A GitHub marketing lead automated event operations end-to-end by treating each event as a GitHub Issue: issue forms capture inputs, labels act as triggers, GitHub Actions stage the event, and Copilot 'skills' (Markdown runbooks) handle planning and post-event follow-up. She argues the pattern applies to any repetitive work touching tools with an API or CLI, weighing it against packaged marketing-automation platforms: regional variation favors custom pipelines with PR-based change management, though a silent cron failure shows monitoring is still required.
The article pushes back on five common AI hot takes: that you can skip reading AI-generated code, that not using AI hurts hiring, that Skills killed MCP, that RAG is dead, and that needing fine-tuning signals bad code. It argues each is oversimplified—review depth should match actual risk, AI fluency is about judgment, MCP and Skills solve different problems, and RAG still grounds answers. It closes by urging readers to test ideas by building rather than debating.
The author argues that 'brain-off' LLM use—having agents write code and just relaying failures back (being a 'meat proxy')—still produces poor-quality software, failing on out-of-distribution problems and overfitting to tests or metrics. Even if it eventually yields good software, it won't benefit the employee, since a company could run the LLM loop without them and lay them off. He contends the present is the worst time to disengage, as companies are eager to cut low-contribution roles.
The author ran a fully autonomous 'software factory' with GPT 6 Astra for 35 hours (~$1200, 79 commits, 75k lines of code) and got nothing usable. His core complaint is that the model's token-efficient 'codegolf' Python for tool calls leaks into committed code, producing unreadable and bizarre output, and that it won't stop on oversized tasks. He argues training rewards local metrics over human readability, and doubts these costlier models still fit day-to-day software engineering versus other use cases.