Compartment Enterprise
Securely share and scale custom internal apps and workflows
What it does
Compartment gives teams a self-hosted path from AI-built source code to a protected app URL. Developers deploy through the CLI or Git. With the new Kubernetes runtime, teams get isolated project namespaces, NetworkPolicies, gVisor-sandboxed builds and workloads, rolling deployments, durable recovery, and promotion without rebuilding. Operators can install it on a clean Ubuntu VM or an existing Kubernetes cluster while keeping infrastructure and data under their control.
Deploy and share AI-built apps, tools, and automations on infrastructure you control.
Small software should be as easy to share with your colleagues as a Google Doc. Compartment turns working code into software your team can use. One command gives every app a consistent deployment path, a stable URL, and HTTPS. Team access and permissions are built in—no auth implementation for every app. Keep each app isolated, observable, and connected to the private systems it needs. Big clouds were built for big software. Compartment is built for the small apps a team creates for itself. Apps, scripts, agents, and workflows designed around the way a team actually works. Install Compartment on your infrastructure, then deploy and share yourfrom compartment.dev
Does the same job
all alternatives →
- OYOwn your Kubernetes: installation, addons, best practices – as code2019 · github.com · ▲122
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