarial reviews before recommending anything, and writes content in my voice. I'll demo the live system: how a single prompt triggers structured thinking workflows with visible reasoning steps, how the agent challenges my framing before agreeing with me, and where this is headed — multi-agent coordination through Paperclip and a conversational gateway called Hermes.

- Event context: AI Tinkerers Austin: April 2026 Demo Night — 2026-04-16 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_f0lg_KSKY8s

### [Don't Lose the Engineering: Parallel Development with AI Agents, Human Orchestration & a Real Methodology](https://medellin.aitinkerers.org/talks/rsvp_KgLs7C6JZlQ)

We built an internal development methodology and orchestration system where AI agents develop features in parallel — coordinated by human architects — shipping an international-grade SaaS product in 3 months. The demo shows our real workflow: how we structure agent-driven development cycles, how our CTO/CPO + backend architect (Lorgi) + frontend architects (Francisco, Mike) validate and audit every AI-generated output, and how we stripped Scrum down to what actually works — killing the bureaucracy and replacing it with product development iterations focused on real value. I'll walk through our local-first approach where each engineer runs their own AI pipeline before integration, our governance structure, and how we recovered engineering rigor after almost losing it to uncontrolled vibe coding. Live demo on our actual codebase and orchestration tools.

- Event context: AI Tinkerers Medellín #20 - SalesAds - 16 de Abril — 2026-04-16 — Medellín
- Public talk page: https://medellin.aitinkerers.org/talks/rsvp_KgLs7C6JZlQ

### [Ingeniería Inversa de Plugins de Claude Code: De Shanghai a Medellín](https://medellin.aitinkerers.org/talks/rsvp_fSouJaWMtIE)

Voy a mostrar cómo construí notebooklm-ai-plugin estudiando plugins del Shanghai AI Laboratory y herramientas de investigación científica. El patrón que encontré: scripts que usan cookies del navegador para comportarse como el sitio web de Google y hacer llamadas RPC directas, aprovechando la capa gratuita de estas plataformas sin necesidad de API keys. La charla cubre cómo reverse engineeré ese patrón, cómo lo adapté con TypeScript y Bun para construir un plugin con 9 tipos de artifacts, y cómo el manifest .claude-plugin lo hace distribuible con un solo comando. Sin diapositivas. Solo terminal. El repo abierto todo el tiempo.​​​​​​​​​​​​​​​​

- Event context: AI Tinkerers Medellín #20 - SalesAds - 16 de Abril — 2026-04-16 — Medellín
- Public talk page: https://medellin.aitinkerers.org/talks/rsvp_fSouJaWMtIE

### [Anthropic Ambassador Chile](https://santiago.aitinkerers.org/talks/rsvp_HCvohBDd5hs)

Claude Community Ambassador at Anthropic Chile — one of 9 leaders selected globally for the Claude Impact Lab program. Builder and founder of Bendita IA (4,500+ members), Chile's most active AI community, and Zyvor, Latin America's first autonomous growth agency operated by AI agents. Architect of Agent OS — an operating system for orchestrating intelligent agents in organizations, defining how companies deploy and govern AI at scale. Focused on one thing: moving Latin America from AI consumer to AI builder.

- Event context: 🔥 AI TINKERERS - SANTIAGO / CAPÍTULO #10 / 2026 — 2026-04-16 — Santiago
- Public talk page: https://santiago.aitinkerers.org/talks/rsvp_HCvohBDd5hs

### [Backpressure helping Claude code to learn from mistakes](https://cologne.aitinkerers.org/talks/rsvp_X_VoB3MUovI)

keel is a pure Rust CLI tool that builds a fast, incrementally-updated structural graph of your codebase and enforces architectural contracts at generation time — not at review time, not at build time. When an LLM coding agent modifies your code, keel immediately validates that the change doesn't break callers, violate type contracts, or introduce structural drift. Think of it as a structural linter purpose-built for the age of AI-generated code.

- Event context: AI Tinkerers Cologne 4: Live Technical Demos — 2026-04-16 — Cologne
- Public talk page: https://cologne.aitinkerers.org/talks/rsvp_X_VoB3MUovI

### [Harness Engineering to Stop Waiting on AI and Defeat the "Dumb Zone"](https://st-louis.aitinkerers.org/talks/rsvp_n9X4ZKjuLLk)

We built a deterministic TypeScript harness that orchestrates multi-agent development pipelines, shifting our workflow from synchronously waiting on LLMs to managing them asynchronously. By simulating the PM and architect layer upfront, forcing strict context resets during implementation, and demanding empirical validation through browser automation, this harness effectively 3x'd our team's productivity.

- Event context: AI Tinkerers St. Louis: April 2026 Meetup — 2026-04-14 — St. Louis
- Public talk page: https://st-louis.aitinkerers.org/talks/rsvp_n9X4ZKjuLLk

### [Computer Vision Pipeline for Drone Image Processing](https://st-louis.aitinkerers.org/talks/rsvp_yf6HEWzPJW4)

With Claude Code and GitHub SpecKit, I built a local web app which allows me to upload images, define regions of interest, and spin up a docker container to process images from my drone to do things like count cars. The container is CUDA aware and uses the GPU for faster processing. Single image processing takes about 30 seconds per image (super easy to demo quickly)

- Event context: AI Tinkerers St. Louis: April 2026 Meetup — 2026-04-14 — St. Louis
- Public talk page: https://st-louis.aitinkerers.org/talks/rsvp_yf6HEWzPJW4

### [Bugs Fix Themselves Now](https://upstate-ny.aitinkerers.org/talks/rsvp_75Gx_cCu2T8)

I built an agent loop that connects an error tracker to a coding agent so bugs go from "detected" to "fixed" without me context switching. Live demo: I'll trigger errors in a real app, then let Claude Code query them via MCP and generate the fix - all in the terminal.

- Event context: AI Tinkerers Upstate NY: Agents and Automation — 2026-04-14 — Upstate NY
- Public talk page: https://upstate-ny.aitinkerers.org/talks/rsvp_75Gx_cCu2T8

### [Orchestra — coordinating 10+ coding agents without becoming the bottleneck](https://seattle.aitinkerers.org/talks/rsvp_RmodSq4wq2Y)

I built Orchestra, a file-based coordination layer for managing multiple coding agents across repos without manually passing context between them. It turns your codebase into a shared memory + execution system using a structured .orchestra/ directory (threads, plans, decisions, handoffs, sessions), so agents can work in parallel across worktrees while staying aligned. In the demo, I’ll show how I go from problem → research → plan → parallel execution across API/frontend/infra agents, without constantly re-explaining context or micromanaging each agent.

- Event context: AI Dev Tool Track — Seattle Meetup - April 13th, 2026 — 2026-04-14 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_RmodSq4wq2Y

### [The Zero-Partners VC - AI Native VC running on AI Agents](https://zurich.aitinkerers.org/talks/rsvp_cPNM9rUu4j8)

We are www.ellipsis-venture.com - 2 GPs who are AI Builders (x-Google, x-founders, x-Apple) who run a fund without employees. We built an agentic system that runs everything - sourcing, due diligence, score cards and memos, marketing, Investors Relations, Ops, etc.

- Event context: AI Tinkerers Zurich April 9th — 2026-04-09 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_cPNM9rUu4j8

## Related Technologies

- [Python](https://aitinkerers.org/technologies/python) ([Markdown](https://aitinkerers.org/technologies/python.md)) — 662 public demos
- [MCP](https://aitinkerers.org/technologies/mcp) ([Markdown](https://aitinkerers.org/technologies/mcp.md)) — 129 public demos
- [TypeScript](https://aitinkerers.org/technologies/typescript) ([Markdown](https://aitinkerers.org/technologies/typescript.md)) — 205 public demos
- [Codex](https://aitinkerers.org/technologies/codex) ([Markdown](https://aitinkerers.org/technologies/codex.md)) — 44 public demos
- [Next](https://aitinkerers.org/technologies/next) ([Markdown](https://aitinkerers.org/technologies/next.md)) — 186 public demos
- [Gemini](https://aitinkerers.org/technologies/gemini) ([Markdown](https://aitinkerers.org/technologies/gemini.md)) — 188 public demos
- [Supabase](https://aitinkerers.org/technologies/supabase) ([Markdown](https://aitinkerers.org/technologies/supabase.md)) — 90 public demos
- [Claude](https://aitinkerers.org/technologies/claude) ([Markdown](https://aitinkerers.org/technologies/claude.md)) — 173 public demos
- [React](https://aitinkerers.org/technologies/react) ([Markdown](https://aitinkerers.org/technologies/react.md)) — 219 public demos
- [Cursor](https://aitinkerers.org/technologies/cursor) ([Markdown](https://aitinkerers.org/technologies/cursor.md)) — 65 public demos
- [OpenAI API](https://aitinkerers.org/technologies/openai-api) ([Markdown](https://aitinkerers.org/technologies/openai-api.md)) — 520 public demos
- [GitHub](https://aitinkerers.org/technologies/github) ([Markdown](https://aitinkerers.org/technologies/github.md)) — 74 public demos
- [Docker](https://aitinkerers.org/technologies/docker) ([Markdown](https://aitinkerers.org/technologies/docker.md)) — 147 public demos
- [Anthropic API](https://aitinkerers.org/technologies/anthropic-api) ([Markdown](https://aitinkerers.org/technologies/anthropic-api.md)) — 66 public demos
- [Markdown](https://aitinkerers.org/technologies/markdown) ([Markdown](https://aitinkerers.org/technologies/markdown.md)) — 24 public demos
- [Node](https://aitinkerers.org/technologies/node) ([Markdown](https://aitinkerers.org/technologies/node.md)) — 99 public demos
- [Vercel](https://aitinkerers.org/technologies/vercel) ([Markdown](https://aitinkerers.org/technologies/vercel.md)) — 48 public demos
- [Gemini CLI](https://aitinkerers.org/technologies/gemini-cli) ([Markdown](https://aitinkerers.org/technologies/gemini-cli.md)) — 16 public demos

## More Results

- Previous: https://aitinkerers.org/technologies/claude-code.md?page=2
- Next: https://aitinkerers.org/technologies/claude-code.md?page=4