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haskie gives Codex and Claude Code a library of sources you trust: technical references, domain knowledge and examples that reflect your taste. Curate it through a simple UI. Your agents explore broadly, then dig into focused passages with citations and fewer repeats. Inspired by how humans research, this discovery–synthesis loop helps agents make decisions grounded in your knowledge. Runs locally, with one-command agent setup.
I built haskie so my coding agents can work from the sources I trust.
I use coding agents every day. They often make mistakes that a source I trust could have helped them avoid. But I was still the one opening the “right book” to catch them. Giving them web search left me checking their answers against my own sources.
With haskie, I import those sources once. Its UI lets me curate them and organise them into collections. It connects to Claude and Codex through the Model Context Protocol (MCP), so my agents can consult those collections as they work.
Agents can search several aspects of a question at once. They can explore a topic broadly, read deeply into a specific problem, and use what they learn to decide what to investigate next. That gives them a way to discover, bring findings together, and search again as their understanding grows. haskie returns relevant passages from diverse sources and removes repeated material. It supplies the knowledge and the language model does the reasoning.
I also care about keeping my taste. I picked these sources because they match how I think: the great article that settles a question, the paper my work rests on. I want those choices to shape how my agents reason and what they build.
Which book or paper do you wish your agent had read before it started working on a problem?
haskie was submitted on Product Hunt and earned 8 upvotes and 2 comments, placing #160 on the daily leaderboard. haskie gives Codex and Claude Code a library of sources you trust: technical references, domain knowledge and examples that reflect your taste. Curate it through a simple UI. Your agents explore broadly, then dig into focused passages with citations and fewer repeats. Inspired by how humans research, this discovery–synthesis loop helps agents make decisions grounded in your knowledge. Runs locally, with one-command agent setup.
haskie was featured in Open Source (69k followers), Developer Tools (521.5k followers), Artificial Intelligence (481.6k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 269.6k products, making this a competitive space to launch in.
Who hunted haskie?
haskie was hunted by Črtomir Majer. 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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Črtomir Majer
Hi Product Hunt!
I built haskie so my coding agents can work from the sources I trust.
I use coding agents every day. They often make mistakes that a source I trust could have helped them avoid. But I was still the one opening the “right book” to catch them. Giving them web search left me checking their answers against my own sources.
With haskie, I import those sources once. Its UI lets me curate them and organise them into collections. It connects to Claude and Codex through the Model Context Protocol (MCP), so my agents can consult those collections as they work.
Agents can search several aspects of a question at once. They can explore a topic broadly, read deeply into a specific problem, and use what they learn to decide what to investigate next. That gives them a way to discover, bring findings together, and search again as their understanding grows. haskie returns relevant passages from diverse sources and removes repeated material. It supplies the knowledge and the language model does the reasoning.
I also care about keeping my taste. I picked these sources because they match how I think: the great article that settles a question, the paper my work rests on. I want those choices to shape how my agents reason and what they build.
Which book or paper do you wish your agent had read before it started working on a problem?