No system is secure.

Reverse engineered game memory layout via signature scanning to extract coordinate hashes and state bytes; bypassed anti-tamper protections to enable real-time RAM reads through a custom DMA python module using pymem.

Trained a DNN + decision-tree hybrid (minimax-style lookahead) via multi-agent PPO self-play, achieving superhuman play. Eliminated SubprocVecEnv IPC overhead with a vectorized MK11VecEnv for ~7.5x training throughput.

Trained an 8-head Transformer from scratch on a single RTX 5070 over 3 weeks. Achieved multi-sentence coherent dialogue via mixed-precision training and gradient checkpointing with no pretrained weights.

Optimized custom tokenization, batching, and data pipelines to improve inference quality and training throughput.

System runs on an NVIDIA Jetson Nano, combining a 5-million-parameter PyTorch conversational model with Picovoice’s Porcupine for keyword spotting and Rhino for intent parsing.

Executes natural language understanding fully on-device, mapping recognized intents to GPIO or MQTT-based appliance controls with zero cloud dependency.

Achieved ±0.05°C stability on an ESP32 using a dual-mode control system (lag-aware PID + constant-power model) with predictive braking and sensor-lag compensation.

Enabled rapid convergence and runtime tuning through a serial control interface with CSV and multi-sensor capabilities.

Embedded DSCNN based ML system for emergency siren detection: distilled a 4M-parameter TensorFlow teacher into a 38k-parameter DSCNN on ESP32.

Achieved 99.97% test accuracy, 92% real-world accuracy, 18 ms latency, and 3+ days per 1% battery using a quantized, low-power inference pipeline with optimized DSP preprocessing.

Built a 30k-line, 165-commit encrypted platform featuring DHKE chat, project posts, auto-groups, tasks, and threads.

Eliminated plaintext exposure via salted SHA-256 auth, per-session DHKE keys, and optimized WebSocket messaging.