Strong Rails for AI-Assisted Development
AI coding gets better when teams rely on strong foundations: opinionated frameworks, compile-time constraints, deterministic tools, and focused tests.
Software EngineeringAgent notes
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Migrations Need a Control Layer
Why software migrations fail when teams treat them as simple data moves instead of operational change that needs verification, ownership, and fast feedback.
EngineeringProve the Case for Agentic Systems Before Building Them
A useful agentic system starts with a real business need, a narrow scope, and a workflow that can be validated without guesswork.
AI EngineeringCollaborative coding with competing LLMs
Using two models against each other can improve plans, reviews, and security checks without giving up human control.
EngineeringKeep Your Hands on the Wheel in AI Coding
AI can write a lot of code, but the human still needs to own the direction, the tradeoffs, and the final decisions.
EngineeringSmall Agent Systems Win on Determinism
Why agent workflows stay better when each piece stays narrow, explicit, and easy to reason about.
EngineeringBusiness Need Before Agentic Systems
Agentic systems only make sense when the business problem is real, bounded, and worth the added complexity.
Product DevelopmentHuman Leads, AI Assists
The most effective AI coding workflows keep the programmer in control, use simpler systems, and turn decisions into explicit checkpoints.
Software DevelopmentHuman-Led Micro-Agents and Deterministic Workflows
Why the best AI systems keep the human in control, split work into small skills, and use clear decision boundaries instead of heavy agent machinery.
AI EngineeringSkills First, Memory Later
Why deterministic skills, markdown harnesses, and retrieval layers make agent systems simpler than memory-heavy architectures.
Agentic Systems