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Slow, deliberate essays on data engineering, AI systems, and what it actually takes to ship software at scale. Long-form writing for thoughtful readers — no listicles, no tracking, no ads.
A long-form, primary study companion. Internals, flows, decision trees, code, and Q&A with reasoning across Spark, lakehouse, graphs, search, LLMs, RAG/agents, distributed HLD, governance, modeling, SQL, JVM, Python, K8s and CI/CD.
A comprehensive deep-dive into the data backbone powering ML, personalization, experimentation, and GenAI on modern streaming platforms
From text , generate audio files and publishing them to webapp
Deploying local python scripts and converting them as an API.
From a few initial adopters of a product, how we can target new set of users who are more likely can use the product
"Exploring the fundamentals of building an .exe file from scratch, including C++ compilation, object files, linking, DLLs, and more."
CS229 provides a broad introduction to statistical machine learning (at an intermediate / advanced level) and covers supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical ); and reinforcement learning among other topics
No schedule, no fluff. One email when a new essay is ready.