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Retorch: Connect any data, run any Python analysis in natural language

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Retorch: Connect any data, run any Python analysis in natural language

Hi everyone, I am the founder of Retorch. Retorch allow you to connect your data from any source including CSVs, SQL, Excel, Google Sheets etc... (and are looking at Stripe/Market Data… integrations) and run queries on the data in natural language. We first retrieve the relevant data and convert your query into Python. We inject all of this into a Virtual Machine, and gather the output and show it to you. We have extended visualisation tools from dataframes, print logs and matplotlib into interactive visualisation tools that allow you to do more and (hopefully!) look and feel better and easier. Above everything I’m genuinely just looking for feedback on my project - I am an parttime indie hacker (student) and I’ve built this over the last few weeks. Currently, I’ve tried tools that allow SQL queries but anything beyond that is impossible which seriously limits how much I could do with data, other than export it to colab and run analysis in python. Ive extended all of this so data retrieval and analysis are tied into one natural language interface. I’ve add certain features beside prompt engineering that I hope make the product more useful like a debug loop (i.e. feed errors back to the LLM to generate new code), persistent VM connections with high RAM (let’s say I create a linear regression model in one prompt, I can call it in the next prompt, and a micro Python IDE so you can change the generated code itself and run a new version of it. Any and all feedback + criticism welcome.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, google · Missing: agents, macos, agent
88%88% 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 · Strong signals: including · Missing: supports, reddit linkedin, podcasting
76%76% 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, including · Missing: https docs, excited, just released
51%51% 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 · Strong signals: google · Missing: mobile apps, ios, personal
43%43% 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
24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
16%16% 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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