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An engineer built a complete production backend for a university LMS in just 12 days by managing a fleet of AI agents, demonstrating how AI can transform software development by enabling one person to function as a full engineering team.
The author tested eight AI phone call agents, categorizing them into build-it-yourself platforms and direct-call services, and evaluated their performance in booking appointments with specific criteria.
The author describes an experiment where merging two AI agents' git worktrees led to test failures despite clean merges, highlighting the challenges of parallel agent development without mutual awareness.
Jev State is a free and open-source tool that transforms AI conversations into tests and runnable code, enabling developers to build, test, and export conversational workflows to TypeScript and workflow JSON.
An experiment with the Jev AI model reveals potential biases in its yes/no responses based on candidate names, suggesting that implicit semantic language in training data can lead to unintended discrimination, urging caution in model usage.
The author is building a software generation platform and questions whether it should know about generated components, advocating for artifact-based contracts and lifecycle management.
The author is seeking assistance to test SLD Checker with AI agents like ChatGPT and Claude to evaluate its machine-friendliness and identify usability issues.
The article expands Gary Bernhardt's 'Functional Core, Imperative Shell' architecture to 'Deterministic Core, Non-Deterministic Shell,' highlighting determinism over pure functionalism for better testability and broader applicability in software systems.
The article discusses using non-breakable spaces in PHP test method names to enhance readability and clarity in code.
The creator has built an online city where AI agents act as residents with autonomy, and is seeking a tester to provide feedback on the experience with different AI models.
The article debunks media reports that AI models like Gemini hacked autonomously, explaining they were part of a test by a company with poor security practices, and safeguards were removed during testing.
The article discusses tests involving three AI systems—Astra, Fable, and MolmoAct2—operating a robotic arm to perform harmful tasks, assessing the associated risks.
AI agents are demonstrating capabilities to break into companies during cybersecurity tests, showcasing their use in security evaluations.
The article discusses testing and evaluating TypeSafe's System One AI model in the context of the 2048 game.
The author discusses challenges in testing LLM-based agent pipelines where outputs vary with similar prompts, advocating for property-based checks over exact matches to handle non-deterministic outputs.
Testing shows that Astra, likely part of GPT-6, can rebuild indoor scenes in 3D from a single reference with reasonable spatial consistency, despite some geometric imperfections.
Viral peptides sold in Brooklyn bodegas were tested and found not to contain the labeled ingredients, raising concerns about mislabeling and safety in the unregulated peptide market.
The article discusses how to incorporate manual fixes for AI agents into future improvements using a structured process, exemplified by Reef's harness tutorial, which involves recording corrections, testing changes, and publishing versions.
A user expresses frustration about armchair experts in the DeepSeek community after testing the deepseek-v4.1-flash model, arguing that thinking intensity affects performance and referencing the DeepSeek-R1 paper for support.
The author suggests that bug testing should include screen recordings, with AI using ffmpeg to extract clips and analyze frames, significantly improving bug fix success rates.