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I built this as a weekend project to explore Japanese naming culture and minimalist UI design.
- *What it does*: Generates a printable/shareable card (1200×800px) with your name in kanji, hiragana, romaji, and meanings for both given & family names.
- *Tech stack*: React + TypeScript, html2canvas for export, all logic runs in-browser (no server, no analytics).
- *Design inspiration*: MUJI, Little Forest movie posters, and traditional calligraphy.
- *Why?*: I wanted a tool that feels calm, respectful, and culturally thoughtful — not just a gimmick.
I’ve been following the development of Clawdbot (a local AI OS) and realized that while the core project is brilliant, there’s a steep learning curve for non-hardcore developers—especially when it comes to 24/7 deployment on hardware like the new Mac Mini M4.
I built getclawdbot.org to bridge that gap. My goal is to make it the "missing manual" for the project. A few things I’ve added beyond the official README:
Hardware Optimization: Deep dives into why Mac Mini is the ideal low-power host.
Searchable Troubleshooting: I’m indexing common errors (like the notorious Gateway Pairing issue) to save people hours of debugging.
Skill Showcase: A more visual way to explore what these agents can actually do (browsing, Notion sync, etc.).
It’s still early days, and I’m actively adding more skill guides and local-first automation tips. I'd love to hear your thoughts on what’s missing or what local AI use cases you're most excited about!
Crawl4AI is an amazing open-source library that solves many LLM-scraping headaches, but I found that new developers often struggle with production configurations—specifically how to use it with MCP servers for Cursor, or how to bridge it with automation tools like n8n.
I built crawl4ai.dev as a community-driven documentation hub to fill these gaps. It includes:
One-click Docker setups for n8n/FastAPI.
Production-ready MCP server guides for Cursor & Claude.
Cost/performance benchmarks vs proprietary tools like Firecrawl.
The goal is to help everyone build self-hosted, affordable AI data pipelines. I'd love to hear your feedback on the guides or what other integrations you'd like to see documented!
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