Tag
A practitioner reports using GLM-5.3 Flash for day-to-day software engineering across millions of lines of code in production, claiming its speed does not come at the cost of capability and that it outperformed expectations versus frontier models for repo exploration, refactoring, and implementation.
An opinion piece arguing that Common Lisp is now the best programming language because LLMs shift the bottleneck from writing code to fast feedback loops, live image-based development, interactive debuggers, and macro-driven domain-specific languages that cut code volume and token costs.
A reflective post re-examining Brooks's classic 'The Mythical Man-Month' in the AI era, arguing that while AI can generate runnable code quickly, the real bottlenecks shift from human coordination to specification and verification of large-scale software systems.
This article breaks down how Uber and other companies built an 'AI-native software factory' — the cloud infrastructure, model gateways, and agent orchestration around bought coding agents like Claude Code and Codex — offering a step-by-step map of the six building blocks and a rollout plan for other engineering organizations.
A user praises Devin, Cognition's AI software engineer, saying it has significantly improved and that the marketing ads accurately described its capabilities.
博客文章指出整数与浮点算术遵循不同范式：整数运算需在灾难发生前预防溢出，而 IEEE-754 浮点运算中的 NaN 与无穷值会自然传播，不应照搬预防式检查（如用零分母或 FLT_EPSILON 守卫），这些检查在多数场景下无效且被 LLM 频繁写错。
Atlassian released its AI SDLC transformation playbook, sharing best practices for deploying AI at enterprise scale across the entire software development lifecycle, from planning and design to review and maintenance.
A developer asks how to prevent AI-assisted coding projects from quickly degrading into unmaintainable, low-quality code — a widely shared concern about code quality with AI tools.
This is a Chinese transcript of a Computerphile video that dives deep into how the undo/redo functionality in software is implemented, covering user expectations around the granularity of undo actions, implementing multi-level undo using stacks (two stacks), and the challenges of memory overhead and snapshot storage in bitmap editing.
DHH argues that reviewing every line of AI-agent-generated code is unsustainable and that the future lies in adversarial agent reviews plus automated testing, while @msimoni pushes back, expressing distrust of software written by AI that nobody actually read.
研究提出将智能体记忆类比 Git 仓库，通过 commits 与 branches 管理任务版本，使智能体能丢弃过期需求并从正确版本恢复。方法 GitHarness 在全部 30 个测试设置中优于普通续跑，某设置下编码任务 token 消耗减少 73.6% 且得分更高。
Gergely Orosz discusses how the economics of introducing and paying off tech debt have changed dramatically, questioning how much tech debt still matters in the AI era for software engineering teams.
Gergely Orosz reveals that Uber now operates fewer microservices in 2026 than it did in 2020, continuing the industry shift toward well-sized "macroservices" to reduce maintenance overhead. More details will come in an upcoming podcast with Uber engineer Uday Kiran Medisetty.
Baoyu's commentary on the podcast interview with Lauren Tan and Matt Pocock: Lauren builds the Grok Bot at SpaceXAI and merges 2,500 PRs a month without reviewing each one, relying instead on AI-driven automated verification, code conventions, and an open-source Skill system called pstack to guarantee quality. The article cautions readers not to rush to copy her approach — first understand what makes it possible for her: models strong enough, token costs, the limits of AI verification, and having your own domain expertise to steer things.
Wavestone AI Lab dissected 11 coding agents — including Claude Code and Codex — and found they all share a common 7-part harness blueprint (loop, LLM layer, tools, memory, safety, orchestration, extensions), ending with a reusable 90-line harness and 18 design recommendations.
A tweet noting that future AI models will be much better at deleting and simplifying code, arriving just in time for developers.
A practitioner with C/C++ experience at Microsoft and Google runs 12 AI code-review experiments on real PRs, finding that fresh reviewer agents with no shared context catch far more defects, that executing code (not just reading it) is essential for logic bugs, and that model choice affects cost-effectiveness more than raw capability.
The essay argues that every SaaS company will evolve into a 'harness' around a stateless LLM — progressing from no harness, to individuals operating harnesses, to individuals orchestrating them, and finally harnesses orchestrating people — turning the company itself into model infrastructure where humans supply context, taste, and review.
Researchers from Meta, Stanford, Harvard, and UW release SWE-sweep, an open-source benchmark that tests whether language model agents can proactively find and fix undiscovered bugs in entire codebases, with results scoring far below expectations and notable cost-efficiency gains from Luna xhigh.
An opinion piece arguing that agentic coding is causing four major problems — 'slop' code that repels humans, engineer alienation from their craft, degraded codebases, and weakened team ownership — with little sign of a solution on the horizon.