AI

AI Chat for Research with References (>10-20 PDFs, No Hallucinations)

Hacker News

AI Chat for Research with References (>10-20 PDFs, No Hallucinations)

Hey HN, We’ve built Wallstr.chat, an AI-powered research tool that helps anyone working with long PDFs extract key insights instantly - with source references for every data point. It can handle 10-20+ PDFs in parallel, making it ideal for finance, research, and deep document analysis. Why it’s different: - No hallucinations (every insight is backed by a direct source reference from your PDFs) - Handles 10-20+ PDFs at once (analyze multiple reports in a single chat) - Fast & structured insights (extract key figures, tables, and references effortlessly) - Investment-grade accuracy (built for 10-Ks, earnings reports, investor decks, and research papers) Features: - Reference exact data points (every number/thesis is sourced, with the source paragraph highlighted) - Generate investment memos in under 60 seconds (custom templates coming soon) It’s open-source, so you can run it locally. GitHub: https://github.com/limanAI/wallstr If you find this useful, please star the repo!

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Actual performance

2points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, open · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
35%35% 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
19%19% 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.

Correct prediction on native model

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