Rust Systems Programming: From Zero to Production
Master Rust for systems programming — ownership, borrowing, lifetimes, zero-cost abstractions, async/await, FFI, and building high-performance networked services.
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What I'm working on, thinking about, or trying to understand better — written like a real person, not a content calendar
Master Rust for systems programming — ownership, borrowing, lifetimes, zero-cost abstractions, async/await, FFI, and building high-performance networked services.
A comprehensive guide to leveraging TypeScript in Next.js — from strict configuration and type-safe routing to server actions, API routes, and advanced patterns for production applications.
Master React Hooks from fundamentals to advanced patterns — useState, useEffect, useReducer, useMemo, useCallback, custom hooks, and real-world performance optimization strategies.
Architect robust, versioned, and performant REST APIs — resource modeling, pagination, error handling, rate limiting, caching, HATEOAS, and OpenAPI documentation patterns.
From Docker Compose to production Kubernetes — containerization fundamentals, multi-stage builds, Kubernetes architecture, deployments, services, ingress, and GitOps with ArgoCD.
Deep dive into PostgreSQL performance — query planning, indexing strategies, EXPLAIN ANALYZE, partitioning, connection pooling, and real-world optimization patterns for high-traffic databases.
Master Nix for deterministic builds, reproducible dev environments, and declarative system configuration. From nix-shell to home-manager and NixOS production deployments.
Integrate security into your CI/CD pipeline — SAST, DAST, dependency scanning, secret management, container hardening, and automated compliance checks for modern deployment workflows.
Practical guide to integrating LLMs into real applications — prompt engineering, RAG, function calling, embeddings, vector databases, and production deployment patterns for AI-first systems.
A complete walkthrough of the Python data science ecosystem — from data manipulation with Pandas and visualization with Matplotlib to building production-ready machine learning pipelines with Scikit-learn.