Notes from the build.
What we have learned shipping AI-assisted software that has to survive in production.
Giving AI Coding Agents the Right Context, Not More of It
Teams often fix a confused AI coding agent by adding more context. The better fix is structured context: skills, live data access, retrieval and memory.
ReadFrom AI Demo to Production: The Engineering Work in Between
A working demo and a production ready AI feature are built from different disciplines. This is the engineering work that separates the two.
ReadWhat Makes AI Coding Agents Production Ready
The model behind an AI coding agent is the easy part. What decides whether it works in production is the harness, the layered architecture and the review process around it.
ReadWhat AI Coding Agents Get Wrong That Tests Do Not Catch
AI coding agents rarely write broken syntax anymore. The defects that matter now are silent assumptions and concurrency bugs no test suite surfaces.
ReadThe Operating Model Behind Reliable AI-Assisted Coding
Faster AI code generation does not by itself produce reliable software. The discipline around context, verification and parallel review decides the outcome.
ReadWhat Next.js Caching Teaches About Briefing AI Coding Agents
Next.js 16's cache components and AI coding agent context both come down to the same discipline: deciding on purpose what a system is allowed to assume.
ReadTurning Code Review Into a Self-Improving Feedback Loop
As AI agents write more code, reviewing every line stops scaling. The fix: review automation that learns from a team's own history and gets checked the way tests check code.
ReadHow Web Teams Keep Control When Running Multiple AI Coding Agents
Running multiple AI coding agents at once only works with scoped tasks, isolated work and independent review, not a single unmanaged prompt.
ReadWhy AI-Generated Code Needs Outcome-Based Review
As AI writes more of the code in a typical project, review has to shift from checking syntax to validating that the result actually meets the business requirement.
ReadWhy Fast AI-Generated Code Still Needs Production Engineering
AI coding tools ship changes fast, but speed without architectural review creates a new kind of technical debt that costs far more to unwind.
ReadThe Line Between an AI App Builder and a Production App
AI app builders can take an idea from description to a live prototype in minutes, but turning it into a product people can trust needs a different kind of work.
ReadWhy AI-Generated Code Needs Tolerances, Not Just Trust
AI coding tools are non-deterministic, which changes what counts as correct in production code: why directing, reviewing and refactoring output matters more than trusting it.
ReadWhat Tokens, Context Windows And Hallucinations Mean For Code
Understanding tokens, context windows and hallucinations helps product teams catch the real risks in AI-assisted code before they reach production.
ReadSpec-Driven Development: A Better Path Than Vibe Coding
AI can generate a polished front end in minutes, but a working product needs a specification, a review step and senior engineering judgement behind it.
ReadA Risk-Based Framework for Trusting AI-Generated Code
Most teams either ban AI-written code outright or let it into everything. A tiered approach, matched to review infrastructure and task scope, works better.
ReadCode Review Is the Real Bottleneck in AI Assisted Development
As AI agents write more code, review becomes the constraint that decides whether a product is safe to ship. Here is what a layered process looks like.
ReadWhy Faster AI Coding Doesn't Speed Up Software Delivery
AI can write code fast, but speed at the coding step rarely shortens delivery. The gains come from disciplined review, not from typing faster.
ReadVibe Coding Risks: How Senior Developers Use AI Strategically
Vibe coding creates production fragility with code no one fully understands. Strategic AI use for boilerplate and relentless review preserves debugging skills and system intuition.
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