Following are my personal thoughts on tech, AI, startups and adoption of AI in Health-Care. You could read more about me here
Text-to-SQL is a demo. Keeping the numbers right for months while the schema moves underneath you is the actual product. Byaan closes that gap with auto-evolving skills: every conversation gets adversarially reviewed, mistakes become skill edits backed by code evidence, ambiguity becomes a question, and subject matter experts hold the merge button.
Episode 3 of Build with Agents. Two things this time: the delegation setup that keeps my Fable 5 limits sane even when I am coding all day, and why I stopped using plan mode in favor of a living artifact.
🎥 Watch the episode. This is part of my Build with Agents video series. Watch Episode 3 on Substack.
Fable 5 is the most incredible model I have used. Not a nicer version of the last one. Another level of increment in capability.
But that is not the part that stuck with me. After days of heavy use, the thing I keep noticing is not what Fable does. It is what Fable chooses not to do itself.
It delegates. And it is good at it. That is the first real glimpse of something I have been waiting for: the LLM router era. This post is the thesis and the playbook: why routing is about to become the default economics, and the exact skill I use to make Fable route work to cheaper models.
I used to be slow at email. Threads sat unread for days. Replies took a weekend of guilt. Archiving was something I did with a mouse, one click at a time.
Superhuman fixed that. It taught me that email is a keyboard problem. c to compose, e to archive, j and k to move, Cmd+K for everything else. Within a month I stopped treating my inbox as a place I visit and started treating it as a queue I clear.
So this is partly a thank-you note. Superhuman genuinely made me productive at email, and I paid $30 a month for years, happily.
I run Claude Fable 5 as my daily driver in Claude Code. It is the best model I have used, and it is priced like it: $10 per million input tokens, $50 per million output. The painful part is that most of what an agent does all day is not deep reasoning. It is reading. Files, web pages, logs, search results, all pulled through the most expensive brain money can buy.
Then I found an Anthropic cookbook that reframed the whole problem: Plan Big, Execute Small. One notebook, one idea: let the frontier model plan and judge, let cheaper models do the mechanical reading. I liked it enough that I turned it into a Claude Code skill, and it has changed how I work.
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.
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.
Anthropic shipped Opus 4.8 today, and along with it Claude Code got dynamic workflows. I have been playing with it for a few hours and it is genuinely impressive.
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.
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.