More context here: https://www.nature.com/articles/d41586-026-00204-9
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Show HN: SpeechOS – Wispr Flow-inspired voice input for any web app
2026-01-21 @ 15:52:15Points: 5Comments: 5
I was inspired by Wispr Flow and wanted the same workflow inside business apps (CRMs, docs, forms, support tools), not just a standalone tool. It’s saved me a massive amount of time vs typing.
How it works: add a couple lines of JS plus an API key, and SpeechOS shows a small mic widget on every text field.
Live demo: https://www.speechos.ai/
(Click a text box and the mic widget appears; click the gear icon to see settings, custom vocabulary, and snippet configuration.)
Users can:
- Dictate: speak naturally, real-time voice to polished text (punctuation, no filler/typos)
- Edit: say “make it shorter”, “fix grammar”, “translate...”
- Command: define Siri-style app actions (for example, “submit form”, “mark complete”), and we match intent to your commands
It also supports:
- Custom vocabulary: domain terms and names (product names, acronyms, jargon) so they transcribe correctly
- Text snippets: saved reusable blocks of text you can insert by voice (for example, “my signature”, “disclaimer”, “my address”)
Why: text entry speed and accuracy still matter for productivity tools. A large-scale text entry dataset with 37,370 participants showed an average typing speed of 36.2 WPM and ~2.3% uncorrected errors. In speech research, speech recognition was about 3× faster than keyboard input and had ~20.4% lower error rate for English text entry. (https://hci.stanford.edu/research/speech/)
SpeechOS is currently in beta and free for now. Sign up at https://app.speechos.ai/accounts/signup/ and enter this HN-only beta code: HN-JFc74cVC (please don’t share outside HN).
Links:
SDK repo: https://github.com/speechos-org/speechos-client
Demo: https://www.speechos.ai/
Signup (code: HN-JFc74cVC): https://app.speechos.ai/accounts/signup/
I’d love feedback on:
1) Where this is most valuable in your stack (notes? docs? CRM entry? support macros?)
2) What you’d want from voice commands/snippets configuration
3) What would make you comfortable shipping this (privacy/security, latency, pricing)
If you’re building anything in voice AI/dictation, I’d be happy to swap notes (david@speechos.ai).
Skip Is Now Free and Open Source
2026-01-21 @ 15:20:53Points: 198Comments: 58
Show HN: ChartGPU – WebGPU-powered charting library (1M points at 60fps)
2026-01-21 @ 14:54:56Points: 411Comments: 131
The core insight: Canvas2D is fundamentally CPU-bound. Even WebGL chart libraries still do most computation on the CPU. So I moved everything to the GPU via WebGPU:
- LTTB downsampling runs as a compute shader - Hit-testing for tooltips/hover is GPU-accelerated - Rendering uses instanced draws (one draw call per series)
The result: 1M points at 60fps with smooth zoom/pan.
Live demo: https://chartgpu.github.io/ChartGPU/examples/million-points/
Currently supports line, area, bar, scatter, pie, and candlestick charts. MIT licensed, available on npm: `npm install chartgpu`
Happy to answer questions about WebGPU internals or architecture decisions.
Show HN: UltraContext – A simple context API for AI agents with auto-versioning
2026-01-21 @ 14:48:10Points: 13Comments: 10
After two years building AI agents in production, I experienced firsthand how frustrating it is to manage context at scale. Storing messages, iterating system prompts, debugging behavior and multi-agent patterns—all while keeping track of everything without breaking anything. It was driving me insane.
So I built UltraContext. The mental model is git for context:
- Updates and deletes automatically create versions (history is never lost)
- Replay state at any point
The API is 5 methods:
uc.create() // new context (can fork from existing)
uc.append() // add message
uc.get() // retrieve by version, timestamp, or index
uc.update() // edit message → creates version
uc.delete() // remove message → creates version
Messages are schema-free. Store conversation history, tool calls, system prompts—whatever shape you need. Pass it straight to your LLM using any framework you'd like. What it's for:
- Persisting conversation state across sessions
- Debugging agent behavior (rewind to decision point)
- Forking contexts to test different flows
- Audit trails without building audit infrastructure
- Multi-agent and sub-agent patterns
What it's NOT:
- Not a memory/RAG system (no semantic search)
- Not a vector database
- Not an Orchestration/LLM framework
UltraContext handles versioning, branching, history. You get time-travel with one line.
Docs: https://ultracontext.ai/docs
Early access: https://ultracontext.ai
Would love feedback! Especially from anyone who's rolled their own context engineering and can tell me what I'm missing.
Show HN: Semantic search engine for Studio Ghibli movie
2026-01-21 @ 14:01:04Points: 6Comments: 2
Describe a dreamscape like "flying through clouds at sunset" or upload an image, and it finds visually similar scenes from the films.
Live demo: https://ghibli-search.anini.workers.dev/
Full Cloudflare stack: Workers, AI Search, R2, Workers AI
Open source: https://github.com/aninibread/ghibli-search
Would love feedback on the search quality and any ideas for improvements!