Matching token prices do not mean matching costs
Matching input and output base rates do not make two AI models equally expensive. Compare context tiers, caching, effort, retries, and cost per accepted result.
Matching input and output base rates do not make two AI models equally expensive. Compare context tiers, caching, effort, retries, and cost per accepted result.
A working leader's changelog for June to September 2026 — what changed in Cursor, Claude and ChatGPT, what it means if you run a team, and the parts worth ignoring.
A large context window can help with whole-document work, but input limits, long-context prices, permissions, and retrieval quality still need attention.
A training cutoff is a warning about built-in knowledge, not a guarantee about every answer. Use current sources and check both the facts and the conclusion.
Reasoning effort changes the cost, latency, and behavior of supported models. Test settings per task and keep validation and human review where needed.
Prompt caching can reduce repeated input charges. Check eligible prefixes, writes, expiration, and measured reuse before estimating what it will save.
Review ten common prompt habits through controlled comparisons. Keep useful roles, examples, and constraints, and remove instructions that fail to earn their place.
Clear requests can improve AI answers, but do not eliminate errors. Six habits for defining the task, supplying evidence and checking what comes back.
Claude, ChatGPT and Cursor all have a feature called Projects. They solve different problems, and assuming they are equivalent is how teams end up with the wrong one.
Grounding makes AI research easier to verify, but cannot guarantee true citations. Retrieve sources, inspect quotations and check the claims that matter.