Artificial intelligence, machine learning, LLM integration, and AI-native application development.
AI is moving faster than most teams can absorb. These articles cut through the hype to cover what actually works in production: LLM integration, agent architectures, prompt engineering, AI-powered dev tools, and the ethical considerations that responsible builders can't ignore.
24 articles
Agentic AI isn't just another developer tool — it's a shift in how software gets built. Here's what agentic AI development actually means, how I use it in production, and what it means for businesses that want software built faster without sacrificing quality.
An honest comparison of the major AI agent frameworks in 2026 — LangChain, LlamaIndex, and Anthropic's native tool use — with clear guidance on when to use each.
A working developer's honest assessment of AI code generation tools in 2026 — what I use daily, how I integrate them into my workflow, and where they still fall short.
How small and mid-size businesses can use AI to get genuine insight from their data — practical approaches that don't require a data science team or enterprise BI budget.
An honest assessment of AI documentation generation — where it adds real value, where it falls short, and how to build a documentation workflow that uses AI effectively without sacrificing accuracy.
A working software architect's perspective on responsible AI in enterprise software — not abstract ethics philosophy, but the concrete practices that reduce harm, build trust, and keep businesses out of trouble.
How AI is reshaping DevOps practice in 2026 — from intelligent deployment pipelines to AI-assisted incident response — and how to integrate these capabilities without adding fragility.
How AI tools are changing legacy system modernization — from automated code analysis to incremental migration strategies — and what that means for businesses with aging software.
A practical, no-hype guide to AI adoption for small businesses — identifying the applications that deliver real ROI, avoiding the common traps, and making smart decisions about where to start.
An honest breakdown of AI code review tools in 2026 — what they catch reliably, where they miss, and how to integrate them without creating review fatigue or false confidence.
A working software architect's take on the AI development trends that actually matter in 2026 — not hype, but patterns reshaping how software gets built.
How AI tools are changing automated testing — from test generation to intelligent coverage analysis — and how to integrate them into a testing strategy that actually improves software quality.
Proven architecture patterns for building AI-native applications — from data layer design to evaluation pipelines — based on real production experience, not theory.
What it actually takes to build business chatbots that work in production — from intent design to escalation workflows, with lessons from real deployments and real failures.
A practical developer's guide to building with the Anthropic Claude API — authentication, model selection, tool use, streaming, prompt caching, and production deployment patterns.
A practicing architect's perspective on where software development is actually headed — what AI changes structurally about how software gets built, and what that means for businesses and developers.
A practical guide to integrating large language models into enterprise applications — covering architecture patterns, common failure modes, and hard-won lessons from production deployments.
Cut through the ML hype with a practitioner's breakdown of where machine learning genuinely improves enterprise software outcomes versus where traditional approaches still win.
A practical guide to natural language SQL systems — how they work, how to build them reliably, and how to give non-technical users genuine data access without the risks of uncontrolled query generation.
A developer's honest comparison of OpenAI and Anthropic for enterprise AI applications — evaluating capabilities, reliability, safety, pricing, and which use cases favor each provider.
Practical prompt engineering techniques for developers building with LLMs — from system prompt design to chain-of-thought patterns, with real examples from production systems.
A developer's practical guide to Retrieval-Augmented Generation — how RAG works, when to use it, how to design it well, and the common mistakes that kill RAG quality.
A transparent account of how AI tools have changed the way I build software — what's faster, what's different, what hasn't changed, and what that means for businesses that hire me.
A clear, practical explanation of vector databases for developers and decision-makers — what they do, when they're the right tool, and when a simpler approach works better.