Back Me Up – Find papers that back your argument

Hacker News

Back Me Up is a tiny science-ish machine that finds published papers that back your argument. It stemmed from a conversation with a friend around life sciences, where we kind of landed at "surely there’s a paper confirming most arguments", i.e. coffee is bad, coffee is great, etc. You enter a claim, e.g. "drinking wine is good for the heart" -> the system does a semantic search across a corpus (PMC, bioRxiv, medRxiv and arXiv) -> pulls in relevant quotes if they fit your claim, and gives you a verdict. Frontend was done with a combination of Sol + Opus, I spent a bit of time exploring different designs and landed on this slightly ridiculous printer that spits out your evidence. Some notes: - I have no accuracy evals, this was a throwaway thing I built, so no clue on reliability. It does pass the vibe test, so give it a whirl! - Initially built a RAG for a tiny set of PMC, but scaling that up seemed like a lot of work for an evening project. Luckily found an API that does the semantic search across the different sources.

Visit product View on Hacker NewsSep 15, 2026

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2points

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Launch Intel predictions

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

90%90% 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: ide, io · Missing: https docs, excited, just released

63%63% 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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

33%33% 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

32%32% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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