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Sinain — The Ambient Intelligence
Eyes and ears for your AI agents and your teammates.
A private Context OS that captures your screen and audio, distilling them into a structured knowledge graph — accessible from MCP, a web UI, an invisible HUD overlay, and shareable peer-to-peer between users. MIT-licensed.
Hey Product Hunt,
I'm Igor. I built Sinain because feeding context into AI agents
became more time-consuming than the work itself. My fleet of
agents grew, the inbound signal grew faster, and I kept losing
5 minutes at the start of every coding session re-pasting old
decisions, screenshots, and notes into the prompt. So together
with my team, we built a tool that gathers it live from your
screen and audio, distills it into a private knowledge graph,
and serves it to any MCP-compatible agent.
Worth knowing:
Privacy-first: Default mode uses OpenRouter (token usage tracked).
Paranoid mode runs entirely on-device with Ollama + whisper.cpp —
zero network at runtime. The HUD overlay is invisible to screen
capture, so it never appears in recordings or screen shares.
Benchmark: 82.8% IPR on LongMemEval (ICLR 2025)
Sharing: Knowledge sharing between users is peer-to-peer over
WebRTC. The recipient's machine pulls from yours directly; their
agents inherit the context through the same MCP.
For now: macOS only (Windows on the roadmap). MIT-licensed.
`npx @geravant/sinain@latest start`
Happy to answer anything in the comments.
How do you handle capturing sensitive stuff like passwords or private messages, is there a quick pause mode?
I’m Dmitrii, also working on Sinain.
The way I think about it: better context = better agents.
A model can be very capable, but if it only sees the current prompt, it misses a lot of the real work: decisions from yesterday, rejected approaches, call context, screenshots, debugging trails, constraints that changed, etc.
But the answer is not “dump all history into the prompt” — that becomes noisy and contradictory fast.
What we’re building is the layer in between: capture more real work, distill it into structured memory, and give MCP-compatible agents the relevant context when they need it.
Still early and macOS-only, so feedback from people actually using agents every day would be super valuable.
Ambient intelligence is the next frontier. How do you handle the privacy aspect of an agent that has 'eyes and ears'? Is all the processing done locally on-device?
About Sinain — The Ambient Intelligence on Product Hunt
“Eyes and ears for your AI agents and your teammates.”
Sinain — The Ambient Intelligence was submitted on Product Hunt and earned 9 upvotes and 7 comments, placing #51 on the daily leaderboard. A private Context OS that captures your screen and audio, distilling them into a structured knowledge graph — accessible from MCP, a web UI, an invisible HUD overlay, and shareable peer-to-peer between users. MIT-licensed.
Sinain — The Ambient Intelligence was featured in Productivity (651.7k followers), Open Source (68.4k followers), Artificial Intelligence (468.6k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 258.7k products, making this a competitive space to launch in.
Who hunted Sinain — The Ambient Intelligence?
Sinain — The Ambient Intelligence was hunted by Igor Gerasimov. 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 Sinain — The Ambient Intelligence stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Igor Gerasimov