# GitHub API Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/github-api
> Markdown URL: https://aitinkerers.org/technologies/github-api.md
> Technology record last updated: 2026-09-18T14:19:41Z
> Generated: 2026-09-21T18:34:48Z

Programmatically access and automate GitHub features: manage repositories, issues, pull requests, and CI/CD workflows via REST and GraphQL endpoints.

The GitHub API empowers developers to automate complex workflows and integrate custom tooling directly with the GitHub platform. It provides two primary interfaces: the REST API (v3) for resource-centric operations, like creating an issue via `POST /repos/{owner}/{repo}/issues`, and the GraphQL API (v4) for efficient, precise data retrieval. Use it to manage core Git database objects, automate deployment processes with GitHub Actions endpoints, or build custom CI servers. Authentication is secured using Personal Access Tokens (PATs) or GitHub Apps, ensuring fine-grained control over permissions and rate limits (e.g., 5,000 requests per hour for authenticated users).

- Official technology site: https://docs.github.com/en/rest
- Public AI Tinkerers demos and talks: 6
- Result page: 1 of 1

## Recent Public Talks and Demos

### [I Built An Agent To Be Me On LinkedIn (And It Failed Three Times)](https://boston.aitinkerers.org/talks/rsvp_vNaKz6EaPyc)

A multi-source, voice-conditioned content pipeline that produces scheduled LinkedIn and Twitter posts in my voice — used in production for inbound GTM as a solo consultant. Not "write a LinkedIn post about X." A weekly research-aggregation-and-generation loop with a human editorial gate. What runs end-to-end: Four input streams aggregated weekly: Daily tinkering / build-moment capture (via a custom skill that captures structured notes mid-flow) GitHub repo scan across active projects — surfaces interesting commits / changes / experiments Tunable web research on topics I care about (AI/agents, regulated industries, GTM patterns) Ambient activity log from the week Synthesis layer: all four streams are collapsed into a single weekly research document. This is the structured intermediate artifact everything downstream reads from — it's what turns "the agent posts random stuff" into "the agent posts from a coherent week of actual work." Voice-first writing system: custom voice guide + platform-specific generation guides. LinkedIn and Twitter are not "same model, different length" — they're different generation modes, with the voice guide as the shared spine. Critical reviewer: second agent reviews drafts for clarity, claim quality, and voice fidelity. Outputs edits, not rewrites. Typefully as the HITL editorial gate: scheduled posts land in Typefully. I review, edit, delete, or reschedule. Nothing posts without explicit approval. Important architectural decision — reusing an existing tool as the review UI instead of building one. What I'll show live: The weekly research doc (the structured input artifact — the room can read it) A live generation pass: research doc + voice guide → draft LinkedIn post The Typefully review pass — real editorial decisions, not theatre Side-by-side: a LinkedIn post that lands vs. a Twitter post the system breaks on (the format failure mode) The voice guide diffs I made to fix the "sounds like me but unintelligible" failure mode The public earlier-iteration version is open-source as the voice-first-writing plugin in my GitHub — internal current workflow has evolved past it but the public version is a usable starting point.

- Event context: AI Tinkerers Boston: GTM Agentic AI Launch — 2026-06-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_vNaKz6EaPyc

### [Selling to Scientists: Sales Intent Identification for Super Technical Buyers](https://seattle.aitinkerers.org/talks/rsvp_zsBLWVQ1iE4)

Sales intent tools watch behavior — they tell you an account is researching your category. They can't tell you what a researcher is working on, what they're stuck on, or what would make them reply. I sold to AI researchers for a decade by reading their papers and asking questions only someone who read them would ask. I built the system I wish I'd had the whole time: a seller defines their product, target accounts, and ideal buying signals in plain English; the engine mines papers, linked repos, and adjacent sources, then surfaces named opportunities with openers grounded in real pain quotes from the buyer's own writing. The novel part isn't the retrieval. It's that the seller's domain expertise becomes the system's input — captured as natural language signals, not keywords or trained models — and the LLM applies that expertise across a corpus the seller could never read on their own.

- Event context: GTM / Growth Track - Seattle — 2026-05-27 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_zsBLWVQ1iE4

### [GitClaw](https://paris.aitinkerers.org/talks/rsvp_minZf42tgUo)

GitClaw is a real-time voice AI companion designed to help GitHub maintainers manage repositories more efficiently. Built entirely by Nelson PROIA, GitClaw integrates directly into the GitHub workflow and allows maintainers to interact with their repositories through a natural voice interface. Instead of constantly switching between dashboards, notifications, and pull requests, maintainers can simply speak to GitClaw to review PRs, triage issues, track repository activity, and receive real-time updates. By combining voice interaction with intelligent repository awareness, GitClaw reduces the operational overhead of open-source maintenance and allows maintainers to focus on building, reviewing code, and engaging with their communities.

- Event context: High-Performance Local AI Development: Kick-off ThinkStation PGX — 2026-03-17 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_minZf42tgUo

### [The IDE is Dead. Vulcan Ships Our Code Now.](https://london.aitinkerers.org/talks/rsvp_lcPJx56YCAU)

Vulcan is an autonomous AI dev system we built at a stealth startup. It orchestrates Claude Code agents on Mac Minis to execute parallelizable development plans, create PRs, and autonomously fix its own CI failures. We haven't opened an IDE in months. I'll walk through: the plan/phase architecture with dependency graphs for concurrent Claude instances, the worker daemon that spawns and manages Claude Code subprocesses, the self-healing CI loop (including BFS-based loop detection to stop infinite fixes), and how MCP tools let Claude manage its own PR lifecycle through our Django backend. Live demo: submitting a plan, workers claiming phases, CI breaking, and Vulcan fixing itself.

- Event context: AI Tinkerers London - 2nd March featuring AMA with Tom Occhino, CPO at Vercel — 2026-03-02 — London
- Public talk page: https://london.aitinkerers.org/talks/rsvp_lcPJx56YCAU

### [Transcript-First CRM: Turning Call Recordings into Proposals, Summaries, and Account Intelligence](https://austin.aitinkerers.org/talks/rsvp_I-D1INcTSo0)

A walkthrough of our work-in-progress internal CRM built on Airtable that treats call transcripts as the primary data source rather than manual entry. I'll show the technical pipeline: Granola captures calls → transcripts flow through chained prompts to extract client needs, generate proposal drafts, and update account health scores → GitHub integrations pull in delivery context → weekly summaries get auto-generated for account managers. The interesting technical bits: prompt chaining architecture for going from raw transcript to structured proposal sections, how we handle context windows when stitching together multiple calls with the same client, and the surprisingly simple scoring system that flags accounts needing attention. This is held together with Airtable automations and API calls—I'll show what's working, what's duct-taped, and what's still broken.

- Event context: AI Tinkerers Austin: January 2026 Demo Night — 2026-01-23 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_I-D1INcTSo0

### [Using Github’s MCP for AI-powered take home coding challenge assessments](https://st-louis.aitinkerers.org/talks/rsvp_ZJNYEH53RE4)

I’ll be showing a system I built that can automatically assess Github code based on a prompt. I’ll cover how the prompt is collected via yaml front matter / markdown and how it’s processed on a server leveraging MCP and OpenAIs API.

- Event context: AI Tinkerers - St. Louis Inaugural Meetup — 2025-06-05 — St. Louis
- Public talk page: https://st-louis.aitinkerers.org/talks/rsvp_ZJNYEH53RE4

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