AutoTool – text-to-dashboard with ToolJet/Retool

Hacker News

Hey all! Excited to demo AutoTool, a GPT3-powered conversational UI for rapidly prototyping internal tooling in ToolJet/Retool. You can describe what you want in an internal tools in natural language - e.g. "a dashboard that does X" - and it produces the result for you. You can also iteratively refine your dashboard using textual prompts, e.g. "Move this table here, add a form to do Y". This is all done with prompt engineering and a creative technique for transforming GPT-3 (and Codex) output to a DSL for internal tools. Would love to hear any feedback. Thanks!

Visit product View on Hacker NewsNov 1, 2022

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

82%82% 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.

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

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% 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

40%40% 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

21%21% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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