Tag
An essay arguing that software engineers repeatedly reinvent well-solved infrastructure like auth, background jobs, rate limiting, and feature flags, trading proven solutions for custom code that they must maintain and debug.
An opinion piece arguing that platform engineering and code reuse remain valuable even with AI coding tools, because tokens cost money and reuse provides leverage. Cites Martin Fowler's similar argument about refactoring.
An essay discussing the common statistical fallacy of averaging percentiles, framed with Hamlet quotes and Hacker News examples, and arguing for empathetic communication of statistical insights.
The author describes how exe.dev decouples billing from business logic using 'billable facts' — atomic events that trigger downstream reconciliation with Stripe, avoiding brittle billing code.
Valentina Servile argues that good software design still matters even as AI writes more code, because the audience for well-designed code now includes AI agents as well as humans.
A Google Testing blog post offering guidance on how to respond to code review comments, emphasizing the importance of adding context when it helps clarify decisions and rationale.
The article argues that selling AI wrappers (simple interfaces over existing models) is easier than building AI systems that actually work reliably in production, highlighting challenges in deployment.
This article presents an experiment on refactoring a large codebase written by AI agents, demonstrating that refactoring reduces token consumption for future AI-driven changes. The author details the methodology and results showing a decrease in input tokens after refactoring.
A software engineer expresses frustration with local LLMs for agentic coding, citing issues like technical debt, ignored instructions, and excessive code generation, questioning their usefulness.
Hillel Wayne analyzes the historical and practical barriers preventing widespread adoption of formal methods in software engineering, distinguishing between formal specification and verification across code and design domains.
A developer reflects on how AI coding agents can rapidly build and modify software, but developers often lose understanding of the codebase architecture and decisions, creating a new engineering challenge.
Addy Osmani recommends using SonarSource for catching bugs and security issues on every commit, emphasizing code quality and taste.
A practical book introducing logic for programmers to improve software design, verification, and reasoning, covering topics from simplifying conditionals to formal verification and constraint solving.
This paper introduces CanItDelete, a benchmark of 200 real-world deletion-only code editing tasks, and measures how LLMs avoid deleting code. It finds that frontier models frequently leave obsolete code in place, producing passable but not merge-ready patches, and that adding deletion-focused training data improves performance.
This blog post argues that GitHub's collaboration paradigm (branches, pull requests, code reviews) is ill-suited for the modern AI-driven software development era where LLMs and agents generate code at high velocity, calling for rethinking of tools and workflows.
A podcast episode featuring Hillel Wayne discusses whether AI will drive mainstream adoption of formal verification, highlighting TLA+ use at Amazon and the challenges of writing formal specs.
A blog post reflecting on the nature of software quality, arguing that quality is about gracefully performing development and leaving the codebase better than found, and exploring how to foster or destroy quality in software products.
The article discusses the challenges and best practices for writing high-quality code with AI assistance, emphasizing the need for rigorous code review and avoiding blind trust in AI-generated output.
This article discusses strategies for migrating C/C++ projects to Rust, emphasizing incremental migration over full rewrites, with insights from Luca Palmieri and JetBrains.
ExplainBench is a new benchmark for evaluating the trustworthiness of explanations generated by coding agents, revealing that explanation quality is a distinct axis of agent evaluation and that an audit agent can automatically improve explanation reliability.