Intended readers: Software engineers torn between the standard career advice—”follow your passion,” “maximize salary,” “find a role that fits your personality”—and the reality that none of it quite...
Intended readers: Mid-career engineers who want to become “full-stack” or “can-do-anything” generalists—or who want to understand what that actually means. People trying to figure out the right ord...
Intended readers: Engineering managers and tech leads wrestling with retention and performance, HR professionals whose engagement programs aren’t moving the needle, and executives thinking about or...
Who this is for: Early-to-mid-career engineers unsure which way to go after being told “these days you should be able to do everything,” plus tech leads, EMs, and HR people who design engineer deve...
Who this is for: Engineers unsure how far they should check AI output, and the tech leads, EMs, and QA leads designing review and QA workflows around an AI-first reality Assumed knowledge: Some h...
Who this is for: Engineers who feel (or are being told) that “with AI, deep specialization is no longer needed,” and tech leads and HR who plan engineer development and team structure on an AI-firs...
Intended readers: execs, HR, and org-development leads rolling out AI; tech leads / EMs responsible for growing their teams; and frontline leaders Prerequisites: some hands-on experience with gen...
Who this is for: IT managers who now decide org direction and team structure (especially the newly promoted), tech leads / EMs, and execs or HR rethinking organization design Assumed knowledge: A...
Intended readers: engineers unsure how far to widen their coverage, tech leads / EMs designing team structure, and execs / HR thinking through org design for the AI era Prerequisites: a basic fee...
Audience: Engineers choosing between Claude and ChatGPT for work, decision-makers shaping company AI policy Prerequisites: Hands-on experience with an LLM and prompts Reading time: 11 minutes ...