Ko

Kora – An AI-native OS layer written in 370k lines of Rust

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Kora – An AI-native OS layer written in 370k lines of Rust

We've been building this for ourselves over the last year. We wanted the computer from Star Trek, something you just talk to, that knows your context, controls your environment, and runs locally. Our core belief is digital sovereignty in that you should own your context. Your conversations, files, voice, memory, and identity never leave your machine unless you explicitly choose to route through a cloud provider. No telemetry, no accounts, no data collection. The AI that knows the most about you should be the one you control completely. Kora is an operating system layer built around an AI agent. On Linux it runs as the GUI directly in Wayland (OpenGL via femtovg). On macOS it's a native app with a Metal compositor. It's not a chatbot wrapper, it's closer to a full OS where the AI is a first-class service. The stack is ~8 services running locally: - UI: custom window manager with guest app compositing. Apps are standalone Rust binaries that communicate with the host over IPC. - Multi-device: one main machine runs the services and models. Thin clients on other devices connect over QUIC and get the full UI, voice, and tool access. The graph service tracks which clients host which apps and MCP servers. Add a screen in the kitchen, a terminal in the workshop, same agent, same context. - Speech pipeline: real-time ASR, TTS, VAD, wake word detection, barge-in interruption. All on-device. - Tool system: MCP servers and CLI for native OS automation, file management, browser control, calendar, email, terminal, music, messages. The AI doesn't need to simulate clicks it calls native OS APIs. - Chat routing: you can talk to it from Slack or Signal. It's the same agent with the same context, just a different transport. It can still use all its tools from a chat message. - Skills, workflows, and missions. Skills are reusable prompt templates that declare which tools they need. Chain them into workflows. Assign the agent a role (personality, permissions, context) and schedule it on a cron — we call these missions. The agent can run tasks overnight, check in on things, or maintain a recurring process without you touching it. - Context service: identity hierarchy (directives → roles → learnings), semantic search over local files, per-session memory. - Cognition service: background "dream state" that processes and connects information when idle. - App framework: guest apps (browser, terminal, text editor, Doom (yes it runs Doom)) run as isolated processes. The host composites their rendered output. Think Wayland but the compositor is AI-aware. The system can generate its own applications on the fly and they automatically show up in the UI when completed. "please build me a magic 8 ball" Nearly everything is Rust. ~370k lines across the workspace. Multi-user — each person gets their own identity, memory, and permissions. No cloud dependency. runs on local LLMs (Apple Silicon / your own inference) or connects to cloud providers via OpenRouter if you want. Happy to answer questions about the architecture, the pitfalls of one device speech pipelines, CLI / MCP integrations, building a window manager in Rust, or anything else. Demo: https://www.youtube.com/watch?v=40TyKAKNvJc

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, agent · Missing: agents, cursor, claude
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, way · Missing: mobile apps, ios, entrepreneurs
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host, calls · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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