Why Your Spend Limit Doesn't Survive a Fresh CI Checkout
A per-day API spend cap enforced in code still failed, because every scheduled run started from a clean checkout with no memory of prior spend.
Articles exploring claude-code through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
19 articles
A per-day API spend cap enforced in code still failed, because every scheduled run started from a clean checkout with no memory of prior spend.
Prompt caching is supposed to be free money. On calls spaced further apart than the cache actually lasts, it's a straight surcharge with nothing recouping it.
We named our cost-control setting something that collided with Claude Code's own environment. Every automated run quietly inherited the most expensive option.
A twice-daily job billed against the API drained an account balance for days. Moving it to a subscription runner fixed the bill and the blind spot it created.
Isolated AI coding sessions can't see your main .env file, so they quietly mint duplicate API keys instead of asking. The mechanism, diagnostic, and fix.
An AI assistant answers fluently whether a fact is current or stale. Here's the rule for knowing what to verify live instead of trusting memory.
The AI that wrote your draft is the worst reviewer of it. Here's the independent-review technique that catches what a second read-through misses.
Token totals and dollar totals are the metrics everyone reaches for first when auditing AI agent spend — and they are frequently the wrong ones. A better diagnostic, and a portfolio-style framework for model selection.
Most people accept an AI agent’s first answer. A three-layer audit — primary-source check, realistic-input test, goal re-derivation — catches what pattern-matching misses.
Most Claude Code advice focuses on the prompt. The habits that actually determine reliable output are upstream of that — and they're the same ones Anthropic's own documentation recommends and the tool's creator uses daily. Here's where those two sources agree, why it works mechanically, and what to actually do about it.
Most Claude Code advice — including Anthropic's own conference talks — describes a tool that no longer exists. Six changes actually change how you should work: rewind, auto mode, background subagents, worktrees, agent teams, and adversarial review.
The dream of a 'master orchestrator' that auto-picks the cheapest model that can do each coding task is a research spiral, not a shortcut.
There's no single best AI coding tool — the largest study of real merged pull requests found no universal winner.
One-shotting a feature with AI isn't luck — it's a method. The goal isn't the prettiest first draft; it's the fewest total tokens to a change that compiles…
Most token-saving advice for Claude Code quietly makes the agent worse — starve its context or drop to a cheap model and you pay for the retries.
Token price and benchmark scores are the wrong scoreboard for choosing an AI coding model.
Master Claude Code hooks to automate your development workflow. Learn how to configure pre-commit, post-commit, and custom hooks.
Learn how to use MCP servers to extend Claude Code with custom tools, database access, and API integrations for your development workflow.
An honest comparison of Cursor and Claude Code after months of daily use. Different tools for different workflows, and why I landed where I did.