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The author accidentally developed a local retrieval tool for debugging in agentic AI software that efficiently narrows down code functions to find bugs, and is seeking more testing ideas to validate its effectiveness.
The article discusses how AI coding tools enable nearly free bug-finding in software, highlighting challenges like increased workload, trade-offs in fixing bugs, and the need for better management strategies to maintain software quality.
A talk on formal methods, using the "thing-doer" three-step process as an example, illustrating how to find bugs in systems that don't yet exist. The opening was delayed by 8 minutes due to a hardware failure.
A blog post by Antithesis detailing bugs found in several open-source Raft consensus implementations, including HashiCorp Raft and OpenRaft, emphasizing the difficulty of testing distributed systems and the need for better tooling.
Zhipu AI released its open-weight GLM-5.2 model, which reportedly matches Anthropic's Mythos in cybersecurity bug-finding tasks, narrowing the gap between Chinese and US AI capabilities.
Zhipu AI has released a new model that reportedly matches the performance of Claude Mythos in identifying security vulnerabilities.
Gergely Orosz shares his experience using Antithesis, a deterministic testing infrastructure that can run hours of testing in minutes.
Nolan Lawson argues that AI coding assistants can be used to write high-quality code slowly by employing multiple models for thorough code review and bug detection, improving codebase health rather than maximizing output speed.
Turso used the Quint formal verification tool to model SQLite's C API and discovered over 10 bugs in SQLite itself, enhancing the reliability of their SQLite rewrite.