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2026

  • 8 min read

Agent Workflows & Loops

Episode 1 was about using agents to ship faster. Episode 2 is about the part agents still have not solved: production follow-through.

Writing code by hand is mostly automated now. Shipping to production is not, and shipping means more than getting a PR merged. It is testing the feature live, telling your team and your customers how it works, watching logs and analytics for a few days, and iterating on what you learn. Loops are what finally help with that.

A workflow is not just a clever prompt. It is the shape around the agent. What does it read? What does it ignore? When does it fork? When does it stop? When does it wake you up?

That is the difference between a useful long running agent and an expensive tab that slowly drifts away from reality.

🎥 Watch the episode. This is Episode 2 of my Build with Agents video series. Watch it on Substack.

  • 6 min read

Your Claude + Codex Setup, and Why You Shouldn't Fear the CLI

This is the first episode of Build with Agents, where I record my coding agent setup, the ergonomics and developer tools I have wired up locally to stay productive. I will keep these short and ship them often, because my setup keeps changing and I would rather share what I have now than wait for it to be perfect.

🎥 Watch the episode. This is Episode 1 of my Build with Agents video series. Watch it on Substack.

  • 5 min read

Open Sourcing Byaan, Your Company's AI Data Analyst

Today I am open sourcing Byaan, a small AI data analyst that runs close to your database and answers the long tail of "can you pull this real quick?" questions that pile up at every startup.

About ten startups are already using it day to day. I quietly shared it with founder friends over the last few months, and the same pattern kept showing up: their customer success and ops teams stopped pinging engineering for one-off data questions, and engineering got hours back every week.

  • 4 min read

I Built My Own Personal Finance App, and Now I Want More Personal Software

I do not think AI will replace every app with one giant productivity blob.

I do think AI changes something more interesting: it makes small, personal software worth building again.

There are places where I still want real software. Finance, healthcare, taxes, legal work, anything with consequences. I do not want a loose chat interface guessing its way through that. I want software with a database, a model of the domain, boring reliability, and a clear relationship with the data.

AI is useful there, but only when it sits on top of a system I trust.

That is why I built Fino, a local-first personal finance app that lets me connect my bank accounts, import the accounts Plaid misses, and then talk to my money from Claude.

Not "chat with a spreadsheet" as a demo. More like: "why did our spending feel weird this month?" and then getting an answer from my actual transactions, local rules, savings goals, recurring subscriptions, and financial memory.

  • 5 min read

Claude Writes the Code. You Run the Loop.

I have been building a lot with Claude recently. Obviously, you can ship code very quickly now. But how do you plan large features, understand UX, brainstorm different patterns, and then ship across 3 repos in an established codebase without breaking things?

The goal is to put large features in front of customers in weeks, not months. Test your assumptions and iterate on top of real feedback.

  • 5 min read

Your AI Coding Agent Can Exfiltrate Your Credentials. You Would Never Know.

I spent last night configuring Claude Code's security and realized something uncomfortable: for months, I had been running an LLM with unrestricted access to my terminal. It could read my SSH keys, browse my AWS credentials, curl data to any endpoint, and push code to production. I just never thought about it because the tool was helpful and nothing bad had happened yet.

That is exactly the kind of reasoning that gets production databases dropped.

  • 10 min read

The Tribal Knowledge Problem Nobody Is Solving for Analytics

Your AI can write SQL. It just has no idea what the data means.

I have spent the last four years building AI products in healthcare. Our databases have columns like amt_1, stat_cd, eff_dt. A model looking at raw schema has no way to know that amt_1 is patient copay in one table and coinsurance in another. That stat_cd means enrollment status, not statistical code. That eff_dt is the date a policy became active, not when something happened.

This is tribal knowledge. It lives in the heads of the three people who built the database. It is not documented anywhere. And it is the reason text-to-SQL fails in production.