# hey, i'm ravi πŸ‘‹

whoami

ravi ranjan Β· bengaluru, india

i build things. lately, most of them think a little.

cat about.md

I've been shipping software for about a decade β€” 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 β€” 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 β€” no API keys, no phoning home.

ls projects/ --active

portico β€” 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 β€” so a healthy run moves at browser speed, not model speed.

org-context β€” 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 β€” a study buddy that runs entirely offline. Drop in your PDFs and notes, ask questions, get cited answers and flashcards β€” local models, local database, zero API keys.

lumen β€” a local-first desktop workbench for poking at, debugging, and operating Kubernetes clusters without living in kubectl.

patient-engagement platform private β€” 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.

ls projects/ --archived

Before any of the AI stuff, I was a mobile dev. A couple of those apps are still my most-starred repos β€” which is either humbling or motivating depending on the day.

doordish ⭐ 45 Β· smart-home-dashboard ⭐ 31 β€” Flutter, back when Flutter was new.

ask --local "anything about ravi"

Ask this portfolio runs in your browser Β· 0 API calls

Retrieval over this page plus a small language model, entirely on your device β€” nothing you type leaves your browser.

until then, answers quote the sources directly
agents welcome too: claude mcp add --transport http ravixdaisy https://ravixdaisy.com/mcp

cat beliefs.md

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.

echo $STACK

TypeScript Β· Python Β· React Β· FastAPI Β· Node Β· PostgreSQL Β· Playwright Β· LangGraph Β· Ollama & local LLMs Β· pgvector Β· Docker Β· Kubernetes β€” and whatever the problem actually needs.

ls graveyard/

cause of death: scope creep Β· app store review Β· no eval suite

./break-this-site.sh

# kills the real nginx container serving this page. docker's restart policy and health checks bring it back β€” usually in a couple of seconds. not a simulation.

● healthy

awaiting chaos β€” nobody has murdered the container today

contact --open-to "good problems"

Always up for good problems β€” AI products, dev tooling, open source, or anything worth building well.

let's talk β†’ [email protected] Β· πŸ“ bengaluru, india