Software developer — I build automation that ships real media, backends that survive load tests, and GPU code that stays fast where it matters.
My pinned work spans AI video orchestration (Sheets → LLM → TTS, images, motion, final MP4), TypeScript product stacks (short-form video with Mux), Go systems (ledger APIs, NATS telemetry to TimescaleDB), and C++/CUDA (stereo depth, image filters with FastAPI + a web UI). Observability is a habit: Prometheus, Grafana, k6, and documented benchmarks show up across repos.
| AI Video Automation Pipeline | Google Sheets queue → n8n → OpenAI (structured script/scenes) → TTS, image gen, I2V, music, Creatomate — row in, MP4 out. |
| VibeFlow | Short-form feed: React + Vite, Express + Prisma, PostgreSQL, Redis, Mux uploads & playback, JWT auth, Latest/Trending. |
| velocis | High-throughput financial ledger in Go: POST /transfer, optimistic locking, Redis balance cache, PgBouncer, Prometheus/Grafana, k6 suites including cross-shard sagas. |
| LumenStereo | Real-time GPU stereo (CUDA SGBM), calibration, ImGui viewer — tuned for VRAM and throughput vs OpenCV on Middlebury-class data. |
| ApexStream | High-frequency F1-style telemetry: NATS JetStream → Go ingest/processor → TimescaleDB + Redis, full observability stack. |
| gpu_image_processing | CUDA Gaussian/box/Sobel pipelines, naive vs optimized kernels, FastAPI + browser UI, pybind11, Nsight-friendly workflow. |
- Automation & production video — spreadsheet-driven jobs, vendor APIs, reproducible n8n workflows
- Backend performance — ledgers, sharding, streaming ingestion, rate limits, hypertables
- GPU & vision — stereo matching, image processing, memory hierarchy and profiling
- X (Twitter): @P_factorial01



