Short on fluff. Heavy on real systems.
A practical guide to AI-assisted Selenium automation, covering where AI speeds test design, locator repair, failure triage, coverage planning, and how to build reliable web UI tests with code and a beginner hands-on task.
A practical guide to AI data leakage prevention for enterprise systems, covering prompt injection, RAG boundaries, memory hygiene, tool permissions, output filtering, and deployable controls that reduce the risk of oversharing sensitive data.
A practical look at why C++ remains central to HFT: market-data pipelines, binary protocol parsing, order-book maintenance, pinned cores, NUMA awareness, queue discipline, timestamping, replay, profiling, and the engineering habits required for deterministic low-latency trading systems.
A practical deep dive into the open-source C++ ecosystem for neural networks: ONNX Runtime, LibTorch, oneDNN, OpenVINO, TensorFlow Lite, and llama.cpp, with guidance on when each library fits production deployment, inference, edge AI, and native systems integration.
An opinionated but evidence-based argument for why C++ remains the stronger default language for AI-assisted systems engineering: larger public code corpora, deeper vendor support, richer profiling and optimization feedback loops, and easier integration with existing native AI infrastructure.
A detailed guide to profiling C++ systems the right way: representative workloads, release builds with symbols, sampling versus tracing, flame graphs, heap behavior, off-CPU time, hardware counters, and a disciplined workflow for turning measurements into reliable performance wins.
