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.

RequestHunt

Loading request...

[AI Search Engines] Implement personalized AI agents for context-aware search results | RequestHunt

1086

[AI Search Engines] Implement personalized AI agents for context-aware search results

| | , , , ,

by @Andrew Yan | 5/28/2025 | submitted by AutoCollector

[view original source]

Users need AI search agents that can understand their specific nuances and preferences based on past usage and context. This would allow the AI to provide truly personalized recommendations (e.g., 'best PC build' tailored to gaming/streaming vs. work/battery life) without the user having to explicitly state all their preferences in each query.

Original Source

@Andrew Yan | 1086 pts

What do you call "AI SEO?" 👀 A few options I've heard so far... 1) GEO (Generative Engine Optimization) 2) AEO (Answer Engine Optimization) 3) LLM SEO (Large Language Model SEO) 4) GenAI Search Optimization 5) AI Search Optimization We're going to need to agree on a term. Because AI is taking over search. But... Not everyone is on the same page. And there are nuances re: "search origin" There are two distinct starting points today: LLM vs Traditional search engine Either a user loads up an LLM like ChatGPT and prompts it with a question or they go to a traditional search engine like Google and are served an AI overview (which are now triggering on 47% of Google searches btw) Fun fact: AI overviews are taking up 75.7% of your screen real estate on mobile which used to be prime pay to play PPC territory. Technically, they are slightly different. But does it really matter? My 0.02 Google will go all in with "AI Mode" And separate names won't really matter then (For context, I was a Product Manager on Google Search focusing on data inquisition and ingestion from third party providers before I left to start AthenaHQ helping marketers win on AI search) P.S. John Mueller confirmed AI Mode reporting will be coming to Google Search Console soon so you can start seeing performance reports in the near future! Excited to check this out. -- 👋 P.P.S. What should AI SEO be called? Lmk your pick below!

Discussion

Loading comments...