A practical collection of writing on AI Engineering, platform delivery, and hands-on experiments.
These are the three threads running through my work and writing.
AI Engineering
How teams turn AI-assisted development into reliable, reviewable delivery.
Platform & Delivery
The platforms, DevOps practices, and delivery systems that help teams ship with confidence.
Experiments & Learning
Hands-on projects and experiments that make the underlying tools and trade-offs concrete.
AI Engineering is not just prompting or model selection. It is the design of workflows, platforms, quality systems, evaluations, governance, and delivery practices that make AI-assisted development useful in real organizations.
My angle is practical: AI makes coding faster, but organizations still need better specs, platforms, review systems, automation, governance, and delivery ownership to actually ship faster.
For the past few months I’ve been hacking on Jarvis in the evenings: a small Go agent that gives an LLM a handful of local tools (read files, run shell, spawn sub-agents, that kind of thing). I’m not trying to ship the next OpenClaw clone. I just wanted to see how the pieces fit together when you build them yourself.
Please Stop Force-Feeding AI FOMO to Others!
Not too long ago, I wrote a post about the AI Hype Burnout and how it was harmful to the whole industry.
I thought that was the end of it, but it seems like the FOMO is starting to kick in. Many people are seeing that AI is finally delivering real value and are “inviting” everyone to quickly jump on the bandwagon.
Continuing my series of Friday night experiments, this Friday I was on a train from Milan, returning from the First Italian Forum on Artificial Intelligence for Industry.
Since the models weren’t working well on the train Wi-Fi, I decided to exercise my coding muscles instead.
As 2025 comes to a close, it’s time to reflect on the year that was. From professional milestones to personal growth, this year has been packed with experiences. This is my personal diary to help me remember them in years to come.
“I was highly skeptical, but now I’m a believer.”
That’s a phrase I find myself constantly repeating in recent conversations about LLMs and AI and it turns out, I’m far from alone! I’ve heard/read variations of this statement multiple times in the past few weeks.