About us
Carrying engineering excellence forward into the AI era.
Look around and everyone's chasing the same things: better models, more skills, sharper rules, LLM-as-a-judge. Almost overnight the industry forgot about plain solid software fundamentals — maybe because it's easier to hope the models will handle the hard parts for us than to put in the work of actually learning them. AI writes most of the code now, and the things that made software durable are quietly eroding: deliberate architecture, real tests, delivery discipline, and the judgment that tells good from merely plausible.
This is the place that goes the other way. We don't take shortcuts — we study software engineering hard and work out how it actually applies in the AI era. That makes this both an academy, where those disciplines get taught, and a lab, where we experiment and find out what holds and what doesn't.
This isn't theory for me. I work on exactly this every day in my professional practice, and these sessions are where I bring that work into the open and teach what I'm learning as I go.
For engineers, tech leads, and architects who intend to still recognize their craft on the other side of this shift.
Upcoming events
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How Do You Measure the ROI of AI in Your SDLC?
·OnlineOnline⚠️ IMPORTANT — this is a remote talk on Google Meet. To get the join link you must register on Luma here 👉 https://luma.com/z47autlj (an RSVP here alone won't get you in.)
We're all wondering what the actual return on all these AI tools is. A lot has gone in already — licences, tooling, a good deal of your teams' time.
There's no clean formula for it yet, but the question isn't a new one for me.
I've been studying delivery metrics since well before any of this. As Head of Engineering at Bridge I went deep into DORA and DX, working out what genuinely tells you whether an engineering organisation is healthy, and that became the baseline I brought to leading AI adoption into the SDLC at an American insurtech.
My thesis then and now: AI in the SDLC is one brick, not a new building. What you're after is still engineering performance, so the metric set stays broadly the one we already had, with some tactical extensions for what's genuinely specific to agents. Since then I've worked through much of what the people behind DORA and DX say about this themselves, and from their thinking alongside my own experience I settled on a set of metrics I now use to track adoption. I want to share all of it.
What we'll look into:
- DORA, and what its authors say about measuring AI-assisted delivery
- DX Core 4 and its tension metrics — four pillars read against each other, so no single number can be gamed on its own
- The Lean Startup learning loop, for connecting engineering work to business outcomes
- The data your AI vendor is already collecting on your behalf
- The extensions that are genuinely AI-specific, and how few of them you need
- The metrics I landed on myself, and what each one can and can't tell me
We'll finish on the most promising direction I know of for closing the remaining gap — and I'd like the room's help thinking it through.
Who it's for: directors, VPs and heads of engineering who own an AI rollout and the decision about whether to keep investing in it — and the platform or developer-productivity leads who'd be the ones actually instrumenting it. No existing measurement practice assumed. If you're writing features rather than measuring how they get delivered, the hands-on episodes in this series will suit you better.
Format: 60 min talk + 30 min open discussion. Remote, recorded.
Wed Oct 7, 2026 · 18:00 CEST (12:00 ET / 09:00 PT) — join link sent when you register.Save your spot → registration is on Luma, not just Meetup RSVP — that's how you get the join link, the recording and the notes afterward: https://luma.com/z47autlj
Part of Software Engineering for the AI Era — a recurring workshop series on agentic coding and AI-native delivery.
18 attendees
Past events
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