RichardHam.co.uk Blog

Tag: AI

  • 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.

  • ChatGPT Enterprise: My First Steps into AI-Assisted IT

    ChatGPT Enterprise: My First Steps into AI-Assisted IT
    Published: September 25, 2023 (retrospective)

    2023 marked my pivot from 25+ years of pure IT/cybersecurity scripting to blending AI into daily workflows—starting with OpenAI’s ChatGPT Enterprise launch in late August. As a fractional IT Director managing M365 environments and Proxmox homelabs, I was sceptical: could AI handle PowerShell automation without hallucinating disasters? This post recaps those early experiments, wins, and the spark that ignited my AI journey.

    The Catalyst: Enterprise AI Goes Live

    ChatGPT Enterprise dropped on August 28, 2023, promising admin controls, data privacy, and unlimited GPT-4 access—perfect for SME cybersecurity without the free-tier limits. I spun it up immediately for real client work: generating Intune policies, parsing M365 audit logs, and drafting Bash scripts for QNAP backups. No more hours tweaking regex—AI nailed 80% on first try.

    Early tests:
    – Converted manual PowerShell M365 mailbox audits to reusable functions
    – Automated DD-WRT router configs for client VPNs
    – Brainstormed cPanel/WHM hardening checklists

    Key Wins and Pitfalls

    Q3 Milestones:
    – September: First AI-generated Intune deployment script—deployed live, zero errors. Saved 4 hours per client.
    – October: Ollama early access teased local runs, but cloud GPT-4 crushed complex queries.
    – November: GitHub’s generative AI repos tripled to 65k+, inspiring my first LocalLLM-Router sketches.

    Experiment Time Saved Issues Found
    M365 Audits 4h/client Overly verbose outputs
    Intune Policies 2 days/project Needed fact-checking
    Backup Scripts 3h/setup Hallucinated syntax (fixed iteratively)

    Pitfalls taught resilience: AI excelled at boilerplate but flopped on edge cases—my cybersecurity instincts always double-checked outputs.

    Lessons from the Frontlines

    1. Start small: Use AI for scripting grunt work, not strategy.
    2. Local potential: Ollama’s October buzz hinted at cost escapes from cloud tokens.
    3. Governance early: Even then, I logged prompts/outputs for audit trails—foreshadowing SentinelForge.

    ChatGPT Enterprise wasn’t a replacement; it amplified my expertise, prepping 2024’s Control Tower orchestration.

    Ready for AI-secured IT? Contact me for M365 audits or homelab setups.

    Next: GitHub AI Boom and My Homelab Shift (Nov 2023).

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