canestackLabs
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Scaffolding for AI, cloud and security.

Canestack Labs is an AI, cloud and security lab. We research and build the systems that hold up modern software: AI agents and models, the cloud they run on, and the security around both.

What we work on Meet Stormglass
The Canestack mark: three canes, bundled and lashed together

What we work on

Three areas, one stack.

AI now runs on the same clusters, budgets and attack surface as everything else. We work across all three, because the hard problems sit where they meet.

AI

Agents, model serving and evaluation. Systems that use models safely, measure what they do, and keep the cost of every call visible.

Cloud

Kubernetes, platforms and spend. Tools that show what is running, what it costs, and what is about to break.

Security

Identity, access and secrets. Least privilege by default, sensitive values masked, and every change approved by a person.

First out of the lab

stormglass

Kubernetes and Docker. Cloud cost coming soon. Free preview

A native desktop client for Kubernetes clusters and Docker containers. Live lists, logs, terminals, YAML editing with a server dry run, and an assistant that runs on the model you choose.

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DockerDocker mode: the containers list with image, state, CPU, memory and ports
Cloud cost ยท soonCloud mode: spend, budget, forecast and waste found, with a daily spend chart (demo data)
KubernetesKubernetes mode: the Pods list of a production cluster with CPU, memory and status

Story

Why "Canestack".

Bamboo canes, lashed together, hold up skyscrapers while they are built.

Across Asia, towers still go up inside bamboo scaffolding. Each cane is light, but bound together they carry the whole building while it is built. They bend in a storm instead of breaking, and they grow back fast.

That is the job we want our work to do: hold up the systems teams build on, from the models to the cloud to the controls around them, and bend without breaking when things get rough.

The mark is the same idea. A cane holds up three tiles: AI, cloud and security.

AI

The models and agents.

Cloud

Where they run, and what it costs.

Security

Who can do what, and what is exposed.

Principles

How we build.

Start from a real failure

Every project begins with something that broke, cost too much or let someone in, in production and in our own hands first. We do not research problems we have only read about.

Work where the three meet

AI, cloud and security are no longer separate fields. Most of what we find interesting sits at a seam between them, so we refuse to staff or scope them apart.

A finding becomes a tool

Results are measured by what someone can run on Monday. If an idea does not survive becoming software, we write down why and move on.

Show the work

We publish what we tried, what failed and the numbers behind it, so others can check us or build on it without starting over.

Roadmap

Where Stormglass is headed next.

  • October 2026

    Stormglass 0.2.1. A native rewrite in Rust for macOS, Linux and Windows, with the assistant panel, the MCP server and Docker mode.

  • Next

    Cloud cost. AWS spend, waste findings and Kubernetes cost allocation, in the same app.

  • Next

    More clouds. Then cost for Google Cloud, Azure and DigitalOcean, in the same app and the same views.

  • Ongoing

    AI and security research. Agents that act safely on real infrastructure, the cost of every model call, and least-privilege access by default.

Questions

What is Canestack Labs?

An AI, cloud and security lab. We research the problems teams hit where those three meet, and build tools to solve them. Stormglass is the first.

Is Stormglass your only product?

For now, yes. It is the first tool out of the lab, and the one we are putting our time into.

Where does my data go?

Nowhere we can see it. Our tools talk to your clusters, models and cloud accounts directly with your own credentials. There is no Canestack server in the path.

Do you collect telemetry?

No. No analytics, no crash upload, and no account. Licenses are signed keys checked on your machine.

Can I use local AI models?

Yes. Ollama, LM Studio, llama.cpp and vLLM are found on localhost automatically. With a local model, nothing leaves your machine.

How do I report a security issue?

Email [email protected]. Please include the version and steps to reproduce.

Contact

Talk to the lab.

General[email protected]Questions, research, press and partnerships Support[email protected]Help with Stormglass and bug reports Teams[email protected]Team plans, invoices and purchase orders Security[email protected]Report a vulnerability

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