Tag
A blog post introducing a series critiquing the overuse of acronyms in software engineering, exploring cultural and marketing reasons for acronym fatigue.
This article examines the gap between hype and reality for AI coding agents, arguing that they are effective for accelerating workflow parts but still require human oversight for architecture, debugging, and review, and questioning whether current benchmarks measure the right things.
A Twitter thread by Katelyn Lesse arguing that teams should confront the hardest part of a project head-on rather than avoiding it incrementally, illustrated by the story of Stripe's v2 accounts.
The author argues that fetching dependencies directly from VCS (as done in Go) is more secure and easier to audit than using package registries with a publish step (as in Ruby, npm, PyPI).
Argues that software engineering will always be necessary because human accountability is required to ensure AI builds safe and correct systems.
A speculative discussion questioning whether AI will render software engineering obsolete by mid 2026.
A tweet observes that new grad software engineers are relying on Fable 5 rather than understanding the codebase, highlighting a potential issue in software development practices.
A software engineer with 20 years of experience praises Fable's code-writing abilities, calling it exceptional and predicting significant change, while also noting the need for competition to OpenAI due to high costs.
An essay exploring why many developers struggle to use git properly, covering common mistakes like panic over merge conflicts, huge commits, and poor branching practices, and examining the root causes.
Rohan Paul discusses Tesla's reported AI spending budget of $800/month per software engineer, suggesting it sets a new benchmark. Chamath Palihapitiya comments that spending above $200/week is waste if Tesla is doing this.
Dan North proposes the 'Best Simple System for Now' (BSSN) approach as a middle path between quick-and-dirty and over-engineering in software development, advocating for simplicity that meets current product needs.
The author shares their experience switching from using superpowers to mattpocock/skills, compares the pros and cons of the two AI programming skill sets, emphasizes that mattpocock/skills saves more tokens and is more flexible, and discusses the evolution direction of AI-assisted software engineering.
An academic debate on whether computer science belongs to mathematics, citing a quote from computer science giant Knuth, involving discussions on discrete mathematics and the essence of algorithms.
ContextSniper is a token-efficient code memory layer for repository-level program repair using LLM agents. It reduces token usage by up to 51.5% and cost by up to 36.4% while maintaining similar resolution rates on SWE-bench Lite.
This paper introduces Hecate, the first tool that quantifies complexity across both prompt and code layers in LLM-integrated applications, using a Prompt-as-Specification formalism rooted in Hoare logic and evaluating 52 candidate metrics on open-source repositories to identify those that capture structural breadth beyond traditional code-only metrics.
This paper introduces Mastermind, a dual-loop framework that learns reusable vulnerability-reproduction strategies for repository-scale tasks, achieving an 84.5% pass rate with a frozen executor by separating strategy learning from execution.
Agent4cs is a multi-agent framework that summarizes large, hierarchical codebases in a bottom-up fashion using specialized agents for summarization, keyword extraction, and quality assurance, achieving up to 8% improvement in semantic consistency and 38% gain in keyword coverage over baselines.
Gergely Orosz argues that experienced software engineers remain valuable because they can discern good code from bad, something AI code generators still cannot reliably do.
This project adapts the engineering principles of Clean Code to JavaScript, providing bad/good comparisons for each principle to help developers write readable, reusable, and refactorable code, solving the pain of code rot in team collaboration.
A community discussion on lobste.rs asks developers for tools and techniques that effectively convey and grow mental models, contrasting them with exhaustive documentation.