bitdrift.ai
The world’s first agentic mobile observability platform
What it does
bitdrift AI is the world’s first agentic mobile observability platform: a real-time, full-fidelity observability system that lets AI agents query mobile user behavior and act on it autonomously. It's built on the bitdrift Public API and bd skills. User journeys, performance metrics, and behavioral changes are all available when your agent needs them, not ten days later waiting for an app release. Early users of bitdrift AI report faster investigations and a 10x improvement in MTTR.
Give AI agents real-time access to unsampled mobile observability data so they can investigate crashes, performance issues, and user journeys directly from customer devices.
Make your agents smarter than anyone else’s: give them access to everything that happens on your customers' devices, unsampled. Have your AI agents find mobile issues before your customers do. Give them real time, accurate views of how humans interact with your apps at scale — even with billions of devices, without any sampling. Finally observe 100% of devices and users. Give your models the ability to work across unsampled data. No log limits, no metrics limits, across millions of devices. Continuously learning, real-time reasoning layer that finds anomalies and patterns in customer journeys. Automatically react to changes in customer behavior. Triage, diagnose, and fix performance issues,…from bitdrift.ai
Does the same job
all alternatives →
- WIWe instrumented Claude Agent SDK using a tiny Rust proxyDec 2025 · laminar.sh · ▲6
At Laminar (https://github.com/lmnr-ai/lmnr) we're building open source AI observability platform in Rust. We obsess over instrumentation DX for our Python and TS SDKs and in this new blog we outline how we made the most seamless way of instrumenting recently released claude agent sdk
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 · 17d 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 · 17d ago · simedw.com