Hacker News

More context here: https://www.nature.com/articles/d41586-026-00204-9

https://jobswithgpt.com/company-profiles/openai/ https://jobswithgpt.com/company-profiles/advanced-micro-devi...

As expected. Almost the whole company is gone, less than 15 people left in engineering.

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).

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.

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.

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!