Data and Financial Analysis and Forecasting via Natural Language

Hacker News

Hey, We just launched the MVP of OpenOS,a data & financial analysis tool! It tackles common challenges like needing technical skills for querying data, integrating open-source models, and scattered data. With OpenOS, you can: 1. Query relational databases & financial info using natural language. 2. Generate reports like MIS & financial statements with a single command. 3. Forecast user growth, cashflow, etc. It's powered by GPT, Tapas, and Prophet. We built it as a project/tool & would love for any of you guys to try it out & provide feedback as we build the product for public launch.

Visit product View on Hacker NewsApr 28, 2023

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

85%85% 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

72%72% predicted probability of success on Indie Hackers, 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

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.

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

46%46% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

12%12% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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