Individual contributors and senior engineers
For Engineers
The agentic development lifecycle from the practitioner's side: which skills are rising, which are quietly atrophying, and how to develop the ones that matter.
The AI-Era Engineer's Playbook — Part 1Are You an AI Dumper?Three patterns of working with AI tools — and why the most common one quietly stops your growth as an engineer.
The AI-Era Engineer's Playbook — Part 2The 17 Points You're Quietly LosingAnthropic's research found developers using AI assistants scored 17% lower on comprehension. The skills that atrophied are exactly the ones you need to supervise AI output.
The AI-Era Engineer's Playbook — Part 3What Your Interviewer Already Knows About YouThe new interview tests three things: specification quality, output evaluation, and failure-mode reasoning. Here's what that looks like from the candidate's chair — and how to prepare.
The AI-Era Engineer's Playbook — Part 4Before the Agent RunsWhen an agent does the implementation, the understand-while-building loop breaks. The cognitive work has to happen before the agent runs — and most developers are not naturally good at that.
The AI-Era Engineer's Playbook — Part 5Why Writing Code Taught You the Wrong Way to ThinkWriting code trained you to verify incrementally and trust progress. Agentic work hands you a finished artifact and asks you to distrust it. Those are opposite reflexes, and experience makes the wrong one stronger.- The AI-Era Engineer's Playbook — Part 6The Prompt That Reveals EverythingTwo developers, one ticket, two prompts. Both produced working code and only one produced correct code. The prompt is not a skill you learn; it is a transcript of thinking that already happened.
The AI-Era Engineer's Playbook — Part 7How to Write a Spec an Agent Can Actually ExecuteOne line of ticket, pasted into an agent, produced an export that leaked credentials, exposed other accounts and paged the on-call engineer. What the specification should have said, category by category.
Coming to this section
- Evaluating AI tools and platforms: vendor selection criteria
- Prompt engineering as a professional practice
- Designing your personal AI workflow
- AI-era code review protocols
- Debugging AI-generated code (it is different from debugging your own)
- Onboarding to a codebase you didn't build
- Knowledge management when AI does the implementation