Tag
MindForge is an automated pipeline that converts open-source command-line programs into source-free training environments for small language models. Fine-tuning Qwen3.6-27B on trajectories generated by GLM-5.2 significantly improves its performance on software engineering benchmarks, achieving results comparable to larger models.
Richard Feldman compares dependency graphs of different programming language websites and games, pointing out that most dependency choices are cultural norms rather than technical necessities.
An article discussing practical approaches to AI software engineering.
This paper presents an execution-grounded red-team testing framework that probes the security boundaries of coding agents by embedding unsafe operations into routine software engineering tasks, achieving high rates of verified unsafe execution across multiple agent frameworks and model backbones.
This article argues that traditional CI fails for AI agents and proposes replacing it with a merge queue where all tests run before merging, allowing agents to fix issues before breaking the build.
The article presents a framework for deciding how much autonomy to give AI agents based on two factors: ease of checking the output and ease of undoing errors. It introduces four levels of delegation, from agent as assistant to full self-driving mode, and illustrates with a decision tree.
EvoCode-Bench is a multi-turn coding benchmark with 26 tasks across 5 domains, designed to evaluate AI agents on evolving specifications and cumulative testing in a persistent workspace, revealing that single-turn scores dramatically overstate reliability.
A reflection on how obsession with productivity tools can distract from solving the right problems, illustrated by a Facebook engineer who used simple tools like Sublime Text and printf debugging.
An analysis of Linus Torvalds' unique role as a leader who stopped writing code early to focus on coordination and direction of the Linux kernel project, contrasting with other maintainers.
An open-source AI software engineering system called Kodiak is being built, capable of planning, researching, coding, testing, and reviewing code.
The author argues that LLMs currently provide about a 2x productivity boost for coding due to their ability to handle easily verifiable tasks, but fundamental limitations prevent a 10x improvement; further gains will come from retooling around existing capabilities rather than model improvements.
A detailed walkthrough of Fedora 45's build pipeline, from package source to release artifacts, explaining the tools and processes like dist-git, Koji, and Bodhi.
A reflective essay arguing that programming languages are authoring tools for platforms, using historical examples like IBM and Microsoft to illustrate how languages shape ecosystems and developer communities.
The article discusses the rise of 'vibe coding' using AI agents, its risks to code quality and developer understanding, and calls for rethinking software engineering practices to move beyond merely generating code from intent.
Vincent Weisser announces the release of over 365,000 open and agentic reinforcement learning environments for software engineering, terminal, and search agents.
A reflective essay on the lack of accountability in software engineering and LLM development, drawing from a keynote at ICST 2024 that called for responsibility akin to other engineering fields.
In the age of AI-assisted coding, the author argues that deep understanding of programming language concepts remains essential for engineering, listing key ideas from various languages that expand mental models for problem solving.
Explores the concept of technical debt specifically arising from the deployment and maintenance of AI agents, suggesting new challenges for software engineering.
The article argues against blindly adopting LLMs and provides six questions to evaluate whether an LLM is appropriate for a given workflow, emphasizing that LLMs trade determinism for flexibility and should only be used when necessary.
The author shares two years of experience building a platform with AI, identifying six recurring failure modes (Band-Aid, Assumption, Drift, Hallucination, Lack of Common Sense, Path of Least Resistance) and argues that even as models improve, these failure modes persist, becoming harder to detect.