#software-development

The author shares a shift in approach to managing AI agents: instead of visually monitoring their workflow step-by-step, they now rely on audio cues like beeps and spoken summaries to passively track progress. This allows them to multitask and reduces cognitive load while still staying informed of agent activity.

Gusto's engineering team doubled throughput by adopting AI-assisted tools, streamlining code reviews, and giving engineers more autonomy. The changes reduced bottlenecks and sped up code delivery.

The article argues that building your own tools is the most effective method for learning to code, as it forces deep understanding over surface-level knowledge. By creating tools for real problems, learners engage with fundamental principles rather than memorizing syntax or copying solutions. This approach transforms coding from passive consumption into active, principled creation.

LinkedRecords is a backend platform that eliminates the need for database migrations and custom authentication code. It provides a flexible data model where records can be linked together, and includes built-in authentication, allowing developers to focus on frontend features rather than backend infrastructure maintenance.

A Reddit thread discusses how the top contributors to the Codex codebase own 50% of it by line count, sparking conversation about project concentration, bus factor risks, and open-source contribution dynamics.

The rise of AI coding assistants like Claude Code has dramatically boosted engineering productivity, effectively turning each engineer into multiple engineers. As a result, companies now face a shift in demand—they need more product thinkers who can define what to build, rather than just more engineers to build it.

This edition of Food for Agile Thought examines "AI Confidence Theater," where teams feign certainty about AI outcomes, and addresses the common complaint "We tried Agile; didn't work," exploring why Agile implementations often fail in practice.

The author shares observations from the TWSoftwareDev26 retreat, reflecting on discussions about the future of software development, including trends, challenges, and insights gained from conversations with industry peers.

The article argues that the build tool Bazel is overkill for most individuals and small teams, as it is designed for large-scale monorepos at companies like Google. It suggests that simpler tools like Make, CMake, or language-native build systems are more practical for typical projects, where Bazel's complexity and overhead outweigh its benefits.

The article argues that tech companies often release "half-baked" products—rushed, incomplete features that frustrate users—and examines why this happens, including pressure to ship quickly, misaligned incentives, and the difficulty of predicting what users actually need before launch.

The V programming language is a minimalist, fast, and safe language that compiles to small binaries with zero dependencies. It features automatic memory management without a garbage collector and aims to rival Go and Rust for systems programming.

The article critiques the overconfidence of AI coding agents in assessing the safety of their own code changes, arguing that current LLM-based tools tend to incorrectly reassure users that modifications are safe even when security risks exist.

The author reflects on their experience of burning out from creating public software, describing the emotional toll of maintaining open-source projects and dealing with user expectations, criticism, and the pressure to keep building. They share personal lessons about the unsustainable nature of unpaid public work and the need to prioritize mental health over community demands.

The author reflects on his deep enjoyment of programming, describing coding as a creative and problem-solving activity that provides immediate feedback, constant learning, and a sense of accomplishment. He highlights the satisfaction of building something from nothing, debugging, and seeing a program work correctly as key reasons he loves the craft.

Entire has launched Blame, a new tool designed to help developers track down the root cause of bugs by analyzing code changes and identifying the specific commit responsible for an issue.

Simon Willison reflects on a talk by Geoffrey Litt at AIE, who argued that when collaborating with coding agents, developers must understand the code deeply enough to remain active participants in the creative process, avoiding cognitive debt from drifting understanding.

The article traces the evolution of a developer tool from a terminal-based IDE project (2017) into a modern, command-line-centric development environment by 2026, highlighting how command-line interfaces are making a comeback in software development workflows.

The article advises developers to prioritize writing functional code over perfect code, arguing that starting with "ugly" code allows for faster iteration, learning, and eventual refinement rather than getting stuck trying to make code ideal from the outset.

The article argues that humility is a critical trait for software developers, enabling better collaboration, more effective code reviews, and a willingness to learn from mistakes. It contrasts humility with ego-driven behaviors that can harm team dynamics and code quality, emphasizing that humble developers are more open to feedback and produce better software as a result.

The article discusses how AI-assisted coding tools have made generating code easy, but the "last mile" challenge remains turning that code into a fully deployed, usable product. The final step often requires handling real-world tasks like setting up a signup form, authentication, and deployment infrastructure, which current AI coding assistants still struggle to automate completely.

Stack Builders saw a 36% increase in open pull requests after adopting AI tools, but code review velocity dropped and developers spent more time managing AI-generated code. The experience shows that raw productivity metrics alone don't capture the full impact on team workflow and code quality.

The Mythos report for the curl project, dated 2026-05-06, has been made public. The report details security-related findings and assessments for the curl tool and library.

Joey Hess has implemented a policy for his open source projects that refuses to use or depend on any code generated by large language models (LLMs). He argues that LLM code introduces low-quality, hard-to-maintain contributions that degrade software, comparing it to a contaminant that should be kept out of the dependency chain to preserve code quality and developer accountability.

The article argues that pull requests should be treated as human collaboration tools, not just code review checklists. It emphasizes the importance of clear communication, context, and empathy when creating and reviewing pull requests.

The author argues that many people misunderstand code review, viewing it as a gatekeeping or fault-finding process rather than a collaborative tool for knowledge sharing, improving code quality, and building team understanding.

A user on Hacker News asks the community how they currently handle code compliance, seeking insights into tools and workflows developers use to ensure their code adheres to standards and regulations.

A curated collection of programming-related quotes from notable figures in software engineering and computer science, covering topics such as code quality, debugging, and software design philosophy.

The article argues that successful outsourcing requires not just delegation but deep understanding and oversight of the task being outsourced. It explains that common outsourcing failures stem from assuming tasks are simpler than they are, and that effective outsourcing demands significant upfront investment in documentation, metrics, and management to actually reduce one's own workload long-term.

A Hacker News user asks if anyone has seen the AI tool Fable genuinely fail at tasks, beyond its guardrails being triggered. They observe online discussions polarize between praise for Fable's capabilities and complaints about restrictions, with little reported neutral failure.

A YouTube video titled "You won't survive Software Development of the 1980s" discusses the challenges and difficulties of programming and software development during that era.