WeWatch AI - Investigate alerts faster
Your first line of defense against alert fatigue
What it does
As former AWS engineers, we know exactly how chaotic it gets sifting through logs when major services throw alerts. That’s why we built WeWatch. It lets your team literally chat with their infrastructure—correlating CloudWatch, CloudTrail, and logs to auto-generate full RCA reports in minutes, not hours. We’re onboarding a small group of early users who will get direct Slack access to our team, locked-in early pricing, and the ability to shape the product.
Does the same job
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Hi HN! I built AI Baby Monitor – a tiny stack (Redis + vLLM + Streamlit) that watches a video stream and a YAML list of safety rules. If the model spots a rule being broken it plays beep sound, so you can quickly glance over and check on your baby. Why? When we bought a crib for our daughter, the first thing she tried was climbing over the rail :/ I got a bit paranoid about constantly watching her over, so I thought of a helper that can *actively* watch the baby, while parents could stay *semi-actively* alert. It’s meant to be an additional set of eyes, and *not* a replacement for the…
- IMI made a cloud security product that actually saves time2022 · argos-security.io · ▲63
I have been a Cloud Security Consultant for over 13 years and all that experience I put into building ARGOS Cloud Security. Probably the most common thing I've seen was teams spending loads of time manually investigating alerts from security products. Detecting security issues is not what it’s ever been about, but about the investigation of these detected issues. If you don’t have time to investigate everything, then what’s the point? Organizations spend time and money on security consultants to help them investigate their environments, but these are often only point-in-time engagements,…
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nightwatch is a local-first, read-only layer on top of your monitoring. it groups alert storm into incidents, flags noisy checks and has an agent that can investigate for you live systems. You can e.g. jump from the incident into the agent directly. the reason for this weekend project is that we had a kubernetes upgrade that went wrong, and at some point a rollback wasn't possible anymore, so it had to be fixed live during the night while several problems came together. We run a lot of different systems, on-prem and several Kubernetes clusters, and in a situation like that you spend most of…
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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…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2026
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Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
