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.

Who uses RequestHunt?

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.

Is RequestHunt free to use?

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.

[OpenAgentsControl] Transition project memory to ADR-style index + individual records

Replace the single `decisions-log.md` file with an ADR (Architectural Decision Record)-style index and individual records for project memory, improving efficiency and scalability for AI agents and collaborative development by reducing context window bloat.

Original Source

@@Claudio-Osorio | 2 pts | 2/2/2026

TLDR: Using ADR files (reading index only) instead of decisions-log.md to improve efficiency and reduce noise. The current implementation of project memory relies on a single, append-only file located at .opencode/context/project/decisions-log.md. While simple to implement, this approach may present significant scaling issues for AI agents and collaborative development. The Problem Context Window Bloat: As a project matures (e.g., reaching 100+ decisions), the log consumes an increasing number of tokens. Forcing the agent to read the entire log to understand one specific decision is inefficient and costly. "Lost in the Middle" Bias: Large language models often lose focus on information buried in the middle of long documents. A single massive log decreases the agent’s ability to accurately retrieve specific architectural constraints. Template Corruption: There is a risk of agents overwriting the structural boilerplate of the single file during long-form append operations. The Propo

Discussion

Loading comments...