Articles
Mostly about building products, healthcare tech, and lessons learned along the way.
Mostly about building products, healthcare tech, and lessons learned along the way.
21 articles published in 2026.

An L2 support engineer that's just Claude Code on a /loop, with all its state living in Slack reactions. No app, no database, no deploy — and it only pings you when it's real.

Most teams asking for AI don't have a model problem. They have a "which problem is even worth it" problem. Here's what a real AI audit surfaces — and why "don't build this" is often the most valuable line in the report.

Production voice-AI has converged on a pattern: graph-based conversations, separated decision and response prompts, synthetic-call regression testing, and per-component latency budgets. Why the stack looks the way it does — and what most teams are still missing.

Everyone keeps announcing the death of prompt engineering. They are describing the symptom, not the shift. The loops you used to run by hand — refine, retry, verify, learn — moved out of your head and into infrastructure. Four of them, simultaneously.

Palantir built a company on the idea that software alone isn't enough: you need engineers embedded with customers. That model has a name, a cost, and a hidden technical debt time bomb most B2B companies are quietly sitting on.

Most healthcare AI companies are failing for the same reason. The ones winning are running the same playbook: one Palantir figured out before anyone called it AI. Forward-deployed engineer. Ontology. Integrations. Then AI tooling.

I built a tool that turns GitHub issues into pull requests using a three-agent pipeline. That's the boring part. The interesting part is what happens when you stop thinking about AI agents as productivity tools and start thinking about them as a workforce, and build a world for them to live in.

Mirth democratized healthcare integration. Then NextGen acquired it, and the world moved on. What the next generation of integration tooling looks like, and why the transition was inevitable.

Most teams skip evals because the process feels overwhelming. The three steps that make eval-driven development achievable: label a small dataset, calibrate an LLM evaluator to human judgment, then iterate configs against the harness.

Updated for 2026: lessons from two years using Claude Code in production. Context engineering, real eval frameworks, model economics, and agent workflows. What works.

I've survived three tech recessions. Lost my job in one, held the axe in another. The AI boom changed the rules again. The updated playbook for 2026.

Reaching principal is the first rung of a new ladder, not the last rung of the old one. What nobody told me about the ownership-autonomy paradox, how leverage works at this level, and the charter model I wish I'd had from day one.

ES Modules have been the supposed future of JavaScript for nine years. Only 9-27% of the ecosystem has adopted them. What's really going on, and how to survive until the ecosystem commits.

Most engineering leaders excel at one of three pillars and quietly fail at the other two. The three are: providing direction, removing obstacles, foreseeing change. What it takes to build strength across all three.

The open-weight ecosystem has matured faster than most engineers realize. MoE proliferation, hybrid attention, and extended context windows are changing what's deployable on-premise. That matters more than ever for healthcare AI.

In 2017, Andrej Karpathy argued that neural networks would replace explicit logic as the dominant programming paradigm. Nine years later, that prediction has fully landed. The implications for how we build software are bigger than most engineers want to acknowledge.

Normal career metrics: title, pay, team size. They tell you how you're doing relative to others. They don't tell you whether you're growing. Three metrics that do.

The 'RAG is dead' narrative is wrong, but it's wrong in an interesting way. What kills LLM context quality in production, and what to do about it.

Q-LoRA + SFTTrainer + Flash Attention v2 means you can fine-tune a 70B parameter model on 24GB of VRAM. What that looks like end-to-end, what it costs in quality, and when to just use the API instead.

I've made bets that paid off because of timing and bets that paid off because of compounding. Confusing the two is how careers stall.

The prediction was: bigger models win. The reality was: DeepSeek R1 rewrote the rules in January and nothing was the same after that. What 2025 taught us about reasoning, inference-time compute, and the economics of intelligence.