Following are my personal thoughts on tech, AI, startups and adoption of AI in Health-Care. You could read more about me here
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.
Like many of you, this tweet from Andrej Karpathy is what pushed me to actually try this. The whole post is worth reading, but the core idea:
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.
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.
There's a lot of hype around Clawdbot. People claiming it'll make you a billion dollars, automate your business, act as your chief of staff. And yes, it's also a security nightmare.
But there's something real here. Clawdbot (now renamed Moltbot) is pointing toward a fundamentally different relationship with AI. Not a chat window you visit, but a system running on YOUR machine, 24/7, on your infrastructure, with your files. AI in a box.
These are the principles we follow at RevelAI Health. They've shaped how we build and ship. Might be useful for other early to late stage startups too.
I started with Zellij. The learning curve was low, commands were intuitive, and I adopted it quickly. Since I've abandoned IDEs for the terminal, having a solid multiplexer was essential.
The promise of autonomous AI agents is vast: give them a high-level goal, grant them access to tools, and watch them execute complex workflows. But reality often hits hard. Specifically, it hits the context window.
Models like Claude Sonnet 4.5 now offer 200K tokens (up to 1M in beta), and GPT-5.1 supports 400K tokens with native compaction that claims to work across millions of tokens. Problem solved, right?
Not quite. Bigger context windows don't solve the problem. They mask it.
User asks an AI data analysis tool: "Pull all patient communication encounters from last month."
The AI confidently writes a SQL query, hits the encounters table directly, and returns 1,247 records. AI confidently answers that. Three days later, you discover the actual number should have been 3,891, because the real path is patients → patient_queue → queue_encounters → encounters. The AI missed two-thirds of your data.
Every care team we talk to has the same complaint: patients happily text, leave voicemails, and fill out surveys, but those signals rarely make it into the plan of care.
Electronic records were never built to absorb that ambient context, and the people who could act on it are already drowning in portal messages and follow-up calls. Yet the value is obvious, timely symptom reporting keeps people out of the ED, surfaces social needs, and lets providers adjust therapy before a flare turns into a crisis.
What we need is a stack that captures self-reported data, triages it with large language models, and still gives clinicians the last word. The winning pattern blends thoughtful UX, observability, and a human-in-the-loop workflow.