This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
AI Research Lab
Read 500+ AI papers without reading 500 papers.
AI Research Lab is a multi-agent observatory over arxiv. 5 specialized LLM agents read 502 papers and surface what matters across them — 39 contradictions, 48 consensus findings, 19 open debates, frontiers. Weekly fresh ingest, every step documented.
Hey PH — Maker here. Software engineer by background. I built AI Research Lab over a few weekends using an AI coding agent as my development environment — returning to the keyboard with an AI pair.
What it actually does: ingests arxiv papers, runs 5 specialized LLM agents (summarizer, contradiction-finder, frontier-detector, trend-mapper, benchmark-extractor), and renders the output into a 5-tab UI per research topic. The tab I care about most is Insights — where the contradiction-finder and trend-mapper outputs land, so you can see "these 4 papers disagree about X" instead of reading 4 abstracts in sequence.
The numbers: 502 papers across 9 topics, 7,393 citations mapped, 837 flagged as influential, 39 contradictions / 48 consensus findings / 19 open debates / 8 warnings already extracted.
Stack: Next.js 15, Postgres + pgvector, Gemini text-embedding-001, an instruct-tuned LLM, Cloud Run. Weekly cron. RSS feed per topic. Open source (MIT).
What I'd love from PH: try a topic, click into Insights, tell me if a contradiction is wrong. If you're a researcher: which agents are missing? If you're an operator or ex-engineer using an AI coding agent to ship things — DM me.
AI Research Lab was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #145 on the daily leaderboard. AI Research Lab is a multi-agent observatory over arxiv. 5 specialized LLM agents read 502 papers and surface what matters across them — 39 contradictions, 48 consensus findings, 19 open debates, frontiers. Weekly fresh ingest, every step documented.
On the analytics side, AI Research Lab competes within Developer Tools, Artificial Intelligence, GitHub and Tech — topics that collectively have 1.6M followers on Product Hunt. The dashboard above tracks how AI Research Lab performed against the three products that launched closest to it on the same day.
Who hunted AI Research Lab?
AI Research Lab was hunted by Abhi Das. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of AI Research Lab including community comment highlights and product details, visit the product overview.
Abhi Das
Hey PH — Maker here. Software engineer by background. I built AI Research Lab over a few weekends using an AI coding agent as my development environment — returning to the keyboard with an AI pair.
What it actually does: ingests arxiv papers, runs 5 specialized LLM agents (summarizer, contradiction-finder, frontier-detector, trend-mapper, benchmark-extractor), and renders the output into a 5-tab UI per research topic. The tab I care about most is Insights — where the contradiction-finder and trend-mapper outputs land, so you can see "these 4 papers disagree about X" instead of reading 4 abstracts in sequence.
The numbers: 502 papers across 9 topics, 7,393 citations mapped, 837 flagged as influential, 39 contradictions / 48 consensus findings / 19 open debates / 8 warnings already extracted.
Stack: Next.js 15, Postgres + pgvector, Gemini text-embedding-001, an instruct-tuned LLM, Cloud Run. Weekly cron. RSS feed per topic. Open source (MIT).
What I'd love from PH: try a topic, click into Insights, tell me if a contradiction is wrong. If you're a researcher: which agents are missing? If you're an operator or ex-engineer using an AI coding agent to ship things — DM me.
Try it: https://www.airesearchlab.space/
Code: https://www.github.com/abhid1234/AI-research-lab
Blog: https://abhid.substack.com/p/wha...
I'll be here all day. — Abhi