Practical perspectives on artificial intelligence, agentic workflows, and AI tooling for software development. Reality checks, not hype.
Best starting post:
GitHub Copilot Agents: A Pragmatic GuideRecommended next read:
Effective Prompt Engineering
A personal reflection on how growing up in the 90s with Tamagotchis, robot dogs, Transformers, and a certain lovable robot called Johnny Five shaped my lifelong fascination with AI and robotics.
Vibe coding lowered the barrier to building. But what happens next (security, resilience, maintainability) is where engineering experience starts to matter. This is my take on the journey.
A pragmatic look at 2026's technology landscape: from agentic AI becoming mainstream to the hardware squeeze affecting gamers and hobbyists, plus what's really happening with robotics and language models.
Why Large Behaviour Models matter: a pragmatic look at what Boston Dynamics and TRI are doing with Atlas, and what it means for industrial automation and domestic robotics.
A pragmatic tour of modern AI tooling for C# developers: Semantic Kernel, OpenAI.NET clients, LangChain.NET, ONNX, TorchSharp and vector stores, with practical snippets.
