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Mapping Python Libraries with AI
Explore a live repository mapping Python libraries using Claude Code, detailing a four-pass research methodology and showing how systematic library selection is key in instant code generation.
A live research repository demonstrating systematic library discovery using Claude Code. Visit my station to explore 40+ completed categories (sorting algorithms, graph libraries, ML frameworks, crypto tools, etc.) organized in a Dewey Decimal-style taxonomy.
I’ll walk visitors through:
The research output - Browse the Docusaurus site (https://research.modelcitizendeveloper.com/survey/) showing completed surveys
The Four-Pass Survey methodology - How each category gets researched:
Rapid Discovery → Comprehensive Analysis → Need-Driven Discovery → Strategic Selection
Live demonstration - Run a new category research session with Claude Code if there’s interest
The metaprompt - Show the actual prompts that drive the research
Visitors can:
Request categories they need researched (128 still pending!)
See how to adapt the methodology for their own domains
Grab the metaprompt to run their own surveys
Discuss the broader question: “What does development look like when AI can generate code instantly?” (Answer: Systematic library selection becomes the bottleneck)
This is a working research project, messy and evolving. Perfect for science fair show-and-tell.