Cutting Your Token Bill Without Cutting Quality
When an AI feature costs more than it should, the answer is rarely a cheaper model. Here are the techniques we reach for first, in the order we reach for them, and the savings they actually deliver.
The Real Total Cost of an AI Feature
The token bill is the cost everyone sees and the smallest part of the real number. Here is the rest of the iceberg: evaluation, monitoring, human review, and the quiet maintenance that keeps an AI feature working.
Charging Clients for AI When the Cost Is a Moving Target
Fixed-price delivery and variable per-token cost do not naturally fit together. Here is how we structure AI work commercially so the client gets a clear number and we do not absorb an open-ended bill.
How We Pick a Model: Frontier, Mid, or Cheap
The instinct is to reach for the most capable model and stop thinking. That instinct quietly wastes money and adds latency. Here is the decision we actually run for every AI feature we build.
The Token Bill Nobody Budgeted For
Token pricing looks trivial on the pricing page and arrives as a real number on the invoice. Here is how AI features actually accumulate cost, and how we budget for it before a line of code is written.
Falling AI Costs, Except at the Frontier
The cost of a given level of model intelligence has dropped sharply and keeps dropping, even as the newest flagship models get pricier. That gap changes which features are worth building, and it punishes anyone who treats today's price as permanent.
AI Readiness Audits Are Quietly Becoming Most of Our Consultancy Work
A year ago clients hired us to ship features. Now they hire us to tell them whether their codebase can survive the AI feature their CEO already announced. The findings are starting to repeat.
A Year of Code Agents in Anger: What Actually Stuck
We have used Claude Code, Cursor, Aider, Cline, and most of what is between them on real client work for over twelve months. The tools that survived our rotation are not the ones the launch hype tipped to win.
When Vibe-Coded Software Hits Production: The Patterns We Keep Cleaning Up
Over the past year we have inherited a growing number of codebases built heavily with AI assistance. The failure modes are starting to repeat. Here are the ones we see most often.
Vibe Coding vs Engineers: The Difference Is Still Judgement
The real debate is not whether AI replaces engineers. It is what engineers actually do that AI still cannot, and why those skills are quietly becoming more valuable, not less.
Vibe Coding, Honestly: What It Is, What It Isn't, and Where It Breaks
Vibe coding has gone from a Karpathy post to a full cultural moment in under a year. Here is what it actually is, where it works well, and the specific places it quietly falls apart.
RAG Is Not Magic: Why Your Retrieval Is Quietly Failing You
Retrieval-augmented generation is pitched as the answer to 'make the LLM use my data'. Most implementations are worse than they appear. Here is where the rot usually is.
LLMs as a Development Tool: An Honest Assessment
After using AI tools heavily in day-to-day engineering work, here is where they genuinely help, where they create more work than they save, and what we have changed our minds about.
Building AI Agents That Actually Do Useful Work
Everyone is building agents. Most of them are not production-ready. Here is what separates the demos from the ones that genuinely work.
Why Most AI Integrations Fail in Production
The gap between a working demo and a reliable AI feature is wider than most teams expect. Here is what goes wrong, and how to avoid it.
The Future of AI in Software Development: Beyond Code Completion
AI is reshaping how software gets built — but the real transformation goes far beyond autocomplete. Here is what we are observing and building with in production.