Category
8 posts
A programming community I take part in watches for AI-generated answers. The moderators have real reasons; so do the people the rule lands on. Here are both cases argued fairly, the three things both sides actually want, and a tiered guideline — verification, disclosure, capacity — that is stricter than a blanket ban, not looser.
Throwing more tokens at a problem feels like progress. It isn't. The context window is a compute surface, not a database, and the distinction has real architectural consequenc
On July 9 OpenAI shipped GPT-5.6 as three tiers — Sol, Terra, Luna — from $1/$6 to $5/$30 per million tokens. Four days later, Anthropic's best model, Fable 5, comes off subscriptions and onto a $10/$50 meter. Same week, opposite directions. Is Fable 5 losing ground — and does Anthropic need to blink on July 12?
Token-shaving is fighting yesterday's war. The price of any fixed capability tier falls roughly 10x a year, so the real edge isn't a leaner prompt — it's an architecture that treats today's frontier model as a swappable, soon-to-be-cheap component.
In six months, AI coding agents went from features to a brutal platform war: $26B startups, a new frontier model every six weeks, pricing whiplash, and a reverse-acquihire that gutted a unicorn. The agent you build on is now a strategic bet.
Generating code with AI is cheap. Reviewing, testing, and deploying it isn't. The new bottleneck for engineering teams isn't writing the code — it's trusting it.
The phrase 'vibe coding' was a joke in 2024. By 2026, it describes how a non-trivial fraction of new software is actually built. The economics of this shift are stranger and more durable than the meme suggested.
For two years, the most-clicked role on LinkedIn was Prompt Engineer. In 2026, that role is quietly disappearing — because the model is now the one writing the prompts.