San Francisco Bay Area
Engineer · Founder · Angel Investor
20 years building systems where failure isn't an option.
↓Introduction
I’ve spent about 20 years building systems where failure isn’t an option — from device drivers on educational tablets to AI platforms inside large financial institutions.
I was employee #2 at Kno, where we grew engineering to 80 and were acquired by Intel. At Intel I led the backend for an education platform serving 100K+ students. Founding-team engineering at Togg followed, and for the past eight years I’ve been CTO at Decision Minds, putting data and AI to work inside financial institutions while co-founding Success4 and running it to 25 customers.
Today I’m founder and CEO of Adalma AI: agentic AI for credit unions and community banks, built so the work stands up to an examiner. On the side, I angel invest in infrastructure and AI, and I build things — trading systems, agents, apps — mostly because I can’t help it.
Career
2026 — Present
Founder & CEO
2018 — Present
CTO & Client Partner
2018 — 2026
Founding CTO
2016 — 2018
Founding Team — Head of Engineering
2013 — 2016
Backend Engineering Manager & Tech Lead
2009 — 2013
Senior Software Engineer & Software Lead
2008 — 2009
DVT Engineer
Anna University Chennai, B.E. ECE → Florida International University, M.S. EE
Angel Investing
Angel investing since 2019 — infrastructure, AI, and the occasional conviction bet on a founder. Member of The Chennai Angels since 2020, bridging the US and Indian startup ecosystems.
AI compute
Direct · 2019
Data infrastructure
Direct · 2019
Analytics
Direct · 2019
Data security
Direct · 2019
RPA / automation
Direct · 2019
Fintech
Direct · 2019
Consumer / food
Direct · 2019
Media
Direct · 2020
Sports tech
Direct · 2020
Consumer / D2C
The Chennai Angels · 2021
Consumer
The Chennai Angels · 2022
Media tech
Direct · 2024
Agritech / supply chain
The Chennai Angels · 2022
Side Projects
Autonomous trading infrastructure on a Cloudflare Workers fleet: cron-driven scans, approve-to-execute order rails, morning briefs and end-of-day digests, drivable from a phone. Now runs seven books off one shared watch list and one cloud market-data feed. Every strategy is preregistered with its Sharpe deflated by trial count, every signal screen scores its own hit rate on a cron, and no broker call fires without a payload-bound, single-use approval. Documented in a public engineering dossier.
dossier.sibt.ai →Solo entry to the Alpaca x lablab.ai AI Trading Agents Hackathon, 31 August to 4 September 2026. Trading agents get judged on the orders they place; this one is built around the orders it declines. 22 named risk gates sit between every decision and every order, and every gate that fires is logged with its reason. Cloudflare Workers and Containers, REST for orders, MCP for options data, fail-closed on the market clock.
GitHub →A quarantined, mechanical auto-trading experiment: cron-driven state machine with an AI council, freshness gates, drawdown halts and a live trailing-stop tuner, every decision logged before it acts. The same worker code now runs paper books for a merchant-power thesis and an options wheel, each preregistered with a scoring date and kill gates. The live sleeve lost to SPY over its run and is being retired; the rails that caught it are the point.
Read the dossier →Answers one question before the market does, TRADE, CAUTION or NO TRADE, from a live market-quality score. The Copilot tier proposes covered calls, puts them to a five-model council vote, and places the winners through your own broker (25+ via SnapTrade) only after you approve each one, with a paper-soak gate before anything goes live. Free, Pro and Copilot tiers; 148 unit tests and 20 end-to-end specs.
sibt.ai →Claude-Code-as-trading-terminal: a packaged MCP setup with risk gates, signal ranking, fill verification and broker routing across SnapTrade and TradeStation. Version 0.5 adds an LLM trading council (three providers: independent analysis, cross-rank, chair synthesis), options-flow and GEX tools, and execution rules R0 to R8, held to a 401-test suite with an 80% coverage gate.
GitHub →An AI hedge-fund team of investor-persona agents debating every position, wired to live paper-trading rails. Runs itself daily, long-only, hedges with index puts when the committee turns bearish, halts when too many model calls fail, and commits a public trade ledger after every session. On probation: 467 committee calls scored so far at a 41.9% hit rate against a 50% bar, verdict due 2026-09-10.
GitHub →A geopolitical and macro intelligence dashboard, forked from koala73/worldmonitor and ported from Vercel to Cloudflare Workers: Durable Objects, cron triggers and seed containers, with its own auth and docs build. Its shipping-chokepoint, trade and economic signals feed the SIBT books every morning.
worldmonitor.sibt.ai →A 13-signal confluence board that hunts for speculative weekly pop candidates, packaged as a Claude Code skill. Ran as a paper sleeve through August: about a 48% win rate with losers two to three times the size of winners across three samples, so it is retired as alpha and kept as a screen.
GitHub →MCP server exposing SnapTrade write-side trading endpoints — equities and multi-leg options.
GitHub →A Chrome extension that decodes opaque travel deals: the hotel behind a Priceline Express Deal, the airline behind a round-trip mystery flight, the car behind a Pricebreaker, corroborated across Hotwire, ITA Matrix, Google Flights and Google Hotels. Fans out to nearby airports, ranks nonstop-first, and learns from what you actually book to sharpen the next guess.
Chrome Web Store →An IPTV app for the living room, built for family and shipped to real users on iOS, macOS, tvOS and Fire TV. Netflix-style library with offline downloads, picture-in-picture, subtitle search, next-episode autoplay, and resume synced over iCloud. The Fire TV build is Compose for TV on Media3.
Releases →Perspective
Every platform shift rewards the people who ship through it, not the ones who spectate. I’ve watched this movie before — mobile, cloud — but this one is different in one specific way: the marginal cost of trying an idea has collapsed to nearly zero.
Three beliefs drive how I spend my time:
Agents are the new backend. The interesting systems being built right now are not chatbots — they’re pipelines of small, verifiable, autonomous steps with gates and audit trails. That’s how I build my trading systems, and it’s how we build compliance systems at Adalma.
Regulated industries are the real frontier. Anyone can demo AI. Making it stand up to an examiner — policy-gated, logged, tamper-evident — is where the durable value is. The institutions that need AI most are the ones that can afford it least, and closing that gap is a business, not charity.
Taste survives automation. When everyone can generate everything, the scarce skill is knowing what’s worth building and when to say no. Twenty years of shipping has taught me that judgment compounds faster than code.
Three people frame how I think the industry shapes up from here. Satya Nadella has argued that the software stack collapses — SaaS thins out into agent-orchestrated workflows, and the business logic migrates to whoever runs the agents. Elon Musk keeps pulling the conversation down to physics: intelligence per watt, compute as a manufacturing problem, and humanoid robots as the largest product category ever. And Nikesh Arora asks the question every enterprise will be forced to answer — not “how do we adopt agents” but “how do we discover, govern, and stop them when we need to.” Stack those together and the next decade looks like agents doing the work, physical AI doing the labor, and governance deciding who gets to sell any of it to serious institutions.
Energy becomes the commodity of the era. Every one of those futures is gated on electrons. Data centers are already negotiating for gigawatts the way they once negotiated for bandwidth, and the winners of the next decade will be the ones who solved generation — next-generation nuclear, solar plus storage, geothermal — and the quieter constraint underneath it all: water. Cooling, agriculture, and communities compete for the same acre-feet, so water recycling and desalination stop being environmental footnotes and become infrastructure businesses. Watch where the energy and water deals happen; that’s where the compute will be.
Robots come home. The first wave of physical AI is industrial, but the one that matters socially is domestic — machines that fold the laundry, watch the stove, and above all help seniors live independently for another decade. Care is the most under-served labor market on earth, and the demographics are unforgiving. A robot that can steady a hand, fetch medication, and call for help is not science fiction anymore; it’s a product roadmap. Having spent years thinking about the people fraud targets first — seniors on fixed incomes — I think dignity-preserving eldercare is one of the most consequential things this wave will ship.
And the one I’m most excited about: agrotech. Specifically, replacing harsh chemical pesticides with UV-based pest control — autonomous robots sweeping fields at night, using calibrated UV light to kill pests and mildew with no chemical residue, no runoff, and no resistance treadmill. Companies like TRIC Robotics are already running tractor-scale autonomous UV robots on California strawberry farms, and the early results are real: chemical-free control that pests can’t develop resistance to. Pair that with precision irrigation and soil-level sensing, and you get food grown with less poison and less water at the same yield. Agriculture never gets the headlines, but it’s where AI, robotics, energy, and water all intersect — and it might be the wave’s most quietly transformative act.
Writing & Feeds
2026-08-25
The proposal on the table is to slow the frontier down. Pre-deployment review, an approval body, a queue. The instinct behind it is decent. I still think it is the wrong lever, and financial crime is where you can see why. The people I build against are not waiting for an app…
Read on LinkedIn →2026-08-24
The most useful AI policy discussion I read this month happened on X over a weekend, and at least one participant was on his second gin and tonic by the pool. Gavin Baker repeated a hard accusation about Anthropic. Sholto Douglas said it was false and explained why. Baker cam…
Read on LinkedIn →2026-08-20
One disagreement in the Baker and Douglas thread is worth pulling out on its own, because our whole business sits inside it. Sholto Douglas thinks models capable of automating most computer facing work arrive around 2028, and that people will still be doing those jobs well in…
Read on LinkedIn →2026-08-18
Sholto Douglas, on X this weekend: a given level of intelligence keeps getting about 10x cheaper every year, compounding towards intelligence too cheap to meter. Wonderful. My compliance officer's questions are not getting 10x cheaper. "Which model saw the member's SSN." "Wh…
Read on LinkedIn →2026-08-05
Nikesh Arora lists what governments should settle before they regulate AI capabilities: IP, liability, infrastructure, protecting critical infrastructure, public systems, defense use. Liability is the one where financial services is ahead, and the answer is unglamorous. The i…
Read on LinkedIn →2026-08-03
Nikesh Arora, on regulating AI: "One can't regulate for success." Agreed, and in financial services the argument is already settled in a way most of this debate misses. Banks and credit unions are not waiting for AI rules. The Bank Secrecy Act, OFAC and the model risk guidan…
Read on LinkedIn →Day 2: options sleeve made its first real trades (MU, SPCX, AMZN, AXON calls), a bug blocking every order since day one is fixed. AXON hit its stop loss and closed for a loss instead of riding it. AI council rejected a SNDK trade over a data integrity issue. @AlpacaHQ @lablabai
2026-09-01 on X →
Day 1 of my Alpaca hackathon agent trading autonomously in the cloud. It placed zero option orders. Four gates blocked it, each only visible after fixing the one before: 1. sleeve off via an inherited flag 2. no tick inside its entry window 3. greeks 400d, 100 symbol cap 4. limit price needed 2 decimals Every run logged healthy. @AlpacaHQ @lablabai
2026-08-31 on X →
Day 1 of the @AlpacaHQ x @lablabai AI Trading Agents Hackathon. Team: Refusal Rails. Solo entry. The pitch: trading agents get judged on the orders they place. Mine is built around the orders it declines. 22 named risk gates sit between every decision and every order, and every gate that fires is logged with the reason. Fresh paper account. Zero trades so far, on purpose: the market closes in half an hour and my agent refuses positions it can't monitor into a weekend. It starts trading Monday, when it can pass its own gates. #buildinpublic
2026-08-28 on X →
Day-to-day: @nkrvivek on X
Beyond Work