I integrated LLMs into Chrome the way that Google should have
Ask Steve unlocks the power of LLMs like ChatGPT and Gemini in every web page. It's like Github Copilot but for everyday work in the browser. - Create reusable prompts (“Skills”) that can be used on any web page or text selection. Over 100 are included. - Right click on any page or text-selection to run a Skill on it - Chat with any page to quickly get summaries, extract key information or run a Skill on it - Get help writing, rewriting and editing in any text field with Skills for content creation & editing - Add AI buttons to any web page that enable you to trigger a Skill with 1-click.…
What it does
In the maker’s words, at launch
Ask Steve unlocks the power of LLMs like ChatGPT and Gemini in every web page. It's like Github Copilot but for everyday work in the browser. - Create reusable prompts (“Skills”) that can be used on any web page or text selection. Over 100 are included. - Right click on any page or text-selection to run a Skill on it - Chat with any page to quickly get summaries, extract key information or run a Skill on it - Get help writing, rewriting and editing in any text field with Skills for content creation & editing - Add AI buttons to any web page that enable you to trigger a Skill with 1-click. 100% Free. 100 requests to try it out with no login. If you find it useful, put in your own API Key to connect to any cloud LLM (including free Google Gemini, OpenAI, Anthropic, HuggingFace, etc.), internal API/LLM, or for maximum privacy and zero cost, a local LLM (Ollama, LM Studio, etc.). Short video walkthrough here: https://www.youtube.com/watch?v=hwU5H2ikh1I Let me know what you think!
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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