Uber burned its 2026 AI budget in four months — and now caps Claude Code
Uber spent its full 2026 AI coding budget in four months. Its fix: $1,500 per employee, per month, per tool.
Working methods, workflows and routines around the productive everyday use of AI — from repeatable processes and speed tooling to cost-aware handling of tokens.
“Working with AI” is not about the theory behind language models, but about the productive everyday: how you actually build AI into your daily work so that you get better results faster. It matters less which model you use and more how you use it — with which processes, routines and ground rules. That is exactly what separates “I once asked ChatGPT” from a system that delivers reliably.
It all starts with workflows — repeatable processes for content, code or research. Instead of reinventing every task, you build fixed steps: prompt templates, clear input and output formats, review and correction loops. A good workflow is documented and transferable, so others can use it too — or you automate it later.
On top of that sits productivity. This is about speed: running several tasks in parallel, choosing the right tooling (from the chat interface to the API to agents that work through multi-step tasks on their own) and establishing routines that remove friction. Not every task belongs with the AI — the trick is recognising where it genuinely saves time.
A central field of application is content creation: AI-assisted texts for blog, glossary and lexicon, drafts, rewrites, research summaries. The AI delivers the first draft and the structural scaffold — the subject-matter judgement, the fact-checking and your own tone stay with you. Especially with scaling content, clear templates are worth their weight in gold.
The frame is set by the token budget. Every request costs input and output tokens, and at high volume that adds up quickly. Working cost-consciously means: keeping prompts lean, choosing the right-sized model (not always the most expensive one), passing context deliberately and using caching wherever possible. That keeps the effort affordable even in continuous operation.
Below you’ll find the topic world around working with AI: current news on new tools and methods, blog articles with hands-on setups, lexicon articles for deeper dives and a glossary of the most important terms. Use the topic filters above to jump straight to workflows, productivity, content creation or token budget.
Uber spent its full 2026 AI coding budget in four months. Its fix: $1,500 per employee, per month, per tool.
Article 50 of the EU AI Act applies from 2 August 2026. Yet AI-generated marketing and SEO text is exempt from the disclosure duty — here is why.
2026 industry data: agentic AI burns 5–30x more tokens than a chat. What that means for multi-agent budgets.
Klarna saved $60M, General Mills $20M: the case studies impress. But only ~29% of firms see clear ROI. The sober 2026 balance sheet.
A typed keyword triggers a fixed AI routine — and every single step must be committed before the next one appears. Why that's the actual trick.
How I turned my CLAUDE.md from a style guide into a token budget — 6 principles for lower cost, less waiting, and more honest reporting.
Why dictated text got swallowed, how SoX normalization and a model switcher fixed it — and what is actually happening under the hood.
The session URL had suddenly vanished from the terminal. How we found it again via a server endpoint — and why it enables working above the IDE.
How I keep an AI knowledge base current with RAG — searchable, maintained by a keyword and re-indexed on its own at night, instead of going stale.
Headless agents on your own machine, fed by the subscription you already pay for instead of an API bill — how boostN orchestrates many models.
Claude Code is Anthropic's official AI coding tool. How it's built, what it does, and how tool use, MCP and permissions fit together.
How the Model Context Protocol connects Claude Code to databases, APIs and custom tools — and what to watch out for on permissions.
How the auto-classifier in Claude Code scores tool calls live, blocks risky actions, and which defense layers sit behind it.
The most important features in Claude Code — from slash commands and hooks to plan mode, sub-agents and MCP integration.
Memories, CLAUDE.md, and slash commands are suggestions — not commands. What it really takes to make AI models stop reliably on critical actions.
Pair hard tasks with easy ones — and why a prepared content workflow makes engineering wait times productive instead of dead air.