The Team of 3: Designer + PM + Engineer

while (world) { observe(); infer(); hallucinate?(); self-correct(); } while (world) { observe(); infer(); hallucinate?(); self-correct(); } while (world) { observe(); infer(); hallucinate?(); self-correct(); }

001101 010011 sigil::bagua seed::entropy trace::mechanical awe::human 001101 010011 sigil::bagua seed::entropy trace::mechanical awe::human 001101 010011 sigil::bagua seed::entropy trace::mechanical awe::human

temperature=0.92 top_p=0.95 sampler=stochastic runtime=deterministic user=astonished temperature=0.92 top_p=0.95 sampler=stochastic runtime=deterministic user=astonished temperature=0.92 top_p=0.95 sampler=stochastic runtime=deterministic user=astonished

☰☱☲☳☴☵☶☷ oracle != truth ritual == interface meaning <- interpretation ☰☱☲☳☴☵☶☷ oracle != truth ritual == interface meaning <- interpretation ☰☱☲☳☴☵☶☷ oracle != truth ritual == interface meaning <- interpretation

seed = hash(question + state); noise = sample(temperature); pattern = deterministic(seed, noise); if (human_can_track === false) mark("mystic"); return explainability_gap(pattern); seed = hash(question + state); noise = sample(temperature); pattern = deterministic(seed, noise); if (human_can_track === false) mark("mystic"); return explainability_gap(pattern);

## Not a person, still persuasive - interface implies intention - language implies confidence - user infers agency => design for interpretability ## Not a person, still persuasive - interface implies intention - language implies confidence - user infers agency => design for interpretability

cast.bagua = pickTrigrams(seed); cast.moonBlocks = deriveHexagram(seed); cast.fortuneSticks = burnModel(intensity); cast.scapula = generateCracks(seed); return readable_fiction(cast); cast.bagua = pickTrigrams(seed); cast.moonBlocks = deriveHexagram(seed); cast.fortuneSticks = burnModel(intensity); cast.scapula = generateCracks(seed); return readable_fiction(cast);

{ "observe": true, "decide": constrained, "act": reversible, "measure": behavior, "learn": weekly } { "observe": true, "decide": constrained, "act": reversible, "measure": behavior, "learn": weekly }

def perceivable_randomness(system): return complexity(system) > attention_budget if perceivable_randomness(llm): user.labels_output = "fate" def perceivable_randomness(system): return complexity(system) > attention_budget if perceivable_randomness(llm): user.labels_output = "fate"

[trace] t=02:13 system murmurs [trace] tokens fall like ash [trace] certainty simulated [trace] mechanism remains [trace] human names it chance [trace] t=02:13 system murmurs [trace] tokens fall like ash [trace] certainty simulated [trace] mechanism remains [trace] human names it chance

If I could choose one default unit for 0->1 AI product work, it’s this:

one product designer
one product manager
one engineer

Not because bigger teams are bad. Because this shape keeps decision latency low.

Why it works

A strong trio can cover:

problem framing
UX and behavior design
technical feasibility and implementation
launch instrumentation

The key is shared context, not ticket passing.

Responsibility split (practical)

PM: problem clarity, scope boundaries, tradeoffs
Designer: interaction quality, trust UX, failure states
Engineer: architecture, reliability, observability

Everyone still participates in product decisions. No “throwing work over the wall.”

Operating rituals that actually help

30-minute scope lock before each sprint
Mid-sprint quality checkpoint on real outputs
End-of-sprint learning memo (not just demo)

The memo should answer:

what we learned
what changed in our assumptions
what we’ll do next

Where trios fail

PM becomes project coordinator only
Design gets pulled in too late
Engineering is treated as execution-only

When that happens, quality drops and cycle time paradoxically gets worse.

My preferred decision principle

“Closest to the problem decides, team validates fast.”

This avoids consensus gridlock while keeping quality bar high.

Scaling beyond 3

I scale by cloning high-context trios around clear domains, then adding a small alignment layer.

I do not scale by adding layers of handoff and meeting debt.

Small teams can ship serious products when the operating model is crisp.