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RequestHunt continuously collects feature requests from Reddit, X (Twitter), and GitHub. You can also trigger on-demand scraping via the API or web interface to get the freshest data for any topic.

Integrate CI Checks for LLM-Generated Code

Add functionality to AI code editors to automatically perform lightweight CI checks, including linting and dependency scanning, on LLM-generated code before it is merged. This would help prevent the introduction of surprises and maintain code quality.

Original Source

@@thebootstrappedmarketer | 115 pts

Nice video — loved the vibe coding walkthrough and how you grounded the whole thing in PRD → frameworks → checkpoints → debugging → context. One extra thing I’d add for folks following along: bake a simple test and a rollback plan into your MVP step. Even a single unit test for core logic and a feature flag for risky changes will save so much time when the model makes sweeping edits. Also, set up a lightweight CI check (lint + dependency scan) before merging so LLM-generated code doesn’t introduce surprises. And if you want a ridiculously easy way to share static previews or MVP builds, Tiiny Host is a great tool — super fast for dropping a static React/Vite/Next build and sharing a demo link without full cloud deployment. Overall, great job — excited to see a follow-up diving into advanced agent rules and CI deployment!

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