Hey, I'm Ravi 👋
I build things. Lately, most of them think a little.
Bengaluru, India
GitHub · X · Medium · systemlore · aioice
I've been shipping software for about a decade — long enough to have a graveyard of early iOS experiments, a couple of Flutter apps people actually starred, and a growing pile of AI systems I'm genuinely excited about. I'm a generalist at heart: I go wide across mobile, backend, infra, and ML, and I learn by building real things rather than reading about them.
These days I'm most drawn to AI that has to earn its keep — agents that do real work, RAG that cites its sources, tools that make other engineers faster. I spend most of my time on the unglamorous parts: Does it actually work? Can you tell why it did that? Does it run without me babysitting it? That's usually the difference between a demo and something people rely on.
I also have a soft spot for tools that run on your own machine — no API keys, no phoning home.
Retrieval over this page plus a small language model, entirely on your device — nothing you type leaves your browser.
claude mcp add --transport http ravixdaisy https://ravixdaisy.com/mcp🔨 What I'm building now
- portico — self-hostable browser automation for regulated portals (healthcare payer / EHR), where PHI can't leave your VPC. The interesting bit: an LLM authors and repairs the automation, but never runs on the hot path — so a healthy run moves at browser speed, not model speed.
- org-context — a context engine for coding agents. It ingests a team's code, PRs, tickets, and incidents and serves permission-aware, source-backed context over REST, MCP, and CLI. Basically: give your agents the same context a senior engineer has.
- study_graph — a study buddy that runs entirely offline. Drop in your PDFs and notes, ask questions, get cited answers and flashcards — local models, local database, zero API keys.
- lumen — a local-first desktop workbench for poking at,
debugging, and operating Kubernetes clusters without living in
kubectl.
There's also a patient-engagement recommendation platform I'm building privately — rules-first, ML-assisted WhatsApp outreach for a health team, where the model suggests but never overrides the safety rules, and every recommendation is fully auditable.
📱 Where I started
Before any of the AI stuff, I was a mobile dev. A couple of those apps are still my most-starred repos — which is either humbling or motivating depending on the day.
- doordish
⭐ 45· smart-home-dashboard⭐ 31— Flutter, back when Flutter was new.
🧭 A few things I've come to believe
- Ship it, then make it good. Something in production teaches you more than a perfect plan.
- If the AI can't explain itself, it doesn't get to decide. Confidence without a reason is a liability.
- Small and self-contained beats big and needy. I'd rather hand you something that runs on your laptop today.
🛠️ Usual suspects
TypeScript · Python · React · FastAPI · Node ·
PostgreSQL · Playwright · LangGraph · Ollama & local LLMs ·
pgvector · Docker · Kubernetes — and whatever the problem actually needs.
Always up for good problems — AI products, dev tooling, open source, or anything worth building well.
Let's talk. 📍 Bengaluru, India