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Thoughts on software engineering, privacy, tooling, and building for the long term.
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Subscription tiers are a business decision, not a technical guarantee. When the math changes, pipelines built on token discipline adapt. Marathon pipelines break.
Anthropic markets the 1M token context window as the greatest thing since sliced bread. They are not wrong. But the hidden costs will eat you alive if you are not paying attention.
After nine posts about making agents work better, the last lesson is the simplest: sometimes you should just do it yourself.
Model updates will break your pipeline. The question isn't if but when. Build to absorb change, not to resist it.
Your pipeline is decaying 5% per sprint and you can't feel it. Four metrics that catch the slow killer before it catches you.
Before you spin up a fleet of AI workers, ask whether you should. Most teams are bottlenecked on specification, not execution.
Every token in the context window is either signal or noise. Noise doesn't just waste space — it degrades everything around it.
Every failure mode in this series traces back to the same root cause: the spec wasn't good enough. The spec is the product.
LLMs have no ego and no allegiance. Use adversarial framing to get review output that collegial prompts would gloss over.
Your agent's output will route around your gates. Not by intent — by optimization pressure. Here's how to close the gaps.
AI agents don't reliably follow instructions. Programmatic gates catch the drift that human review misses.
Context degradation is gradual, not sudden. By the time auto-compaction fires, your agent has been impaired for a while.
Privacy controls, pricing traps, multi-provider strategy, and token discipline — the packing list for getting started with AI coding.
A maturity model for AI-assisted coding — from copy-paste to semi-autonomous pipelines. Map your level and find your next step.