Long-form essays from the engineers shipping AI inside payers, hospitals, energy operators and proptech platforms. Written for technology leaders who care more about what runs in production than what trended last week.
How to use AI to migrate legacy systems without changing your business logic. Capture behavior, migrate incrementally, and prove equivalence.
AI does not just speed up coding. It reweights the whole software lifecycle. Here is what changes in plan, spec, generate, verify, ship, and operate.
What senior engineers actually delegate to an AI pair and what they never do. A practical division of labor that keeps quality and sharpens judgment.
When AI writes half the diff, human review breaks. Here is how to layer automation, AI triage, and focused human gates to hold quality at volume.
AI speeds up code generation and moves the bottleneck to review, specs, and testing. Here is how to find the real constraint and budget for it.
Why vibe-coded prototypes die in production and what production engineering adds: architecture, testing, security, and the operations a demo never needed.
Why prompts are not specs and how durable specifications give AI tools the persistent context and quality bars that make AI-assisted code last.
Why bolted-on AI features rot in production and how to design intelligence as a real architectural layer: model abstraction, evaluation, guardrails, and observability.
Moving AI from pilot to production with an engineering partner: closing the reliability, data, monitoring, and operations gaps the pilot never had to, faster.
Data lakehouse architecture explained for enterprise leaders: what it is, why it combines the lake and the warehouse, and what to know before adopting it.
A Head of Platforms checklist for implementing multi-cloud: pursue it for a real reason, standardize across clouds, and contain the complexity, or consolidate.
Taking an observability strategy from strategy to production with an engineering partner: the gap between an observability vision and answering "what broke" affordably.
Why model latency optimization matters for scaling healthcare teams: slow AI inference becomes a clinical workflow bottleneck and a cost driver as usage grows.
A framework for data contracts in mid-market and enterprise teams: where to apply contracts, what to put in them, and how to enforce them without slowing everyone.
Taking a warehouse migration from strategy to production with an engineering partner: the gap between a migration plan and a migrated warehouse nobody's reports broke on.
Why deployment automation matters for scaling energy and utilities teams: manual deployment becomes a bottleneck and a risk as systems multiply near the grid.
How to approach change data capture in real estate: prioritize reliable, log-based capture of the data that matters, with deletes and schema changes handled, not just streaming.
How to measure and prove modern data architecture ROI: quantify the cost of the legacy foundation and the value of what a modern one enables, against the rebuild cost.
The common platform engineering pitfalls: building what nobody adopts, gold-plating, mandating instead of earning adoption, and no product mindset, and how to avoid each.
Hallucination mitigation explained: the concepts behind reducing and containing LLM hallucination, the benefits of doing it, and the trade-offs against cost and latency.
How to measure and prove buy-vs-build AI ROI: compare the fully-loaded cost of building against buying, per capability, including speed and the value of differentiation.
An SRE lead's introduction to CI/CD pipeline design: build the pipeline as a reliability tool, with the gates, safety, and rollback that protect production.
A CTO's checklist for multi-agent orchestration: coordinate AI agents reliably with clear control, bounded autonomy, and observability, before scaling complexity.
Taking production-grade AI from strategy to production with an engineering partner: building the reliability, monitoring, and safe-failure a working model lacks.
One long-form essay every other Wednesday. Written by the engineers shipping production AI for our clients, not by a content team. No promotional emails. Unsubscribe in one click.
