RichardHam.co.uk Blog

Tag: Proxmox

  • CrewAI Launch: Building Secure Agent Crews

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    When multi-agent tooling matured in late 2024, it solved one problem and exposed another. Coordination got easier. Risk did too.

    A system that can research, draft, test, and propose changes is useful. A system that can do all of that without a clear approval path is a liability.

    The basic shape

    Agent orchestration
-> local inference
-> secret handling
-> human approval

    That setup sounds obvious now. It did not feel obvious at the time.

    The first production uses were straightforward: security reviews, repetitive checks, and content drafting. The output was good enough to save time, but only when the guardrails stayed in place.

    Lessons that stuck

    • Give each agent the minimum access it needs.
    • Log outputs before anything changes state.
    • Test for prompt injection and bad assumptions early.

    Those three habits did more for reliability than any naming convention or framework choice.

    What changed

    The real shift was mental. I stopped thinking about agents as clever helpers and started treating them like junior team members. Useful, yes. Trusted by default, no.

    That distinction matters. A junior teammate can ask questions and escalate. An unchecked automation chain just keeps moving.

    The safest systems are boring in the right ways. They make decisions visible. They make mistakes recoverable. They let a human step in before the wrong thing becomes the permanent thing.

  • GitHub AI Boom: 65k+ Projects Spark My Homelab

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    In late 2023 the pace of AI tooling changed fast. The cloud options were exciting, but they also made the cost and privacy trade-offs impossible to ignore.

    So I started moving experiments into a local homelab.

    The setup

    The early setup was basic:

    • a GPU-backed VM
    • local model storage
    • simple monitoring from day one

    That was enough to prove the idea.

    What I learned

    Local inference was not perfect, but it was good enough for a surprising amount of work. The bigger models stayed useful for harder reasoning tasks, but a local model could handle a lot of the boring stuff cheaply.

    That changed how I thought about the system. The cloud stopped being the default. It became the exception.

    The real lesson

    Once you run a few workflows locally, the shape of the problem changes. Privacy becomes easier to reason about. Costs become predictable. And you start writing down the routing rules because the routing rules themselves become valuable.

    That was the start of a much longer shift for me, but the important bit was simple: the local stack made the work feel more deliberate.

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