Forem: TreeSoop

claude2codex: migrate Claude Code config to OpenAI Codex in one command

TreeSoop — Fri, 17 Apr 2026 14:30:13 +0000

Our team pays ~$700/month for Claude Code Max (3 accounts). We're Claude-native. But between Claude's recent reliability issues and Codex's cost advantages for simpler workloads, we've been moving some work to Codex.

Migrating turned out to be annoying — plugins, MCP servers, memory files, harness configs all live in different places with different formats.

We wrote a CLI to automate it and open sourced it.

📚 Full writeup: https://treesoop.com/blog/claude2codex-migration-tool-open-source-2026
🔧 GitHub: https://github.com/treesoop/claude2codex

What it migrates

Claude Code	→	Codex
`~/.claude/CLAUDE.md`		`~/.codex/config.md` (format converted)
`~/.claude/settings.json`		`codex.toml`
`~/.claude/skills/*.md`		`~/.codex/prompts/*.md`
`~/.claude/user_profile.md`		Codex profile
MCP server registrations		Codex-compatible config block
Harness trigger logic		Best-effort port with warnings

Install

npx claude2codex init
npx claude2codex migrate --dry-run  # preview
npx claude2codex migrate            # execute

Who should care

Teams running both Claude Code and Codex in hybrid mode
Anyone hitting Claude Code session limits or reliability issues
Teams evaluating Codex but not wanting to set up from scratch

Results in our team

Nine team members migrated their setups using this tool. ~95% of settings auto-converted and worked in Codex on first try. The remaining 5% were flagged in a conflict report and manually adjusted.

Why hybrid, not replacement

We still default to Claude Code Max for anything requiring strong reasoning or long-horizon planning. Codex picks up:

Short repetitive tasks (doc generation, test writing)
Tasks where token cost matters more than depth
Fallback when Claude adaptive thinking underallocates

Details

MIT licensed
Your original Claude config is preserved (not modified)
Codex unsupported features get warnings in a report

More from TreeSoop: ai-news-mcp, hwp-mcp, whisper_transcription

Blog: https://treesoop.com/blog

Local Whisper pipeline beats paid Korean transcription services

TreeSoop — Fri, 17 Apr 2026 14:29:35 +0000

We were paying for Notta to transcribe Korean meetings. The Korean accuracy on technical terms was consistently bad — we were spending more time fixing transcripts than just writing notes by hand.

So we built a local Whisper pipeline. Turns out it beats the paid service on Korean accuracy.

📚 Full writeup: https://treesoop.com/blog/whisper-transcription-local-korean-stt-2026
🔧 GitHub: https://github.com/treesoop/whisper_transcription

Setup

Audio → ffmpeg preprocessing → Whisper (large-v3) → sentence boundary post-processing → markdown

Key decisions:

Whisper large-v3 for Korean technical vocabulary accuracy. base/small/medium all struggle with domain-specific terms.
ffmpeg preprocessing — 16kHz sample rate, light noise filter. Measurable accuracy bump.
Sentence boundary post-processing — Whisper outputs long monologues. We re-chunk using commas, conjunctions, and timestamps.

Results (30-min Korean meeting)

Technical term accuracy: noticeably better than paid service
Processing speed on M1 Pro: faster than realtime
Cost: zero
Security: entirely local, no cloud transmission

Why local matters

Most of our use cases can't legally send audio to cloud:

Customer meeting recordings (NDA)
Legal/medical meetings (privacy laws)
Strategy meetings (trade secrets)
R&D discussions (IP)

Local-only pipeline removes all of that concern.

About VibeVoice

We tested it. Didn't run stably on Apple Silicon when we tried. Skipped for this release. Will revisit if they fix Apple Silicon compatibility.

TreeSoop context

We also have a commercial Korean STT product called Asimula with domain-specific fine-tuning for medical/legal. This OSS pipeline is a good starting point if you want to validate basic Whisper quality before investing in domain tuning.

MIT licensed
macOS Silicon optimized (M1/M2/M3/M4)
See repo for setup

More from TreeSoop: ai-news-mcp, hwp-mcp, claude2codex

Blog: https://treesoop.com/blog

Stop burning tokens on DOM noise: a Playwright MCP optimizer layer

TreeSoop — Fri, 17 Apr 2026 14:21:00 +0000

If you've used Playwright MCP for AI browser automation, you know the pain. Every page navigation dumps the full DOM tree into the model context. Simple flows like "order 5 items from this shop" can burn hundreds of thousands of tokens on navbar/sidebar/footer noise that has nothing to do with the task.

We built a small MCP layer that sits in front of Playwright and only forwards the relevant bits. Open sourced it.

📚 Full writeup: https://treesoop.com/blog/playwright-mcp-optimizer-token-saving-2026
🔧 GitHub: https://github.com/treesoop/claude-native-plugin

The problem

Playwright MCP serializes the full DOM:

AI ← {ENTIRE_DOM_JSON} ← Playwright MCP

This works for QA where you need to see everything. For "browse and take an action" it's 5-10× the tokens you actually need.

The optimizer

AI ← {relevant_only} ← Optimizer ← {full DOM} ← Playwright MCP

Three filter rules:

Interactive elements first: button, input, a — not decorative div/span
Semantic grouping: navigation / main / form / footer regions, so the model knows where it is
Task-aware skipping: if the current task is "checkout", skip sidebar recommendations and ad banners

Measured impact

On a "cart → checkout" flow with GPT-4: tokens dropped substantially, and round-trip latency improved as a side effect (smaller payloads → faster agent decisions).

Not a silver bullet. For QA tasks where you need full DOM accuracy, use vanilla Playwright MCP. For general browsing / automation agents, this is the cheaper + faster path.

Tool comparison (our testing)

Tool	Strength	Use for
playwright-mcp (default)	Full DOM accuracy	QA, complex validation
playwright-optimizer (this)	Token efficiency	Automation agents, browsing
vercel-browser-agent	Code generation speed	Simple browsing
claude-chrome-extension	Uses logged-in session	Tasks needing auth state

We use all four for different jobs.

Install

npm install -g @treesoop/playwright-optimizer
claude mcp add playwright-opt -- playwright-optimizer

MIT licensed
Configurable per-site presets
--log-tokens flag for measurement

More OSS from TreeSoop: ai-news-mcp, hwp-mcp, whisper_transcription, claude2codex

Blog: https://treesoop.com/blog

ai-news-mcp: 17 AI trend sources auto-scraped, served via MCP

TreeSoop — Fri, 17 Apr 2026 14:19:24 +0000

Keeping up with AI news means scraping the same sources as everyone else — HackerNews, Reddit (r/MachineLearning, r/LocalLLaMA), ArXiv, GitHub Trending, Dev.to, Lobsters, and about 10 more. Everyone builds their own version. Seems silly.

So we built one and open sourced it.

📚 Full writeup: https://treesoop.com/blog/ai-news-mcp-17-sources-auto-scraping-2026
🔧 GitHub: https://github.com/treesoop/ai-news-mcp

What it does

17 sources scraped every 6 hours by a Mac mini in our office. Results exposed via Model Context Protocol so any MCP-compatible AI tool can query it.

Sources include:

HackerNews, Reddit (4 AI subs), Dev.to, Lobsters
ArXiv AI, ArXiv ML
GitHub Trending
OpenAI, Anthropic, Google AI, Meta AI blogs
TechCrunch AI, VentureBeat AI, The Verge AI

Install

claude mcp add ai-news -- npx -y @treesoop/ai-news-mcp

Works in Claude Code, Cursor, Claude Desktop, ChatGPT — anywhere MCP is supported.

Example queries

"Show me HackerNews top AI posts from today with 100+ points"
"Summarize ArXiv AI papers about RAG from the last 24 hours"
"What's trending on r/LocalLLaMA about Qwen3?"

Why bother

Our blog agent at treesoop.com uses this MCP to decide what to write about each day. Before it existed, we were scraping manually in 5 different scripts. Now it's one call.

If you run a dev newsletter, slack bot, or content agent, this probably saves you an afternoon.

Details

MIT licensed, commercial use OK
Data stored in our Supabase instance (free for public use)
Self-host option in README if you want your own cadence

More TreeSoop OSS: hwp-mcp, whisper_transcription, claude2codex.

Blog: https://treesoop.com/blog

Introducing hwp-mcp: Korean document support for Claude via MCP

TreeSoop — Fri, 17 Apr 2026 14:15:48 +0000

Korean office documents (.hwp / .hwpx) are everywhere in Korean government, enterprise, and legal workflows. Until now, Claude, ChatGPT, and Cursor couldn't read them natively — a real blocker for anyone building AI systems for Korean organizations.

We (TreeSoop) just released hwp-mcp, an open source MCP server that fixes this.

📚 Full writeup: https://treesoop.com/blog/hwp-mcp-korean-document-ai-claude-2026
🔧 GitHub: https://github.com/treesoop/hwp-mcp

What it does

hwp-mcp exposes these tools via the Model Context Protocol:

Extract text from .hwp / .hwpx files
Parse tables into structured data
Pull out embedded images
Find-and-replace within documents
Fill template variables (name, company, date)

Works on macOS and Windows. No Hancom Office license required.

Install in one line

claude mcp add hwp-mcp -- uvx --from hwp-mcp hwp-mcp

Works with Claude Code, Claude Desktop, VS Code Copilot, Cursor — anywhere that supports MCP.

Why this matters for Korean AI adoption

If you're building RAG systems, internal search, or document automation for Korean companies, 60–80% of the source documents will be HWP. Before hwp-mcp the options were:

Manual conversion (doesn't scale)
Hancom API licensing (Windows-only, paid)
Convert everything to Word org-wide (non-starter)

Now you just install the MCP and Claude reads HWP natively.

What we're using it for

TreeSoop uses hwp-mcp in:

Corporate RAG chatbots ingesting HWP knowledge bases
Government RFP automation (RFPs are distributed as HWP)
Legal contract review (Korean law firm contracts = HWP)
Meeting-note template auto-fill

MIT licensed

Commercial use is fine. Contributions welcome.

GitHub: https://github.com/treesoop/hwp-mcp
Docs: included in repo README

TreeSoop is an AI-Native dev agency from Korea. POSTECH/KAIST team. We're building production AI agents, RAG systems, and MCP tools. More OSS: ai-news-mcp, whisper_transcription, claude2codex.

Blog: https://treesoop.com/blog