Ship 3x faster without hiring.
Proprietary AI delivery systems + embedded engineers.
Three CTOs brought us to their next job.
200+ engineers. 4.9/5 on Clutch.
Our rejection email went viral on Reddit yesterday.
People are shocked a company would tell a candidate exactly why they got rejected.
We're shocked that's shocking.
We asked for 3 sentences about a hard bug.
We got four paragraphs about "holistic approaches to software craftsmanship."
The take-home used temp1, temp2, temp3 as variable names.
Our company name was misspelled twice in the paragraph about attention to detail.
We told them all of that. Directly.
And we told them the door is open if they come back with work that shows they wrote it and read it before sending.
We review code the same way.
Direct. Specific. No hand-waving.
That's just how we build.
Our rejection email went viral on Reddit yesterday.
People are shocked a company would tell a candidate exactly why they got rejected.
We're shocked that's shocking.
We asked for 3 sentences about a hard bug.
We got four paragraphs about "holistic approaches to software craftsmanship."
The take-home used temp1, temp2, temp3 as variable names.
Our company name was misspelled twice in the paragraph about attention to detail.
We told them all of that. Directly.
And we told them the door is open if they come back with work that shows they wrote it and read it before sending.
We review code the same way.
Direct. Specific. No hand-waving.
That's just how we build.
90% of our code is AI-generated. Zero quality regression.
Real numbers from a 2-engineer pod:
→ 122 PRs merged in 3 months
→ 330+ PRs in 6 months
→ First code ships Week 1, full velocity by Week 4
→ AI compute cost: ~$200/dev/month
→ Every line reviewed by a senior engineer before merge
Get in touch and we will show you how we do it.
Most AI advice comes from people watching. This newsletter comes from people building.
A weekly briefing from inside 100+ engineering teams shipping AI into production.
Subscribe for free👇
https://t.co/h46msl3xMD
90% of our code is AI-generated. Zero quality regression.
Real numbers from a 2-engineer pod:
→ 122 PRs merged in 3 months
→ 330+ PRs in 6 months
→ First code ships Week 1, full velocity by Week 4
→ AI compute cost: ~$200/dev/month
→ Every line reviewed by a senior engineer before merge
Get in touch and we will show you how we do it.
Most AI advice comes from people watching. This newsletter comes from people building.
A weekly briefing from inside 100+ engineering teams shipping AI into production.
Subscribe for free👇
https://t.co/h46msl3xMD
Most AI advice comes from people watching. This newsletter comes from people building.
A weekly briefing from inside 100+ engineering teams shipping AI into production.
Subscribe for free👇
https://t.co/h46msl3xMD
McKinsey says the agentic SDLC delivers 3-5x productivity with 60% smaller teams.
Our data from 14 engagements:
73% of production AI work is infrastructure, not model work.
Projects that ship: 30/70 model-to-infra split.
Projects that stall: 70/30.
The ratio inverts. That's the part nobody budgets for.
McKinsey just described a 24-hour software delivery model. AI agents execute overnight. Humans review by morning.
Daily sprints replace two-week cycles. 3-5x productivity gains. 60% smaller teams.
We've been operating a version of this across our client engagements. The direction is correct. What's missing is what turns a delivery framework into a production system.
McKinsey lists four foundations: clear product vision, standardized architecture, structured agent inputs, consistent stakeholder engagement. Those are necessary. They're also about 30% of the actual infrastructure required.
The rest is production engineering. Evals. Guardrails. Observability. Human-in-the-loop exception handling. Cost and quota controls. Prompt version management.
We track 13 layers in our production agent stack. Most companies implementing "agentic delivery" have built 2 or 3 of them.
The article calls "knowledge graphs" the critical unlock for agent autonomy. We'd frame it differently.
What you actually need is a measurement layer: cost-per-commit, AI-generated code that reaches production versus code that gets thrown away, model usage patterns across teams. Without that, 3-5x is an aspiration. Not a metric.
One thing the article gets exactly right: the remaining team needs to be significantly more senior. Smaller pods, higher skill density, architecture judgment over ticket throughput.
That's been our operating model from the beginning. Embedded senior engineers. AI-augmented delivery. Everything measured before and after.
The agentic era rewards teams that build the infrastructure around the model. We know because we measure what happens when they don't.