Othor AI 2.0
Your AI Head of Intelligence
What it does
BI assumes you have an analyst, a warehouse, and time. Leaders have none. Othor inverts it: connect a source, and agents start analyzing on their own. 10 new features, including Data Integration Agents (100+ sources, no migration), Analytical Agents, Decision Briefs, daily Big Picture reports, Document Intelligence, Chat with Data, Dashboards, and a Model Selector for your own LLMs. They compose - sources feed agents, agents feed briefs, briefs feed Big Picture. One pipeline, three altitudes.
Othor AI is the AI-native business intelligence platform that helps teams launch fast, data-driven pages with modern analytics and storytelling.
Signal reports, decision briefs, and answers to questions you never thought to ask. Advanced data science, simplified. A new kind of business intelligence built on the best practices of world’s elite intelligence agencies. Othor AI is the AI-native, fast, easy, and secure alternative to traditional business intelligence tools like Power BI, Tableau, and Looker. Built from first principles for the way businesses actually work. Othor AI can understand any data. Simply connect to your data sources. It scans through all your data and unlocks every signal in less than 5 minutes. Runs fully automated out of the box. Connect your data once and watch hundreds of insights and visualisations emerge…from othor.ai
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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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Launched alongside, August 2026
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Life & fun · 10d ago · louisabraham.github.io
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
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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