Journal

Applied AI and the Hundred-Year Advisory Relationship

Multi-generational wealth advice is the most personal, judgment-heavy work in finance. The question isn't whether to automate it, but where AI carries the weight and where a human still has to.

How to scale a deeply personal advisory relationship across a family and decades without turning it into a robo-advisor.

From Marketplace to Operating System: Applied AI and the NIL Back Office

The first wave of NIL software solved discovery — match an athlete to a brand. The revenue-sharing era turned NIL into an ongoing operation someone has to run. Marketplaces don't run operations. Operating systems do, and that's where applied AI earns its keep.

Discovery in the NIL economy is solved. The unsolved, expensive part is the back office — deals, disclosures, compliance, payments — at scale. Here's where applied AI fits, and where judgment still has to win.

Applied AI in Sports Media: When Every Athlete Is a Distribution Channel

Teams and leagues are expected to produce thousands of personalized clips a week for athletes who each out-reach the old broadcast networks. The supply of moments hasn't changed; the demand for content cut from them has exploded. That gap is where applied AI earns its keep — or gets built badly.

The athlete became the distribution channel, and almost no one can feed that channel by hand. Here's where AI actually fits in sports media — and where judgment still has to win.

The Future of AI in College Athletics: What NIL Taught Us About Building for Chaos

NIL turned college sports into a fast-moving, lightly-governed market overnight. That's exactly the kind of mess AI is good at — and exactly the kind of mess that punishes software built without judgment. Here's where we think AI actually fits.

HARNESS: Turning AI Fog Into a Buildable Idea

AI made it easier to generate ideas. That is not the hard part anymore. The hard part is turning a fuzzy possibility into something a team can actually evaluate, scope, and build.

After Boulder Startup Week, the thing that kept sticking with me was not that teams need more AI ideas. They have plenty. What they need is a way to turn the fog into a real product shape. That is what the HARNESS Canvas is for.

Two Kinds of Google Keys, Two Endpoints

The Gemini key that rejects your request with 403 API_KEY_SERVICE_BLOCKED is almost certainly a Vertex key being sent to the AI Studio endpoint. Here is how to tell them apart, and which endpoint to use.

We spent an hour chasing what looked like a billing issue and turned out to be a key-format mismatch. The key worked. Google was just telling us we had pointed it at the wrong building. If you are shipping a Gemini integration, this is the one-page reference we wish we had read first.

Design Tokens as Prompt Anchors

When you hand a generative image model an adjective, it drifts. When you hand it your design-system tokens by exact hex, it stops drifting. The discipline we found after shipping it wrong once.

We shipped the first cut of our CMS image generator with preset language like "hand-drawn whiteboard sketch" and "muted red accent." The output was childish chalkboard stick figures — nothing like the pen-and-ink illustrations already on our blog. The fix was not a better adjective. It was the same move you use everywhere else in design: stop naming things with vibes, start naming things with tokens.

Context Laundering

The one use case where spawning a sub-agent always pays off. It is not parallelism. It is triage for your main context window.

We use the word parallelism when we talk about sub-agents. Most of the time we are not actually running in parallel. We are protecting the main context from heavy reading. That is the move.

Red/Green TDD When Your Dep Is a Generative API

How to test-drive code that integrates with Gemini, Claude, or any non-deterministic API — without pretending the API is deterministic.

Gemini does not care about your unit tests. But your unit tests can still be green, fast, and trustworthy — if you put the seam between your code and Gemini in the right place. Here is the pattern we used to ship a CMS image-generation feature in under a day, with every layer TDD'd and the Gemini boundary mocked without hand-waving.

Explaining Claude Code to a Non-Technical Audience

What Claude Code actually is, why it changes how work gets done, and where it fits in a business (and where it doesn't).

If your engineers keep mentioning Claude Code and the name alone does not tell you much, this is the plain-English explanation. What it is, why it is different from a chatbot, and where it actually fits.

How to Access Claude Code in Your Organization

Starter paths from a solo developer trying it over lunch to a full company rollout with billing, auth, and audit in place.

Getting Claude Code into your team doesn't have to start with a committee. Here are the paths we've watched work, from a single dev with a free trial to a company-wide rollout with governance and audit, plus the first 30 days we'd actually run.

The Sub-Agent Skill Trap

Spawning a sub-agent looks like the obvious move for any multi-step task. Half the time it is the wrong one, and the signals are quieter than you think.

Sub-agents feel like leverage: one call, many workers, results in parallel. But most of the time we reach for one, the work was never parallel to begin with. Here is how to spot the trap before you fall in.