Talking Tree
Save thousands with lawyer-trained legal AI.
What it does
Enterprise-grade tools. Nonprofit prices. Join 14,000+ entrepreneurs saving thousands in legal costs. Draft, review, and sign contracts in one workflow — plus access attorney drafted templates and tools to safeguard your IP, navigate disputes, collect on invoices and make smarter legal decisions. Crafted by Fortune 500 attorneys and structured as a 501(c)(3) nonprofit to provide you with the same enterprise tools at 1/10th the market price.
Lawyer-trained legal AI for startups and small businesses. Draft, review, sign, store, and manage contracts with enterprise-grade tools at nonprofit prices.
Draft, review, and sign contracts in one workflow — plus tools to safeguard your IP, navigate disputes, and make smarter legal decisions. Crafted by Fortune 500 attorneys. Accessible to everyone. Built by a 501(c)(3) nonprofit · Same enterprise tools. 1/10th the cost Draft, redact, use attorney-built templates, send for signature, and connect with counsel without stitching together separate systems. Talking Tree is a 501(c)(3) nonprofit legal AI platform for startups and small businesses—built to draft and review contracts, manage legal documents, and handle routine legal work at a fraction of traditional cost. Designed by experienced attorneys for dependable everyday business legal work.…from talkingtree.app
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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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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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