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), GitHub, YouTube, LinkedIn, and Amazon. You can also trigger on-demand scraping via the API or web interface to get the freshest data for any topic.

Develop customizable AI agent workflows and evaluation harnesses

Users need more advanced customizable workflows for AI agents, beyond basic plugin systems, with improved hooks. Additionally, robust evaluation harnesses are required to ensure consistency and reliability of AI agent updates.

Original Source

@Paul Lundin | 7 pts

AI infrastructure and AI patterns are currently stuck in single player mode. This is a pattern we've seen before. Back in the day it was massively frustrating to deliver software to production. No git, no CI/CD, no k8s, no containers etc. Often deployments were manual, debugging was manual and maintenance windows took hours spanning into days. We got better at software delivery in part by evolving from single player to multi-player approaches. We even changed how we structure teams to deal with the challenges and evolutions. Now? "It worked on my machine" has become far less common to hear or see. We have to do it again with AI, and a big part of the lift is getting away from single shot prompting and one-off, single player workflows. All of the major AI influencers are showing off amazing aspects of how the emerging AI toolchain works, but they are doing it on small projects with solo developers.

Discussion

Loading comments...