Osuite
Root cause and a fix, in under five minutes
What it does
Osuite is production intelligence for going from a P0 incident to RCA + fix in minutes. System breaks → runtime graph → AI investigation → root cause → fix → <5 min. Unlike traditional observability, Osuite correlates telemetry, code, deployments, and runtime context into a context mesh, giving both engineers and AI the context needed to investigate. An AI investigation agent works directly in your IDE, helping you understand what broke, why, and how to fix it.
Osuite is AI-native observability: instrument any stack in minutes, then get an investigation agent right in your IDE that finds the root cause and a fix — in minutes, not hours or days.
AI-native observability. One agent instruments your stack in minutes; another lives in your IDE and hands you the root cause and a fix — tracing any failure from a click to the query behind it. Observability used to mean weeks of setup and walls of dashboards you had to read yourself. AI-native observability does the work for you — from signal, to root cause, to a fix, without leaving your editor. The Osuite instrumentation agent wires up logs, traces, and metrics across any language or framework in about five minutes. It's built on OpenTelemetry, the open standard — if your stack emits telemetry, Osuite speaks its language. No custom agents, no rip-and-replace, no vendor lock-in. Most…from osuite.io
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Progress AI Observability30d ago · telerik.com · ▲168Trace, evaluate, and improve AI agents in production
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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.
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