Rands in Repose

Where Did Software Go Wrong? | Jesse Li

Software is broken, but it's not because of NPM, startups, AI, or venture capitalists. A deep dive into how we think about and produce code, and how our software systems reflect the manic state of the modern world.

Software You Can Love

celebrate the art of creating software for humans

Sung Kim (@sungkim.bsky.social)

I like this quote on code review: "The idea that humans will reliably look through code to find the increasingly rare issues that automated tools miss seems like a fantasy." By Marc Brooker, AWS.

Measuring the Impact of Early-2025 AI on Experienced Open-Source...

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...

The Myth of Deterministic Software

How I've come to terms with LLM non-determinism in software and abandoned the comforting lies we've been telling ourselves about traditional software.

The Value of Source Code

Exploding Myths

On AI Coding and Its Discontents - Cal Newport

Back in January, I received a note from a senior software engineer in Silicon Valley. He described himself as an AI skeptic who became converted ... Read more

Tending Your Software Garden

Software: do you write it like a book, grow it like a plant, accrete it like a pearl, or construct it like a building? As Steve McConnell notes in Code Complete 2, there’s no shortage of software development metaphors: A confusing abundance of metaphors has grown up around software

Software Should Be Allowed to Be Weird - Ewan’s Blog

A fairly large amount of the software I write would probably die in a corporate planning meeting. There would be questions about the target audience, the bu…

Introducing Claude Fable 5.1 and Claude Mythos 5.1

Our most advanced models for coding and knowledge work. Their research capabilities also offer an early glimpse of how AI models will contribute to scientific progress.

The Last Human-Written Paper: Agent-Native Research Artifacts

Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural costs: a Storytelling Tax, where failed experiments, rejected hypotheses, and the branching exploration process are discarded to fit a linear narrative; and an Engineering Tax, where the gap between reviewer-sufficient prose and agent-sufficient specification leaves critical implementation details unwritten. Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work. We introduce the Agent-Native Research Artifact (ARA), a protocol that replaces the narrative paper with a machine-executable research package structured around four layers: scientific logic, executable code with full specifications, an exploration graph that preserves the failures compilation discards, and evidence grounding every claim in raw outputs. Three mechanisms support the ecosystem: a Live Research Manager that captures decisions and dead ends during ordinary development; an ARA Compiler that translates legacy PDFs and repos into ARAs; and an ARA-native review system that automates objective checks so human reviewers can focus on significance, novelty, and taste. On PaperBench and RE-Bench, ARA raises question-answering accuracy from 72.4% to 93.7% and reproduction success from 57.4% to 64.4%. On RE-Bench's five open-ended extension tasks, preserved failure traces in ARA accelerate progress, but can also constrain a capable agent from stepping outside the prior-run box depending on the agent's capabilities.