How can academic researchers use AI tools to speed up literature management, paper writing, statistical analysis, and presentations? A practical perspective from an assistant professor with four concrete entry points.
Open →Karpathy's original 4 rules cut AI coding errors from 41% to 11%. After 6 weeks of testing across 30 codebases, 8 more rules brought that down to 3%. Here's the breakdown.
Open →An open-source Next.js app that connects LLMs to draw.io, letting you generate and refine diagrams with natural language. Best for researchers and engineers who need quick architecture diagram drafts with full draw.io editing control afterward. Requires Node.js setup and an LLM API key.
Open →A deep dive into Claude Code's CLAUDE.md config. From identity declaration to memory routing, build personalized AI agents.
Open →Mole replaces CleanMyMac, AppCleaner, DaisyDisk, and iStat Menus with a single CLI binary. Best for developers who live in the terminal and run AI agents like Claude Code or Cursor. Free, open-source, and actively maintained. The main risk is permanent deletion on most commands.
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