A practical engineering guide to machine learning algorithm types—supervised, unsupervised, and reinforcement learning—and how to avoid overfitting vs underfitting in real systems.
Learn how a data readiness report framework and robust data governance turn messy enterprise data into AI-ready assets. Practical guide for engineering in AI.
Discover how to design AI products users trust — from the first chatbot ELIZA to modern systems like ChatGPT, learn 7 key principles for trustworthy AI product design.
Learn how to engineer AI product discoverability via Model Context Protocols (MCPs) in the age of agentic shopping — a practical guide for brands.
A comprehensive review of Lovable.dev—an AI-powered no-code app builder—examining its ease of use, MVP-ready workflow, pricing tiers, pros & cons, and ideal use cases for both developers and non-technical creators.
LLMs are more than just chatbots — they’re a new runtime layer for software. This deep dive into the LLM application stack reveals what every engineer should know to ship real-world, AI-powered features with GPT-4, LangChain, and vector databases.
Large Language Models (LLMs) like GPT-4 are transforming how users interact with digital products — and frontend engineers are at the heart of this shift. It’s time to think beyond chatbots and embrace the future of intelligent interfaces.
Ocasionally, I’ll share practical frameworks and tools you can apply right away.
