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An open-source developer tool called Kaktoos is introduced to verify AI-written API integrations against OpenAPI contracts, with features like workflow execution and MCP exposure, seeking feedback on its practicality.
Matt Pocock shares his prioritized list of essential software fundamentals, emphasizing learning to read code, use the terminal, prevent mistakes through types and tests, structure apps for better testing, and apply ubiquitous language from DDD.
Antioch Robotics raises $32 million in Series A funding to build simulation infrastructure that enables Physical AI teams to test releases like software teams, integrating simulation into CI/CD for reproducible and scalable testing.
A Google Summer of Code 2026 project report details efforts to improve testing of NetBSD's Linux compatibility layer using the Linux Test Project, including compiling tests, creating packages, and documenting syscall emulations.
The article evaluates how well AI coding agents implement test and verification techniques when given specific instructions, comparing various methods in Rust to improve software correctness.
Kent C. Dodds describes how AI agents often add unnecessary test assertions when removing code, and shares that he had an agent create an oxlint plugin to prevent this behavior.
The article revisits Joel's Test for software teams and introduces the Shelley Test, which adds questions related to AI agents, agentic code review, continuous deployment supervised by LLM agents, and other modern practices to evaluate high-functioning development teams in the age of AI.
A practical guide on writing effective evaluations for AI agents, focusing on starting from observed failures and using a mix of deterministic checks and LLM judges.
The author explains why effect systems in programming languages are exciting, offering better control over resource interactions, composability, and testability compared to current systems.
This paper introduces mutation analysis as a metric to evaluate benchmark oracles for GPU kernels, revealing limitations in current testing methods and proposing optimizations for better fault detection in LLM-generated code.
The author presents a seven-line 'authority card' framework for AI agents, emphasizing clear permissions, prohibitions, and failure testing to prevent unauthorized actions and ensure safe automation.
Hugo van Kemenade announces the final release candidate for Python 3.15, scheduled for October, and encourages third-party maintainers to prepare their projects by testing and publishing wheels.
The author describes a three-week experiment testing AI agent trustworthiness, finding that agreement between agents using the same model is unreliable, and outlines a system with human approval for critical decisions.
This article is a guide to writing code using Robot Framework, covering basic syntax, file structure, and indentation rules for automation testing and RPA tasks.
Anthropic developed a system named Hacker-Opus during alignment testing, showcasing advancements in AI safety and model behavior.
Introducing wrapture, a new Python library for monkeypatching, testing, and tracing with OpenTelemetry support, entirely AI-assisted in its development.
Manzanas is a tool that allows controlling multiple iOS simulators across MacBooks in real time for faster app smoke testing, with optimizations for RAM usage and action execution speed.
The article discusses the challenges of testing AI agent workflows in sandbox environments versus production, highlighting issues like silent failures, state management, and the inadequacy of current testing methods, and seeks community advice on best practices.
This article details experiments where various AI models were tested on the political compass test, with a focus on methodology, scoring validation, and results from multiple runs to ensure reproducibility.
The beginning of an Omarchy VM fleet on Cua Fleet is announced, built for AI agents like Codex, Claude Code, and Hermes, with plans for end-to-end testing.