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A wave of startups, including &Truth ($70M), Harmonic ($295M), and Theorem with Anthropic, are betting generative AI can make formal software verification practical, potentially transforming how reliable AI-generated code is validated.
Shopify unveiled its internal AI infrastructure, Remote Dev: 61% of mobile PRs that were ultimately merged were completed independently by an AI agent in Slack. With development environments moved to the cloud, developers only need to assign tasks in Slack.
Matt Pocock announces his most ambitious course, 'Software Factories for Real Engineers,' a 2-week cohort starting November 9th that teaches engineers how to constrain AI coding agents with classic software engineering fundamentals (automated review, codebase design, DDD, mutation testing, feedback loops) to build a trustworthy 'software factory' and avoid AI-generated code slop.
An essay reflecting on the author's career across tech startups, arguing that technologists are trapped in a system where engineering labor serves business narratives rather than genuine innovation or value creation.
Simon Willison shares a quote from Amazon engineering leadership's Terk Terkowitz, arguing that engineers who can explain where AI does not yet help their work are demonstrating valuable judgment rather than failing.
Cognition introduces FrontierCode 2.0, a coding benchmark that evaluates whether a model's pull request would actually be merged by a maintainer — testing intelligence, codebase fluency, and judgement beyond simply meeting a spec.
Ilya Raz interviews ~2 dozen researchers, engineers, and managers about real-world coding agent usage, concluding that fancy harnesses don't work, stock tools like Claude Code/Codex suffice, and that teams must guard against slop infestation, overbuilding, and eroded collaboration.
Skill Dreaming is a framework that extracts executable knowledge from a repository's pull request streams to build and continuously refine a repository-specific skill library for LLM coding agents, enabling co-evolution of PRs, repositories, and skills.
A practitioner shares hard-won lessons from running multi-agent teams around the clock, arguing that excess process—not bad models—is the main failure mode, and describing a lean workflow that built 11 C# libraries (~1.14M lines) in six days using design interviews, orchestrator-per-library units, handoffs over long contexts, and external test suites.
一篇关于 AI 编码代理（如 Codex）承担大部分开发工作后，工程师仍需保留何种技能的思考文章，作者以 SonarQube 报错引发的排查经历为例，提出「知道何时出错并能深入排查」比亲手写代码更重要。
An essay exploring why technologists ("Johnny") compulsively create layers of abstraction, examining the human and cultural drives behind abstraction in computing.
A developer reflects on how AI evolved from a 'rubber duck' debugging companion into a capable coding assistant, weighing its benefits against risks like code entropy, skill atrophy for juniors, and unhelpful assumptions.
SWE-Shift 是一个评估编码智能体在执行过程中遭遇环境、依赖与需求变化时表现的新基准，包含120个场景和12类变化。在评测的17个编码智能体系统中，Shifted 条件下的成功率均低于 Clean 条件，GPT-6 Astra 从 67.35% 骤降至 24.79%。
Charles Miller argues that short-lived "AI skills" like prompt engineering have little long-term value, since such tricks are quickly absorbed into products and become obsolete, comparing them to outdated programming folklore like preferring ++i over i++.
A practitioner describes building a coding harness where fleets of Claude Code agents pick up Jira tickets and implement features, and recounts a surreal moment where two humans essentially just relayed messages between each other's Claude agents before the coding agents took over — questioning whether humans are still needed in such workflows.
TEX 是一个基于执行交叉验证的协作式测试时扩展框架，多个智能体并行生成测试与补丁并交叉执行形成反馈矩阵，在 SWT-Bench Verified 上达到 86.4% 的新 SOTA，比单智能体基线提升 9.0%。
A software engineer reflects on society's eagerness to automate human labor through AI, questioning what progress is meant to achieve and whether eliminating work also requires eliminating dependence on selling labor to survive.
作者回顾一年来AI编写全部代码的经历，分析为何科技行业并未出现预期的AI失业潮，认为高利率才是裁员主因，并提出AI尚未取代岗位的若干原因。
A blog essay arguing that agentic AI coding has turned code from a hard, stonelike material into malleable clay, enabling rapid 'modeling' workflows. It contrasts two emerging human-involvement extremes: the artisan staying in the loop versus the factory-like automated approach.
A Hacker News discussion sparked by a Meta engineer's LinkedIn post questioning the growing practice of shipping AI-written code without fully understanding it, asking how developers cope with this new standard.