Research library
Software engineering
Practical architecture and development decisions for software that people can use, maintain, and adapt.
58 articles · Page 5 of 5
Taxonomy of AI: From ML to World Models
A map of AI systems — machine learning, deep learning, LLMs, multimodal models, and world models — with clear definitions and comparisons.
Technical Debt: The Product Velocity Killer
Technical debt compounds silently until it dominates your roadmap. Learn to measure, communicate, and systematically reduce it.
Prompt Engineering Patterns for Production Systems
Learn 7 battle-tested prompt engineering patterns that reduce failures and improve reliability in production AI systems. Includes code examples.
Understanding Tokens and LLM Inference
Discover how LLMs process text through tokenization and inference. Essential knowledge for optimizing AI costs and prompt performance.
Designing RAG Pipelines for Production
Architecture patterns and implementation considerations for building retrieval-augmented generation systems that work reliably at scale.
Developer Experience Is a Business Metric
Slow builds, flaky tests, and painful deploys are measurable drags on revenue. Learn how to quantify and improve developer experience.
Right-Sizing Your Architecture
Monolith vs. microservices is a false binary. Match your architecture to your team size, product maturity, and actual complexity.
API Design Principles That Stand the Test of Time
APIs outlive the code that calls them. A practical guide to designing HTTP APIs that stay stable, intuitive, and maintainable as your product scales.
The Case for Boring Technology
Proven tools beat shiny frameworks. How boring technology choices compound into faster delivery, fewer outages, and real competitive advantage.
AI-Assisted Development: Beyond the Hype
An honest look at AI coding assistants like GitHub Copilot and Claude. Learn where they excel, where they fail, and how to use them effectively.