OUR THOUGHTS
Notes from real delivery work and lessons teams can apply directly
AI & Engineering
Successful AI transformation starts by redesigning work one function at a time — defining where AI can operate independently, where human judgment remains essential, and where investment in new capabilities creates measurable value.
Tyler Cohoon
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AI is increasing engineering output faster than most delivery systems can absorb. Organizations seeing sustained productivity gains are redesigning how work moves through architecture, review, and approval.
Matt Pointer
Organizations should selectively own AI capabilities where they create competitive advantage and use commercial platforms where they provide the greatest value.
Kiana Micari
Product
Engineering captured AI's first wave of delivery leverage; the next opportunity sits earlier in the lifecycle — improving requirements, resolving ambiguity, and reducing rework before development begins.
Mid-2026 data shows that AI adoption is no longer the differentiator it was a year ago. The organizations pulling ahead are the ones that standardized how AI gets used.
Teaching while delivering builds AI capability faster because engineers learn the method inside production work while the roadmap continues to move.
James Barcellano
How one V.Two engineer built and runs team-scale automation infrastructure solo using AI-native development.
Tomas Cormons
Learning by doing changes engineering behavior more reliably than standalone AI training because engineers apply new practices to real code, real constraints, and real quality gates.
Key insights and best practices for designing microservice architectures that scale from startup to enterprise.
Alberto Pallares
Guidelines for creating robust, scalable APIs that support complex enterprise integration requirements.
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