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AirWiki
Turn your docs into knowledge people and AI can use
Turn team docs, study notes, and repository decisions into knowledge people and AI can use. Built on Open Knowledge Format (OKF), the open standard introduced by Google Cloud. Share reviewed wikis with authorized colleagues on a private LAN, publish your own notes to an experimental public network, or give coding assistants project memory via MCP. Open source, private by default, with separate sharing and AI controls. Desktop technical beta for macOS and Windows; no supported stable release yet.
Hi Product Hunt! We're building AirWiki because useful knowledge often gets stuck in a folder or an old conversation. The next person, or their AI assistant, has to start from scratch to find the same explanation.
AirWiki turns that material into searchable wikis. Here are three workflows we're building it for:
Teams: support needs to know how engineering recovers a failed import. Engineering shares a reviewed troubleshooting Wiki with verified colleagues on the local network. They can find the explanation themselves; connected assistants can retrieve evidence when the source owner permits that AI access.
Students: your notes finally make database normalization click. Turn them into a reviewed Wiki and choose to publish it to the experimental public network, so classmates and other students studying the topic can discover it. Share only material you have the right to publish. Publishers must be reachable, and public readers can retain what you share.
Developers: keep architecture decisions and project conventions in a portable .airwiki Wiki beside your code. Authorized tools such as Codex, Claude Code, and Gemini CLI can consult it in later tasks, so you spend less effort rebuilding context in every conversation. You review the files and decide what enters Git; AirWiki never commits or pushes.
For AI tools, the value is context they can look up: structured concepts, relationships, and relevant passages with source references through MCP. Preparation and retrieval run locally. A connected cloud AI provider may process evidence its app receives; sharing and AI access are separate choices.
Built on an open standard: AirWiki uses Open Knowledge Format (OKF), introduced by Google Cloud. Our independent OKF v0.2 implementation keeps knowledge in readable Markdown files with structured metadata for provenance, verification, and freshness. You can inspect the files, review changes in Git, and export your Wiki for reuse with compatible tools.
Current stage: open-source desktop technical beta, not a supported stable release. The 0.3 beta is available for Apple silicon Macs and Windows x64 as v0.3.0-rc.1. The Mac installer is signed and notarized; Windows installers are unsigned. Downloads need no account, and updates are manual. The gallery shows this version's interface with synthetic data. Public federation is experimental, with no supported always-on relay service. Private sharing uses LAN devices; there is no SSO or cloud sync.
We'd love developers, students, and people who manage team knowledge to try one workflow with sample material and help shape it. What context do you keep having to explain again, and where would a shared Wiki help? Feedback, documentation, and code contributions are welcome.
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About AirWiki on Product Hunt
“Turn your docs into knowledge people and AI can use”
AirWiki was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #145 on the daily leaderboard. Turn team docs, study notes, and repository decisions into knowledge people and AI can use. Built on Open Knowledge Format (OKF), the open standard introduced by Google Cloud. Share reviewed wikis with authorized colleagues on a private LAN, publish your own notes to an experimental public network, or give coding assistants project memory via MCP. Open source, private by default, with separate sharing and AI controls. Desktop technical beta for macOS and Windows; no supported stable release yet.
AirWiki was featured in Productivity (660.2k followers), Open Source (68.8k followers), Notes (8.4k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 207.2k products, making this a competitive space to launch in.
Who hunted AirWiki?
AirWiki was hunted by Michael Pintos. 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.
Want to see how AirWiki stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Michael Pintos
Hi Product Hunt! We're building AirWiki because useful knowledge often gets stuck in a folder or an old conversation. The next person, or their AI assistant, has to start from scratch to find the same explanation.
AirWiki turns that material into searchable wikis. Here are three workflows we're building it for:
Teams: support needs to know how engineering recovers a failed import. Engineering shares a reviewed troubleshooting Wiki with verified colleagues on the local network. They can find the explanation themselves; connected assistants can retrieve evidence when the source owner permits that AI access.
Students: your notes finally make database normalization click. Turn them into a reviewed Wiki and choose to publish it to the experimental public network, so classmates and other students studying the topic can discover it. Share only material you have the right to publish. Publishers must be reachable, and public readers can retain what you share.
Developers: keep architecture decisions and project conventions in a portable .airwiki Wiki beside your code. Authorized tools such as Codex, Claude Code, and Gemini CLI can consult it in later tasks, so you spend less effort rebuilding context in every conversation. You review the files and decide what enters Git; AirWiki never commits or pushes.
For AI tools, the value is context they can look up: structured concepts, relationships, and relevant passages with source references through MCP. Preparation and retrieval run locally. A connected cloud AI provider may process evidence its app receives; sharing and AI access are separate choices.
Built on an open standard: AirWiki uses Open Knowledge Format (OKF), introduced by Google Cloud. Our independent OKF v0.2 implementation keeps knowledge in readable Markdown files with structured metadata for provenance, verification, and freshness. You can inspect the files, review changes in Git, and export your Wiki for reuse with compatible tools.
Read the open specification: https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md
Current stage: open-source desktop technical beta, not a supported stable release. The 0.3 beta is available for Apple silicon Macs and Windows x64 as v0.3.0-rc.1. The Mac installer is signed and notarized; Windows installers are unsigned. Downloads need no account, and updates are manual. The gallery shows this version's interface with synthetic data. Public federation is experimental, with no supported always-on relay service. Private sharing uses LAN devices; there is no SSO or cloud sync.
We'd love developers, students, and people who manage team knowledge to try one workflow with sample material and help shape it. What context do you keep having to explain again, and where would a shared Wiki help? Feedback, documentation, and code contributions are welcome.
Explore AirWiki: https://airwiki.github.io/airwiki/
Download the 0.3 beta: https://airwiki.github.io/airwiki/#download
Feedback: https://github.com/airwiki/airwiki/blob/main/SUPPORT.md
Report security concerns privately: https://github.com/airwiki/airwiki/security/advisories/new