Test driven approach for code generation

Hacker News

We are working on a product that incorporates test driven approach for code generation, where AI learns from mistakes that caused test failures and generates code that matches the style of your codebase while passing all of your test cases. We believe this approach will reduce the time spent on code refactoring and make it more reliable for use in production. The test cases can involve checking inputs and outputs of different functions. They can also include automated browser testing or emulator testing for mobile apps. In these scenarios, the AI can learn from failures triggered by specific UI events, such as the click of a button and generate appropriate code.

Visit product View on Hacker NewsJul 13, 2023

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

89%89% 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: io · Missing: https docs, excited, just released

64%64% 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 · Strong signals: mistakes, ios · Missing: supports, reddit linkedin, podcasting

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

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

54%54% 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

26%26% 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

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

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

Incorrect prediction on native model

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