ATA – open-source terminal research agent for keeping up with papers

Hacker News

Research is moving faster than ever across every field. We built ATA to help keep up. ATA is a free, open-source fork of OpenAI's Codex CLI that adds a research stack: paper search (Semantic Scholar, arXiv, OpenAlex), citation graph traversal, PDF reading and synthesis, Hacker News discussion analysis, patent search (90+ offices via EPO), Zotero integration, and a persistent knowledge base that accumulates across sessions. We also built a reading view for long-form output — when ATA synthesizes a paper or generates a report, it opens as a navigable document with sections and foldable details instead of dumping it into chat. Follow-up questions rewrite the relevant section in place rather than appending at the bottom. Free, open source, runs locally. Try it: npm install -g @a2a-ai/ata https://github.com/Agents2AgentsAI/ata/

Visit product View on Hacker NewsFeb 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, codex · Missing: mac, macos, cursor

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: open source, hacker news, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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