A natural language search interface to ETF (and deep insights on each)

Hacker News

Main features: 1. Search for ETFs using natural language queries like "find me ETFs that invest in bitcoin with low expense ratio" or "find all ETFs managed by cathie wood" 2. Multilingual search (results are all English though) 3. All source data straight from the SEC filings parsed into millions of lines of clean training text, no middlemen which allows us to do things that rely on semantic document understanding 4. Qualitative analysis of every ETF that answers questions like 'what are pros and cons of this etf', 'what is the detailed breakdown of expense ratio', 'how exactly does this ETF gets its exposure' and a lot more! We worked really hard on this one, would love to get feedback, thanks :) video demo https://youtu.be/BiOgA1tv1TM

Visit product View on Hacker NewsAug 29, 2025

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Launch Intel predictions

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

75%75% 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.

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: video, answers · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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

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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