Wajo
Action agents that get real-world tasks done
What it does
Wajo builds proactive, self-learning action agents that go beyond recommendations and complete real-world tasks. Fo, our first consumer agent, can book appointments, coordinate vendors, handle travel changes, place orders, and also hire agent-human teams to do things it can’t. Teams can also build agents for business operations and customer care.
Wajo – action agents that get things done in the physical world.
Proactive, self-learning, action agents that don't just stop at recommendations. From back-office operations to customer care, build a team of autonomous agents that learns how your business works. Tired of prompting your agents? They run autonomously and make the call themselves instead of waiting for you to drive. When an agent hits something it can't handle, it recognizes that and pulls in the right person or teammate to close the gap. They adapt to you. Every piece of feedback tunes the agent. Basically, the more you use it, the better it gets at handling your requests. Buy credits, spend them on jobs. Unspent credits just sit there, fully refundable, and mildly bored. An action agent…from wajo.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


- SA
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.
AI · 17d ago · simedw.com