IdeaWell

Project ideas from Hacker News discussions.

Now Is the Time to Give LLMs Access to the ACM Digital Library

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Three dominant themes

Theme Supporting quote
1. ACM’s forthcoming open‑access policy “Beginning January 2026, all ACM publications and related artifacts in the ACM Digital Library will be made open access.” – cobbal
2. AI/LLMs reshaping research and peer‑review “Since starting to use AIs seriously for search in the last 3 months I have read and referenced more published papers and academic primary sources than I think I did in the previous 5 years. They’re fantastic for pointing at some claim and asking for the primary source for it, and then it does the work of following things through the layers of backref to the original, assuming it’s online.” – jerf
3. Authors vs. corporate exploitation of scholarly content “TLDR: “we’re going to try to get money from LLM providers for access to our back catalog without getting permission from the authors or providing them with any share of the revenue.”” – anticensor

These points capture the community’s focus on ACM’s open‑access rollout, the transformative (and contentious) role of AI in scholarly work, and the ongoing debate about author compensation and corporate use of published research.

🚀 Project Ideas

ACM Open Access Navigator

Summary

  • A browser extension that instantly detects and saves open‑access ACM PDFs while surfacing author‑friendly licensing info.
  • Makes ACM’s 2026 open‑access rollout truly “open” for every researcher.

Details

Key Value
Target Audience Graduate students, early‑career researchers, and indie scholars who frequently search ACM.
Core Feature One‑click PDF download + automatic copyright status summary (free vs. restricted).
Tech Stack Chrome/Firefox extension (JavaScript/TypeScript) + backend API querying ACM Open Access endpoints.
Difficulty Medium
Monetization Revenue-ready: subscription $4/mo for premium features (batch download, citation export).

Notes

  • Directly addresses HN complaints about “buried results” and “premium filters” on ACM’s search.
  • Quote from ilaksh: “I don’t want to be mean but if it’s that hard to even recognize that LLMs are even a valid thing…”. This tool leverages AI to surface the right paper without manual digging.

AI‑Driven Paper Mining Hub

Summary

  • A web platform that lets users query the entire ACM Open Access corpus with natural‑language prompts and receive full‑text PDFs, citation graphs, and related‑paper suggestions.
  • Turns the open library into an AI‑ready knowledge base for rapid literature review.

Details

Key Value
Target Audience Researchers, practitioners, and AI enthusiasts who need fast, citation‑accurate access to primary sources.
Core Feature Natural‑language search → auto‑extracted PDFs + provenance links to original ACM records.
Tech Stack React front‑end, Python backend (FastAPI), LLM (e.g., GPT‑4‑Turbo) for query parsing, PostgreSQL for metadata, Hugging Face embeddings for similarity search.
Difficulty High
Monetization Revenue-ready: tiered usage credits ($0.01 per retrieved PDF, $20/mo for 500 credits).

Notes

  • Solves jmount’s call for “public access to the ACM digital library” and jerf’s observation that LLMs can “dig back into primary sources”.
  • Encourages discussion about ethical data use while providing immediate utility.

Author‑Centric Licensing Dashboard

Summary

  • A SaaS dashboard that tracks how AI providers ingest ACM papers, offers authors real‑time royalties reporting, and mediates micro‑licensing agreements.
  • Gives scholars a transparent revenue stream from AI training on their published work.

Details

Key Value
Target Audience Academic authors, university tech transfer offices, and research institutions publishing in ACM.
Core Feature Automatic ingestion monitoring + royalty calculation + optional micro‑licensing contracts.
Tech Stack Node.js backend, MongoDB for tracking, smart‑contract‑like UI for licensing, API hooks to ACM’s publication metadata.
Difficulty Medium
Monetization Revenue-ready: 5% of royalties collected as platform fee (minimum $1/mo per author).

Notes

  • Responds to bonoboTP’s critique that “authors get nothing in return” and dwattttt’s question about fair compensation for knowledge used by AI.
  • Could spark lively HN debate on fair AI data compensation while offering a concrete service.

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