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Kent C. Dodds gives a talk titled 'The Last Software Engineer' at Nerdearla 2026, discussing the future of software development in the AI era.
The author argues that coding agents, despite their capabilities, make software engineering more challenging and require extraordinary discipline, referencing recent AI models like Opus 4.6 and GPT-5.2 in the discussion.
Taste-Bench is a benchmark that evaluates LLMs' ability to choose optimal paths at decision forks in long-horizon tasks, using trajectories from software engineering and machine learning research, with a leaderboard indicating current top models like GPT-5.6 Sol achieving 59.7% accuracy.
Simon Willison notes that coding agents, while powerful, add complexity to software engineering, requiring significant discipline and knowledge to unlock their full potential.
Kent C. Dodds demonstrates a technique to prevent low-quality code generation without needing to manually review the code.
This paper introduces PLLM+, a hybrid pipeline that combines deterministic replay of historical dependency configurations with LLM-based repair to resolve Python dependency conflicts, showing improved success rates and reduced runtime on the HG2.9K benchmark.
Stanford University's CS329Z course on Engineering AI Agents has started, with lecture slides now public, covering topics from models to autonomous systems and featuring instructors like Diyi Yang and Michael Ryan.
This article discusses the importance of evaluations in AI systems, explains why traditional testing is insufficient, introduces three main types of evaluations, and provides implementation suggestions.
The tweet discusses using formal verification methods like TLA+ and Lean to ensure the correctness and scalability of mission-critical AI infrastructure software, with references to Intent Lab and Boris Cherny's work on the Claude Agent SDK.
The author describes an experiment where merging two AI agents' git worktrees led to test failures despite clean merges, highlighting the challenges of parallel agent development without mutual awareness.
SWE-Bench Pro V2 is an updated benchmark for evaluating AI agents in software engineering, featuring 642 tasks across 11 repositories with improved evaluation protocols and contamination controls.
Casey Muratori emphasizes the importance of learning assembly language for software optimization in a discussion shared by Gergely Orosz.
This article critiques the over-reliance on AI in software development, arguing that AI lacks wisdom for code maintainability and architecture, which can hinder developer expertise and lead to long-term issues.
A tweet sharing Lauren's talk on merging 2000 pull requests in a month, emphasizing creativity and practical workflow in software development.
A developer built a router to cut AI agent costs but found it only escalates requests, increasing spending; effective savings came from caching rather than routing.
Cognition introduces new CLI and SSH access for Devin Cloud, enabling developers to manage AI coding sessions directly from the terminal and interact with a dedicated VM.
A software engineer describes Claude as his cofounder, sparking a discussion on humanizing AI tools in software development.
Gergely Orosz is preparing a lecture for sophomore students on software engineering, revamping his 2024 talk with an angle emphasizing proactive learning.
This paper introduces SWE-Proof, a benchmark of formally verified code patches for real-world software issues, demonstrating that formal verification improves error detection in LLM-generated code and identifies specification synthesis as a key open problem.
A software engineer reports that AI models seem to be getting worse at following instructions in recent updates, often making unasked changes and ignoring contracts, leading to increased manual work.