HuangShengZeBlueSky / llm_interview_auto_fetch

What is llm_interview_auto_fetch?

This Python tool automates fetching and processing LLM interview questions, top conference papers, and course notes into a searchable knowledge base. Drop images, PDFs, or texts into raw folders, run workflow scripts, and it uses LLMs to classify by company or topic, generate deep analyses, and build a static VitePress site for browsing interviews, papers, and insights. It solves the grind of manually curating AI job prep materials from scattered sources like face-to-face experiences or Arxiv.

Why is it gaining traction?

Its two-stage LLM pipeline—classify then deeply parse—turns raw screenshots into structured Markdown reports with zero manual tagging, plus auto-insights from recent content. Developers love the webhook endpoint for instant ingestion from phones or bots, and paper scrapers that pull high-rated ICLR/NeurIPS submissions. Unlike basic scrapers, it delivers a ready-to-deploy site with dynamic sidebars.

Who should use this?

LLM engineers prepping for ByteDance, Alibaba, or Tencent interviews who want auto-tagged question banks by topic like RAG or agents. AI researchers tracking NeurIPS trends or students distilling CS224N notes into textbooks. Ideal for anyone building a personal LLM study hub without coding custom pipelines.

Verdict

Grab it if you're in LLM interviews—solid for personal use despite 17 stars and 1.0% credibility signaling early maturity. Polish docs and add tests to boost adoption; it's a smart bootstrap for your own auto-fetch system.

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