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A software engineer writing under the 'clanker' label describes insisting on fully reviewing code generated by AI coding tools before shipping it, rather than trusting it blindly. The author says accountability for production failures rests with the human whose name is on the pull request, not the AI tool that wrote the code.
In a blog post titled 'Resisting Cognitive Surrender,' developer Kunal describes the temptation to uncritically accept AI agent outputs without reviewing or understanding the code produced. He argues this approach, while fast and low-effort, often leads to suboptimal decisions, accumulated technical debt, and reduced familiarity with one's own systems. The post reflects on his own workflow and calls for finding a better balance of trust when collaborating with AI agents.
Rachel Laycock, CTO at Thoughtworks, writes that many skilled developers remain cautious about fully adopting AI coding agents despite the firm's history of embracing early tools and practices like continuous integration and automated testing. She says a Technology Radar team discussion in September helped clarify that the hesitation isn't primarily about job security or love of hand-written code, but a deeper concern tied to quality and production-readiness.
An Anthropic engineer argued that conventional coding interviews no longer effectively measure a candidate's real-world ability, given how widely AI coding assistants are now used in actual software development. The claim centers on the idea that interview formats have not kept pace with how engineers work day-to-day with AI tools.
Amazon published updated engineering guidelines on October 27 warning staff against 'tokenmaxxing' — padding AI usage metrics to appear productive. The company said metrics like token consumption and adoption rates are 'deeply flawed' when disconnected from real outcomes, and said it prefers measuring long-term results instead.
Amazon circulated a memo to software engineering staff in September, later posted publicly, warning against 'tokenmaxxing' — overusing AI tools regardless of output quality. The memo updates how Amazon defines the roles of software development engineers and managers as AI becomes more embedded in coding work.
An AI/productivity writer describes an ongoing disagreement with developer Matt Pocock over spec-driven development methodology. The writer contends that clearly articulating intent and specifications matters more than the resulting code, while Pocock treats code as the central source of truth and favors heavy human oversight in AI-assisted workflows.
The piece presents a short poem titled 'Of Code and Shortleaf Pine' that draws a parallel between the branching structure of pine trees and the logic of computer code. It reflects on the stillness of nature against the restlessness of an engineer's work, framing both as forms of interconnected, purposeful structure.
A Hacker News 'Ask HN' post cites a LinkedIn message from a Meta engineer, Ali Hamie, who says he increasingly ships AI-written code without fully understanding every line. Hamie describes feeling uneasy that reviewing all AI-generated code in detail would be too slow, leaving him with only a high-level grasp of systems he builds. The HN post asks other engineers how they are coping with this shift.
Microsoft and Carnegie Mellon University's Kenneth C. Griffin School of Computer Science have launched the Preceptorship for AI-accelerated Software Engineering (PRAISE) program. It lets early-career Microsoft software engineers take CMU online courses in software engineering and AI while receiving structured mentorship from senior engineers at the company. One participant, Jace Watson, takes the courses remotely from Seattle while working full-time at Microsoft.
The IEEE Computer Society's Columbia Section Chapter (Region 3) held a technical session examining how AI in software development is moving beyond simple code-completion tools. Speakers discussed the emergence of agentic AI systems capable of more autonomous roles in enterprise software engineering workflows.
Margaret Hamilton, the computer scientist who led development of the onboard flight software for NASA's Apollo missions at MIT, has died aged 90. She is widely credited with coining the term 'software engineering' and later founded companies building programming systems used in aerospace, defence and other industries.