DealForge autonomously sources, scores, and writes investment memos on venture deals. Stop manually hunting.
1,180+ deals tracked · 22 AI investment memos · Updated daily
Show HN: Lint-AI by RooAGI, a Rust CLI for AI Doc Retrieval
We’re RooAGI. We built Lint-AI, a Rust CLI for indexing and retrieving evidence from large AI-generated corpora.<p>As AI systems create more task notes, traces, and reports, storing documents isn’t the only challenge.<p>The real problem is finding the right evidence when the same idea appears in multiple places, often with different wording.<p>Lint-AI is our current retrieval layer for that problem.<p>What Lint-AI does currently:<p>* Indexes large documentation corpora. * Extracts lightweight entities and important terms. * Supports hybrid retrieval using lexical, entity, term, and graph-aware scoring * Returns chunk-level evidence with --llm-context for downstream reviewer / LLM * Use exports doc, chunk, and entity graphs.<p>Example:<p>* ./lint-ai /path/to/docs --llm-context "where docs describe the same concept differently" --result-count 8 --simplified<p><pre><code> That command does not decide whether documents are in contradiction. It retrieves the most relevant chunks so that a reviewer layer can compare them. </code></pre> Repo: <a href="https://github.com/RooAGI/Lint-AI" rel="nofollow">https://github.com/RooAGI/Lint-AI</a><p>We’d appreciate feedback on:<p>* Retrieval/ranking design for documentation corpora. * How to evaluate evidence retrieval quality for alignment workflows. * What kinds of entity/relationship modeling would actually be useful here?<p>Visit: <a href="https://rooagi.com/" rel="nofollow">https://rooagi.com/</a>
Lint-AI addresses a timely problem in the AI infrastructure stack—managing and auditing large AI-generated datasets—using a high-performance Rust-based approach. However, the project is in its infancy with minimal traction signals and faces significant competition from established RAG frameworks and vector database providers.