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coyaSONG/README.md

coyaSONG

I build reliable AI-agent and developer tooling, and contribute tested fixes and features across the open-source ecosystem.

I use coding agents transparently; the work highlighted here is limited to upstream-merged contributions with reproducible validation.

Blog · Portfolio · Merged open-source contributions · YouTube Research MCP on npm

What I build

Project Focus
YouTube Research MCP Citation-ready YouTube research for AI agents, with exact timestamp links and cross-video evidence
ralph-research Local-first runtime that keeps only verified improvements from recursive research experiments
tmuxicate Durable, vendor-neutral coordination for coding agents in tmux, with explicit task, review, and blocker state

Project pulse

Project Stars Latest release
YouTube Research MCP 15 YouTube Research MCP v1.2.0
ralph-research 0 v0.1.6
tmuxicate 0 No tagged release yet

Last updated 2026-08-17 via GitHub Actions.

Selected open-source work

Read the Korean case study: how I turn merged pull requests into engineering evidence.

Project Contribution
Quicktype Added opt-in final Swift class generation and validated all 241 Swift fixture runs
Swift Package Manager Prevented completion-script generation from creating unnecessary build directories
Effect Fixed Schema-to-Arbitrary handling in record-form property tests and added regression coverage
Sentry Propagated original event IDs into symbolication error scopes for traceable internal failures
Tailscale Corrected plain TCP serve-status annotations while preserving TLS and PROXY protocol labels
Super Productivity Added reactive custom-section task counts with Playwright regression coverage

Open-source footprint

40 merged pull requests across 24 public projects.

Latest upstream merges:

Last updated 2026-08-17 via GitHub Actions.

How I work

  • Reproduce the problem before changing code, then add regression coverage.
  • Follow each repository's own contribution, formatting, and validation workflow.
  • Report environment limitations separately from failures caused by a change.
  • Disclose substantive AI assistance and keep the verification evidence reviewable.

Writing

I write in Korean about frontend engineering, developer tooling, security, and AI-assisted software development at coyasong.dev.

한국어 소개

신뢰할 수 있는 AI 에이전트 및 개발자 도구를 만들고, 여러 오픈소스 프로젝트에 테스트와 검증 근거를 갖춘 개선을 기여하고 있습니다.

Pinned Loading

  1. youtube-mcp-server youtube-mcp-server Public

    Citation-ready YouTube research for AI agents — transcript search, exact timestamp links, cross-video evidence, and channel intelligence over MCP

    JavaScript 15 3

  2. ralph-research ralph-research Public

    Local-first runtime for recursive research improvement. Define metrics, run experiments, keep only verified improvements.

    TypeScript

  3. tmuxicate tmuxicate Public

    Crash-safe, vendor-neutral coordination for AI coding agents in tmux (and across machines over SSH)

    Go

  4. Effect-TS/effect Effect-TS/effect Public

    Build production-ready applications in TypeScript

    TypeScript 15.3k 679

  5. kopia/kopia kopia/kopia Public

    Cross-platform backup tool for Windows, macOS & Linux with fast, incremental backups, client-side end-to-end encryption, compression and data deduplication. CLI and GUI included.

    Go 13.9k 709

  6. mdn/content mdn/content Public

    The official source for MDN Web Docs content. Home to over 14,000 pages of documentation about HTML, CSS, JS, HTTP, Web APIs, and more.

    Markdown 10.9k 23.2k