This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
h4cker turns Hacker News into a focused AI-native reading workspace. Browse the original feed, then use a persistent Flue-powered Agent to summarize stories, analyze discussions, research linked sources, and personalize what deserves your attention. First Brief, HN Scout, long-term reading memory, feedback-driven recommendations, and scheduled Agent Digests form a complete Agent loop. The code is publicly available on GitHub under a noncommercial source-available license.
Hey Product Hunt! I built h4cker because I wanted to learn Flue—an agent framework built on Pi Agent—through a small but complete real-world product. Hacker News felt like the perfect test bed: the source material is public, discussions are rich, and the daily reading workflow is easy to understand.
What started as a faster HN reader grew into a persistent Agent system with First Brief, HN Scout, deep research, long-term reading memory, explicit feedback, and scheduled Agent Digests. Under the hood it uses Flue workflows, typed tools, specialized skills, and bounded read-only subagents, while keeping product data and runtime state clearly separated.
The source is available on GitHub under a noncommercial license. I’d love feedback on whether the Agent actually helps you find and understand higher-signal technical discussions.
the fact that the agent actually remembers what you care about across sessions is kind of brilliant, like it feels less like a chatbot bolted onto HN and more like a real reading companion that learns your taste
About h4cker.app on Product Hunt
“An AI-native reading workspace for Hacker News”
h4cker.app was submitted on Product Hunt and earned 0 upvotes and 3 comments, placing #29 on the daily leaderboard. h4cker turns Hacker News into a focused AI-native reading workspace. Browse the original feed, then use a persistent Flue-powered Agent to summarize stories, analyze discussions, research linked sources, and personalize what deserves your attention. First Brief, HN Scout, long-term reading memory, feedback-driven recommendations, and scheduled Agent Digests form a complete Agent loop. The code is publicly available on GitHub under a noncommercial source-available license.
h4cker.app was featured in Productivity (656.2k followers), News (36.9k followers), Artificial Intelligence (473.7k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 284k products, making this a competitive space to launch in.
Who hunted h4cker.app?
h4cker.app was hunted by YiChu. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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