RequestHunt is a community-driven platform that collects and curates feature requests from X (Twitter), Reddit, and GitHub. We help product teams discover what features real users are asking for, making it easier to validate product ideas and prioritize feature development based on actual user demand.

How does RequestHunt collect feature requests?

RequestHunt uses AI-powered extraction to automatically collect feature requests from social media platforms including X, Reddit, and GitHub. The platform monitors discussions, issues, and posts to identify genuine user feature requests, extracting key information like the requested feature, product mentioned, and user sentiment.

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RequestHunt is used by product managers, founders, developers, and product teams who want to discover what features real users are requesting. It's particularly valuable for startups looking to validate ideas, established companies prioritizing their roadmap, and developers building products that solve real user problems.

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Yes, browsing and searching feature requests on RequestHunt is completely free. We also offer API access for developers who want to integrate feature request data into their own applications. Check our pricing page for API plan details.

How often is RequestHunt data updated?

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.

AI Coding Assistant: Enhance adherence to project style and architecture guides

AI coding assistants should be improved to more consistently and effectively adhere to provided project-specific style guides and architecture documentation. Users currently experience a need for constant feedback loops and explicit reminders for the AI to follow these guidelines, indicating a lack of robust, persistent integration of these rules into the AI's generation process.

Original Source

@Jim Kring | 24 pts

Because AI coding assistants are designed to generate (based on how LLMs fundamentally work), they have a tendency to generate new (unhelpful) requirements and specifications, on the fly, in addition to implementing solutions that satisfy the initial (actual) requirements and specifications. It takes lots of extra work to keep a coding assistant on the rails. Have you found this to be true? How do you keep coding assistants from going off the rails?

Discussion

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