MOTA
AI agents that follow your rules, not their imagination.
What it does
MOTA is a governed execution runtime for high-consequence workflows. Instead of letting LLMs improvise, MOTA separates AI planning from deterministic execution. It translates user intent into a strict execution graph to enforce business policies, retrieve live data, and route tasks conditionally. With human-in-the-loop approval gates, durable execution states, and transparent audit records, MOTA guarantees that sensitive customer support actions are safe, predictable, and fully verifiable.
Finish what AI starts. MOTA is the governed runtime for consequential enterprise AI workflows, starting with Salesforce Agentforce: done right, every time, with proof.
AI agents answer questions. MOTA completes workflows: planned, verified, and audited. Starting with Salesforce Agentforce. Works with the Agentforce licence you already hold. Deploys on your infrastructure. Our standard walkthrough uses live Salesforce data. Simple queries work everywhere. Then we ask this one: The query is understood. The workflow is not completed. Someone on your team finishes it. MOTA maps every task before anything runs, then completes them in order. The log shows each one. The same demo runs four ways. MOTA checks every request against refund policy and live Salesforce data before anything fires. Sometimes the right answer is no, and it says so. Ask for the 15-minute…from motaai.dev
Does the same job
all alternatives →More ai this month
the category →
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 · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com


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…
AI · 26d ago · cactuscompute.com


Launched alongside, August 2026
the whole month →- TL
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 · 16d ago · simedw.com