Systems plumbing, concurrent pipelines, and secure runtime isolation for agent startups. The claim I'd rather be judged on: not that my agents are correct, but that being wrong is discoverable. Write-up: <a href="https://sriramvarun0636.github.io/" rel="nofollow">https://sriramvarun0636.github.io/
Vasool — compliance-gated payment recovery agent on Razorpay's test APIs. I pre-registered seven falsification criteria before running anything, and then lost against one: a dumb baseline that retries everything recovers 16.35pp more than mine. It also breaks policy in 1,000 of 1,000 seeded runs; mine breaks it in 0. That trade is the finding, and it's the second section of the README, not an appendix. The LLM never calls a tool — inert verdict type, no adapter to the execution plane, asserted by an import-graph test. Simulated outcomes, tagged as such in the source; every figure is a key in a committed manifest, so you can check any number without running anything. <a href="https://github.com/sriramvarun0636/Vasool" rel="nofollow">https://github.com/sriramvarun0636/Vasool — 5-min walkthrough: <a href="https://youtube.com/watch?v=B0Iov6qAaqs" rel="nofollow">https://youtube.com/watch?v=B0Iov6qAaqs
AutoPatch-AI — autonomous code remediation agent on a LangGraph state machine. Zero-trust Docker execution layer (network_disabled, 256MB RAM, 128 PID cgroup caps), real-time telemetry over thread-safe queues via SSE, AST-based parsing that cut context bloat ~80%. <a href="https://github.com/sriramvarun0636/AutoPatch-AI" rel="nofollow">https://github.com/sriramvarun0636/AutoPatch-AI — 90s sandbox demo: <a href="https://www.loom.com/share/104cf5ebcfc144a09f49c62830755408" rel="nofollow">https://www.loom.com/share/104cf5ebcfc144a09f49c62830755408
SentinelPrime — multi-threaded options system on live WebSocket data. Releases the GIL via Numba (nogil=True), actor model over daemon threads isolating DB I/O and API calls, fixed-size ring buffers to kill allocation overhead under 24/7 operation. <a href="https://github.com/sriramvarun0636/SentinelPrime" rel="nofollow">https://github.com/sriramvarun0636/SentinelPrime
Looking for backend/infra/agent-systems work at pre-seed to Series A — founding engineer or engineer #2-5. Eval and agent-safety teams too. Standard loops are fine, and I'll also take a paid 2-3 week trial sprint on a real bottleneck in your codebase.
varun636 · · focus · HN ↗
Willing to relocate: Yes
Available: Remote immediately, on-site from Dec 2026 — final-year B.Tech CS, MNNIT Allahabad
Technologies: Python, C/C++, Docker SDK (sandboxing, cgroups), LangGraph, FastAPI, WebSockets, Numba, SQLite (WAL), Linux
Resume: <a href="https://drive.google.com/file/d/1XXZ-d2hf8wEJYx0O-kX310qkeHr" rel="nofollow">https://drive.google.com/file/d/1XXZ-d2hf8wEJYx0O-kX310qkeHr...
Email: sriramvarun636@gmail.com
GitHub: <a href="https://github.com/sriramvarun0636" rel="nofollow">https://github.com/sriramvarun0636
Systems plumbing, concurrent pipelines, and secure runtime isolation for agent startups. The claim I'd rather be judged on: not that my agents are correct, but that being wrong is discoverable. Write-up: <a href="https://sriramvarun0636.github.io/" rel="nofollow">https://sriramvarun0636.github.io/
Vasool — compliance-gated payment recovery agent on Razorpay's test APIs. I pre-registered seven falsification criteria before running anything, and then lost against one: a dumb baseline that retries everything recovers 16.35pp more than mine. It also breaks policy in 1,000 of 1,000 seeded runs; mine breaks it in 0. That trade is the finding, and it's the second section of the README, not an appendix. The LLM never calls a tool — inert verdict type, no adapter to the execution plane, asserted by an import-graph test. Simulated outcomes, tagged as such in the source; every figure is a key in a committed manifest, so you can check any number without running anything. <a href="https://github.com/sriramvarun0636/Vasool" rel="nofollow">https://github.com/sriramvarun0636/Vasool — 5-min walkthrough: <a href="https://youtube.com/watch?v=B0Iov6qAaqs" rel="nofollow">https://youtube.com/watch?v=B0Iov6qAaqs
AutoPatch-AI — autonomous code remediation agent on a LangGraph state machine. Zero-trust Docker execution layer (network_disabled, 256MB RAM, 128 PID cgroup caps), real-time telemetry over thread-safe queues via SSE, AST-based parsing that cut context bloat ~80%. <a href="https://github.com/sriramvarun0636/AutoPatch-AI" rel="nofollow">https://github.com/sriramvarun0636/AutoPatch-AI — 90s sandbox demo: <a href="https://www.loom.com/share/104cf5ebcfc144a09f49c62830755408" rel="nofollow">https://www.loom.com/share/104cf5ebcfc144a09f49c62830755408
SentinelPrime — multi-threaded options system on live WebSocket data. Releases the GIL via Numba (nogil=True), actor model over daemon threads isolating DB I/O and API calls, fixed-size ring buffers to kill allocation overhead under 24/7 operation. <a href="https://github.com/sriramvarun0636/SentinelPrime" rel="nofollow">https://github.com/sriramvarun0636/SentinelPrime
Looking for backend/infra/agent-systems work at pre-seed to Series A — founding engineer or engineer #2-5. Eval and agent-safety teams too. Standard loops are fine, and I'll also take a paid 2-3 week trial sprint on a real bottleneck in your codebase.