Pairing LLM code generation with traditional templates

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Hi, I’ve been exploring Claude 3.5 code generation abilities for a while and it looks like it can generate more consistent code than other models. However it would still be unmaintainable if you ask it to write a lot of code and it still sucks at system design. So, I’ve been playing around the idea of using the code repository with a template for directory layout and infrastructure, then adding the repository information to Claude and asking it to generate code. It seems that it works, if I pass the structure of some OpenAPI based backend it can update the API definition and implementation pretty consistently. Take a look at the PoC and please share your thoughts about this! I wanted to see if someone else is working on similar idea, I’m open for a chat!

Visit product View on Hacker NewsJul 24, 2024

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

91%91% 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

53%53% 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

44%44% 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

33%33% 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

27%27% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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