CSV Analyzer – natural-language analysis, chat and preview, dashboards

Hacker News

I built a small web app for analysts who don’t want to pivot-table the same CSV for every question. You sign in with Google, upload CSV (or similar) files, and chat with an LLM that can run code against the data. Large tables are handled with a recursive / chunked RLM-style flow so we’re not stuffing the whole file into context. There’s a chat + tabular preview layout and dashboards where you can keep saved charts.

Visit product View on Hacker NewsMar 30, 2026

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Launch Intel predictions

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

78%78% 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.

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

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

49%49% 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 · 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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