RichardHam.co.uk Blog

Tag: Ollama

  • How I Stopped AI Workflows From Sprawling

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    By mid-2024 I was juggling several AI tools at once. The work was useful, but the coordination started to eat the time I was trying to save. Every extra prompt, every follow-up, every half-finished idea added friction.

    So I changed the shape of the work. Ideas became GitHub issues. Research happened first. Code changes went through review. Nothing moved forward without a human looking at it.

    The workflow

    Idea -> GitHub issue -> research -> code -> review -> deploy

    The important part was not the diagram. It was the discipline around it.

    • Every item carried a priority and a budget.
    • The review step stayed human.
    • The system ran overnight so I could review the output in the morning instead of reacting in real time.

    What it changed

    The main win was not speed for its own sake. It was clarity. I spent less time babysitting the process and more time making decisions that mattered.

    A rough summary of the difference:

    Metric Before After
    Code hours per week 20h 4h
    Monthly token spend £120 £12
    Projects shipped per month 1 4
    GitHub commits per month 45 200+

    What made it work

    1. Keep the routing local when you can.
    2. Keep write access scoped.
    3. Treat the repository as the source of truth.

    That combination is what made the setup durable. Not the tools, really. The rules.

    I still think that is the part people miss. AI does not remove the need for process. It makes process more obvious.

  • 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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