Hello, I'm Sahil Menon

Full-stack maze platform with a C++20 generator hitting ~38 Mcells/s single-threaded (≈2× a published C# baseline) and ~92 Mcells/s on 8 cores, streaming 10-billion-cell mazes in O(width) memory, with real-time multiplayer and a WebGL2 renderer.

Generating a 10-billion-cell maze at full resolution should take terabytes of memory. Getting it under tens of MB while supporting thousands of real-time players is the actual constraint.

Eller’s algorithm with within-row strip parallelism and AVX2/BMI2 SIMD packing scales to ~92 Mcells/s on 8 cores in O(width) memory. FastAPI + Redis pub/sub fan-out across K8s pods (p99 3.3 ms at 100 concurrent bots); WebGL2 renderer batches an entire 64×64 chunk into one GPU draw call at 60 fps. One-command AWS deploy via Terraform + EKS.

Python framework (published on PyPI) that compiles arcade game logic into fully playable Google Slides via BFS state enumeration: one slide per reachable state, hyperlink-navigated.

No runtime, no JavaScript, no server. Just a shareable URL that plays a full arcade game.

1,000-state ceiling bounds exponential growth (Pac-Man scales as positions × 2ⁿ with n pellets). Token-bucket rate limiter at ≤50 API writes/min with 5 concurrent batch uploads generates ~500-state presentations in 1–3 min. Pygame-inspired 1920×1080 rendering API with 40+ colours and 3 themes; campaign system across 4 bundled games (491–600 states each).

Solo competitor in IMC Prosperity 4, finishing top 10% worldwide and top 200 in Australia out of 22,000+ global teams across 5 rounds of algorithmic and manual trading.

The challenge: keeping a market-making engine profitable across changing volatility regimes without overfitting to any single one.

Built a three-tier market-making engine (take/clear/make) using Welford online mean, online AR(1) on price deviations, z-score tiered sizing, and asymmetric bid/ask anchoring. Built a separate trend-following MM with hardcoded-slope discovery, online OLS blending (70/30), and full-book order imbalance microprice adjustment.

Python file-to-video codec that encodes any file into MP4 for lossless storage on YouTube, recovering the original file exactly despite H.264/VP9 re-encoding.

YouTube lossy-compresses every uploaded video. Storing arbitrary binary data there without any corruption is the problem.

2×2 uniform pixel blocks survive ±127 DCT luma ringing in H.264/VP9. Three-tier Reed-Solomon ECC over GF(2&sup8;) (vectorised → Berlekamp-Massey → parallel) with byte interleaving for burst-error recovery. AES-256-GCM + PBKDF2-SHA256 encryption, zlib compression, and 38× faster encoding at 0.82–2.88 MB/s via NVENC/AMF/QSV hardware acceleration with YouTube OAuth2 upload.