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Following are my personal thoughts on tech, AI, startups and adoption of AI in Health-Care. You could read more about me here

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Blog / Thoughts

  • 7 min read

Escaping Context Amnesia: Practical Strategies for Long-Running AI Agents

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.

  • 5 min read

AI Sucks at Analyzing Data

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.

  • 6 min read

Designing a Patient Self-Reporting Stack Around Humans and AI

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.

  • 4 min read

Recursive Summarization Unlocks Effective LLM Integration in Healthcare

Your patient has 247 pages of medical records spanning 8 years. Two ER visits, three specialists, ongoing knee osteoarthritis, recent ACL reconstruction. How do you create a coherent summary that preserves critical information while making it digestible for both clinicians and AI systems?

The answer isn't just summarization, it's recursive summarization. And the secret isn't just what you summarize, but what you choose to preserve at each level of abstraction.

  • 5 min read

How Claude Code Made Me Fall in Love with the Terminal

Like many of you, I recently made the full switch from Cursor to Claude Code. This transition marked more than just a tool change – it fundamentally transformed how I think about development environments.

For years, I lived in VSCode (recently Cursor), relying heavily on mouse navigation and minimal keyboard shortcuts. I resisted the pull of Neovim and keyboard-centric workflows. But after embracing Claude Code, I discovered something profound: the terminal is the new IDE. You can run it everywhere with a consistent workflow – be it a Linux box, your Mac, or a VPS. That's all you need.

  • 5 min read

Cognitive Architecture Patterns in Health Care for LLMs

We're inspired by the ideas from this Cognitive Architecture paper and an insightful Langchain Blog by Harrison Chase.

At RevelAI Health, we're exploring how to create closed-loop, safe agents in healthcare — systems that can reason and execute on patient needs in a secure and reliable way. The key is understanding how these agentic systems should think, the flow of execution in response to patient intent, and ensuring safety through structured, observable loops.