CTO @DiaIectica · Built @OrfiumMusic from 0→700 people across 7 countries · PhD, 25 publications in APIs & Semantic Web · Writing on Agentic Web & 20x teams
Greece
Joined December 2009
- Tweets17.7K
- Following2.4K
- Followers3.9K
- Likes2.7K
Pinned Tweet
In 1890, Poincaré proved that three bodies under a single force have no closed-form solution. The orbits are chaotic: nudge a starting velocity and the system ends up somewhere else.
Gravity is the polite case. One force, always attractive, symmetric, one parameter per body.
A software team is not under one force. Ego, ambition, incentives, fear, trust, and the educational distance between a PhD and a bootcamp graduate. These forces are asymmetric, they change sign, they are not conservative, and they interact with each other.
Then Conway's law closes the loop: the system you ship copies the communication structure that built it. The chaotic trajectory of the human n-body system is not the backdrop to your architecture. It is the compiler.
The counting, for a team of 12: 66 pairwise channels (Brooks), 396 if each pair carries six forces, and 4,083 possible coalitions. The coalitions decide what ships, and nobody has them on a chart.
Physics never solved the n-body problem either. It learned to shape it: hierarchical configurations (tight inner pair, far third body), fewer bodies, fewer forces crossing a boundary, and interfaces that carry the coordination so the far body never needs the meeting. That last one is where API design and org design turn out to be the same discipline.
Full argument, with the graphs:
apilama.com/2026/10/26/conwa…
I liked that.
At the age of AI, everyone should take more risk.
In larger companies, when bringing in newer companies and helping them evolve, they should share the risk.
"If we don't deliver, you don't pay."
Every week another "AI transformation partner" turns up with "agentic" in their tagline and case studies from clients nobody can name.
A CTO at a financial software company told our team about the ones who'd already pitched them. They'd sorted them into four groups before we asked.
First, the "AI-powered" outsourcers. In the CTO's words, they "talk a big game" and then it's "offshore people clicking AI buttons."
Then the "AI experts," who "come in and can talk but don't seem to do anything at all."
Then the "Applied AI" startups with their own platform, who wanted a regulated company to move its whole stack onto something that isn't SOC 2 certified yet.
And the "AI engineer" placement firms, whose plan was seven hires at $300,000 a year each.
What the CTO actually cared about came out a few minutes later: "If you guys don't produce, I just blew the rest of my year."
None of those pitches had an answer for that.
We've been an engineering firm for 10 years, since long before anyone called it "Applied AI."
I replied: "If we don't deliver, you don't pay."
A reorg redraws the boxes. Conway's law doesn't copy the boxes.
It copies who actually talks to whom, who defers to whom, and which two leads stopped talking in March.
What an engineering leader can do about it:
5/ Set the initial conditions before the architecture exists.
The Inverse Conway Maneuver: design the org for the system you want. In matched product pairs (MacCormack et al., 2012), loosely coupled orgs shipped more modular code, with coupling differing by up to 6x.
6/ This week: list your five most expensive boundaries and name one owner on each side.
No owner, or a meeting needed to cross? That's where the chaos gets compiled.
Write-up: apilama.com/2026/10/26/conwa…
Which boundary in your system exists because of people, not design?
If agents take the tasks we used to give juniors, where do seniors come from in five years?
I don't think the answer is to stop hiring juniors. It's to change their first year: reviewing agent output, owning a small system end to end, debugging real production issues.
Would you hire a junior engineer in 2027?
Yes / no?
Three bodies, one force: chaotic (Poincaré, 1890).
A team of 12: 66 pairs × 6 forces, 4,083 possible coalitions.
Conway's law compiles that graph into your architecture.
You don't solve n-body.
You shape the initial conditions
In 1890, Poincaré proved that three bodies under a single force have no closed-form solution. The orbits are chaotic: nudge a starting velocity and the system ends up somewhere else.
Gravity is the polite case. One force, always attractive, symmetric, one parameter per body.
A software team is not under one force. Ego, ambition, incentives, fear, trust, and the educational distance between a PhD and a bootcamp graduate. These forces are asymmetric, they change sign, they are not conservative, and they interact with each other.
Then Conway's law closes the loop: the system you ship copies the communication structure that built it. The chaotic trajectory of the human n-body system is not the backdrop to your architecture. It is the compiler.
The counting, for a team of 12: 66 pairwise channels (Brooks), 396 if each pair carries six forces, and 4,083 possible coalitions. The coalitions decide what ships, and nobody has them on a chart.
Physics never solved the n-body problem either. It learned to shape it: hierarchical configurations (tight inner pair, far third body), fewer bodies, fewer forces crossing a boundary, and interfaces that carry the coordination so the far body never needs the meeting. That last one is where API design and org design turn out to be the same discipline.
Full argument, with the graphs:
apilama.com/2026/10/26/conwa…
Teams don't slow down because people get slower. They slow down because work starts waiting: for a review, a decision, an owner.
AI makes the doing faster. The waiting stays exactly where it was.
Most AI agent demos break at the same point: the second hour of production traffic.
Not the model. The retries, timeouts and state nobody designed for.
The job most at risk from AI isn't the junior engineer.
It's the manager whose main output is status updates.
Agree or disagree?
On Friday I asked why Greek engineers scale everywhere, but Greek tech companies rarely do.
Few of you picked capital. Most said distribution or ambition. Others added things I hadn't listed: culture, regulation, a small home market, and "why leave a good role at a growing company?"
The last one gets talked about the least.
Which would you fix first?
A) distribution
B) ambition
C) the safe-job pull
This week's network failures in one Google Cloud region in London took a bank's app and cards offline across Europe for hours.
Regions fail. That part isn't news. Going hours without a real update is the part users remember.
An API key used to mean "this service may call that endpoint."
Hand it to an agent and it means "may do anything that endpoint allows, at machine speed, with nobody watching."
Same string, very different blast radius.
How do you scope agent credentials?
A) one key per agent
B) short-lived tokens
C) human approval for writes
Looking to talk with engineers who've actually trained models with RL: Prime Intellect environments, verifiers, GRPO/PPO runs, reward design.
Not a job post (yet). I want to hear what broke in practice.
Reply or DM me. What was the hardest part: rewards, infra, or evals?
I found new ways to use @bot, and I love it, but now, in a day, I spend my weekly balance, which, up until now, I was heavily using, was never even close.
20 years ago I was at FOSDEM watching Linux vs Microsoft like it was a holy war.
Then I started the API meetup. Then the cloud computing meetup (now the AWS community group in Athens).
Now I'm launching two new meetups.
I had forgotten how much work this is.
I had also forgotten how much I love it.
Selective memory: enabled 🙃