INARY
AI work platform built for organizations, not individuals
What it does
INARY is an AI work platform designed for organizations, not individual AI chats. Teams can work with AI in shared threads, retain context across organizational, departmental, personal, and thread-level memory, collaborate across languages, and connect everyday work such as email and external collaboration. After completing a closed beta, INARY is now opening its public beta in the United States and Japan.
Keep questions, INARY answers, teammates’ corrections, and decisions in shared Threads/Chats, then carry that context into the organization’s next task.
INARY is a business workspace with built-in LLMs from four AI brands—ChatGPT, Claude, Gemini, and Grok. It enables teams to share AI conversations, revisions, and decisions. By retaining the context of your organization and its work, INARY accelerates digital transformation across your company. Individual AI use can spread while measurable returns, enterprise-wide adoption, and shared knowledge remain out of reach. These three sources point to the same issue: the challenge is no longer whether a company uses AI, but whether it can connect that use across the organization. In MIT NANDA’s 2025 preliminary study, about 95% of organizations in the study reported no measurable return from their…from inary.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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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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