Every Mediovsky piece tagged Automation.
Google sunset developer tokens on September 9 and moved each access level to the Cloud projects that had called the API with that token in the previous 90 days. A token that sat idle lost its access, and v22 stops answering on October 7.
OpenAI gave developers six months’ notice and left the replacement column in its deprecation table empty. Adobe’s own list of retired partner models has ten entries dated April to September, so the pipeline has to be built to outlive the model.
Two migration dates are circulating with the wrong campaign types attached. Google’s 7% and the independent 16% come from different verticals, baselines, and years, and the question worth testing is whether the added volume was ever new.
Every team now has models writing, classifying, and summarizing at volume, and almost none can say whether the output got better or worse last month. The fix is an eval: a frozen set of real inputs, a rubric that discriminates, and a threshold. The research on how to build one is unusually clear, and unusually uncomfortable about the shortcut everyone takes.
You can run Google Ads from a terminal now: read the account, draft the change, spend the money, all in plain English. This is the build guide. Which of the four paths to use (scripts, the official read-only Model Context Protocol (MCP) server, a community write server, or the raw v25 API), what v25 quietly broke, and how to wire a write path that fails safe instead of fast.
Tymbre went from a clickable mockup to the App Store in 30 days, built by one person after hours, with AI. Here is the full product: 1,116 launch-period commits, a self-hosted core architecture in Warsaw, adversarial QA, and the lesson that the bottleneck was never the typing.
AI, Digital, Automation, Measurement. Every business already runs on all four, and is fragile in the exact one nobody is watching. ADAM is a diagnostic that plots you on a 4×5 matrix, names your weakest pillar, and forces one decision per quarter. Here is the whole framework, and the free playbook.
Claude Code already pulls last week’s numbers, drafts the negatives, and stages a budget change across Google Ads and Meta, wired in through the Model Context Protocol (MCP) and the platforms’ own APIs. Here is how the plumbing works, why Google’s own connector refuses to spend a cent, and where the guardrails still have to come from you.
Model Context Protocol (MCP) turns a large language model (LLM) from smart autocomplete into an agent that can touch your CRM (customer relationship management), analytics, and ad platforms through a single standard. How the handshake works, who adopted it, and the security perimeter it opens.
When a single AI agent hits its ceiling, the work moves to a fleet with an orchestrator on top. The 2026 problem is no longer model quality. It is coordination, guardrails, and reliability.
The 2026 workflow-automation choice is not really n8n versus Make. It is knowing which marketing tasks belong to a no-code canvas, which need real code, and which should stay in a human’s hands.
Google Ads scripts still earn their keep in 2026 doing deterministic, boring work: budget alerts, zero-conversion flagging, out-of-stock pausing. AI can now draft them from a sentence. The judgment about the rules is still yours, and that’s the whole point.
