Keith Townsend

CTO Turned Advisor | Helping Vendors Resonate and IT Leaders Execute. Engage with my virtual twin Independent Advisor.

[Keith Townsend @CTOAdvisor]

I published my first full framework for applying Deterministic Code in the Loop.

The use case is cloud migration, but the pattern is bigger than migration.

For the past year, the industry has talked a lot about human-in-the-loop AI. That is useful, but it does not scale as the primary control model for enterprise automation.

A human cannot inspect every recommendation, every code change, every migration step, every policy decision, and every exception across a large application estate.

So the question becomes: what sits between probabilistic AI and human review? My answer is deterministic code in the loop.

In this framework, LLMs do not own the migration process. They are called only when developer-like adaptation is required. The control plane owns the process.

- Playbooks constrain the work.
- Deterministic code executes known transformations.
- Agent harnesses provide controlled execution.
- Validators define done.
- The landing zone validates fit.
- Traceability records authority.
- Humans handle exceptions and authority boundaries.

That distinction matters.

Human-in-the-loop is not wrong. It is just insufficient by itself.

At enterprise scale, humans should not be the first line of defense against every AI mistake. They should sit at the authority boundaries: low-confidence decisions, exceptions, policy conflicts, failed validators, and material risk.

Everything else should be constrained, tested, validated, and recorded by deterministic systems.

Cloud migration makes the pattern visible because the risk is obvious. You cannot simply let an agent assess, plan, refactor, validate, and migrate workloads without knowing where decision authority lives.

This is why I frame the problem as a migration control plane, not an AI migration factory.

The core loop:

- LLM proposes.
- Playbooks constrain.
- Deterministic code enforces.
- Agent harness executes within bounds.
- Landing zone validates.
- Traceability records authority.
- Humans resolve exceptions.

That is the practical model for using AI in enterprise automation without letting decision authority drift into the model.

Read the framework here:

https://t.co/gW7gJt9gnq

[Keith Townsend @CTOAdvisor]

My lab site has been up for six weeks. I've published 16 labs, with one additional vendor-sponsored lab currently in review.

AI has absolutely changed the way I work. I've shared before how it's enabled me to do more hands-on lab work than I've done since leaving the engineering ranks.

What AI has really unlocked, though, is my ability to share that work.

I've always created and tested interesting things in the lab. What I couldn't do was synthesize all of it, turn the experiments into something coherent, and publish the findings at this pace.

Now I can.

[Keith Townsend @CTOAdvisor]

I started the day fine-tuning a 70B parameter model on @AMD MI300X accelerators. I ended it answering a harder question: how do I serve both the human readers and the machine readers of my labs?

My content strategy has been AI-first in every sense. I create using AI, for AI. That debate is a post for another day.

However, the consistent feedback is that AI-first content is hard for a human to actually read.

Every one of my sites already has two layers. HTML was never machine-friendly, so each page carries a dedicated JSON mirror of its content.

Tonight I broke that mirror on https://t.co/3v9LHXEvdF. The machine-readable surface stays untouched. The human surface is now written for humans: every lab reads like a post instead of a spec sheet.

Sounds simple in concept. In practice, we'll see. Operationalizing this at lab cadence is the interesting part.

[Keith Townsend @CTOAdvisor]

@HotAisle @AMD @nvidia This has been in my head all night. You have me creating a whole new data structure to solve for this.

[Keith Townsend @CTOAdvisor]

I spent $29.75 of @HotAisle's money. Or at least in credits to seperate myth from truth. What did I learn about @AMD AI Accelerators vs. @NVIDIA? Is there a real hardware moat? Is AMD's software infrastructure lacking? Is Jon blowing smoke about his infrastructure?

The most important thing for this medium, AMD Neoclouds are real. I put in a request to gain access to NVIDIA GPUs via hyperscalers and a NEO cloud a month ago. I'm still waiting.

I got access to an AMD MI300X in like 15 minutes and did some real work. That's that work?

Here are the receipts.

https://t.co/iVQLgboUof

[Keith Townsend @CTOAdvisor]

@QuinnyPig Staying disciplined is the problem. These things are temptingly good. Resisting punching the first hole in the sandbox is the challenge. Ask me how I know.

[Keith Townsend @CTOAdvisor]

@HotAisle @AMD @nvidia You only half-joke. But I'll never win this game writing for humans first. layer2c is a bet on AI.

[Keith Townsend @CTOAdvisor]

@HotAisle @AMD @nvidia Thanks. That's more of a feature. I don't actually expect humans to read any of it. I expect them to ask Grok and ChatGPT for the TLDR and move on. It's all for the machine.

[Keith Townsend @CTOAdvisor]

@RonOglesby Ha... Winframe... That's a throwback. Great story and thanks for sharing.

[Keith Townsend @CTOAdvisor]

How did you get into technology? I didn't come the traditional route (whatever that is). I always wanted to do tech as a living, even as a kid. I didn't go to college but stayed a power user.

After not getting a promised motion working for Hilton, I set out to purposefully find a job in IT.

I had a scheduled interview for an ISV helpdesk position. After being an hour late because I couldn't find the office, I called the hiring manager using a pay phone.

She encouraged me to still try to make it. I got the job, and 27 years later, I'm the CTO Advisor.

[Keith Townsend @CTOAdvisor]

If your organization can’t say who holds decision authority at the reasoning layer, you don’t have an AI strategy yet. You have someone else’s. https://t.co/k3P3xTek3V

[Keith Townsend @CTOAdvisor]

Finishing up some fine-tuning inside of @HotAisle on some @AMD gear. I couldn’t get @Nvidia gear to do it. I’ve been trying for over a month.

I’m going to write it up as a lab but it’s kind of uneventful. It worked as planned. It was an easy onboarding. Jon hooked me up with more than enough credits to do the work.

The signup and administration via TUI took me back to days of using Telnet to check email.

But after I got Claude my ssh keys it was smooth sailing.

[Keith Townsend @CTOAdvisor]

My first time playing around with an AMD MI300... Where have you been my whole AI lab life?

[Keith Townsend @CTOAdvisor]

DeepSeek v4 Flash on two Nvidia Sparks is far more than useful. I'm now looking for the edges of what it can't do. https://t.co/BU6diGKwk5

[Keith Townsend @CTOAdvisor]

I spent my morning fixing things that weren't broke when I woke up... Happy Monday.

[Keith Townsend @CTOAdvisor]

@umang147 I’ve only used it as a headless harness in my bench testing. I’m pretty solid in Claude Code for day-to-day use. I’m going to give Codex a run this week.

[Keith Townsend @CTOAdvisor]

Notice the coding debate is no longer Opus vs. GPT. The debate is Claude Code vs. Codex. We are in an era where the models are good enough. The battle is now in the agentic harness. Which harness allows me to get the most out of my agentic workforce?

[Keith Townsend @CTOAdvisor]

@umang147 OpenCode seems like the consensus third option. It’s why people like OpenAI as they allow you to bring Codex to it.

[Keith Townsend @CTOAdvisor]

AI has rekindled my love for local hardware.

I love me some GCP and OpenAI for API-driven applications. However, there's something about not needing to worry about an unexpected API bill or debating whether the experiment is worth burning some cloud credits on.

[Keith Townsend @CTOAdvisor]

@LawrenceDCodes We went to the 50 Aniversary concert last year. I was very proud of the culture. Top-notch production and performance.

[Keith Townsend @CTOAdvisor]

I'm surprised that the most utility I'm getting out of the 2nd Nvidia Spark isn't clustering. While being able to run DeepSeec v4 Flash is a legit capability, being able to run two experiments at the same time is an unlock.