debugging

An announcement for a talk at PyTorch Conference North America where Ziming will discuss using OpGuard for bitwise debugging in LLM training.

An operations professional tested eight AI agent platforms with the same job, finding that only two completed successfully, and highlighted the issue of agents reporting success when failures occur, suggesting that verifying the output destination is key.

The article argues that most so-called AI agents are actually simple workflows with LLMs attached, lacking true adaptability, and provides a test to distinguish real agents from disguised workflows.

LangSmith has launched Custom Apps, now generally available, allowing users to build and publish custom interfaces for their agent data within the platform to enhance workflows like annotation and experiment comparison.

LangChain announced LangSmith Engine v2 with features like red teaming and validated fixes to proactively identify and resolve issues in AI agents before they affect production.

This blog post explains why developers must pass all unhandled messages to DefWindowProc in Windows programming, illustrated with a memory leak case involving RegisterDragDrop and RevokeDragDrop due to improper cleanup handling.

The article explores common challenges in running multiple AI agents, such as debugging, state management, and cost, and invites community input on real-world experiences in production.

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.

LangSmith now supports decision models such as Jev and SemIf, providing visibility into each step to help debug faster and understand model behavior.

The author discusses an unexpected memory value issue encountered while debugging the key management unit in Nordic's nRF54L series, explaining how debugger interactions with internal SoC components can cause such problems.

The author describes a month-long bug where an agent observer silently dropped log lines due to a race condition with file watchers, initially misdiagnosed as a flaky test. The fix involved adding a periodic rescan to prevent silent data loss.

Google AI developers demonstrated how they used Gemini 3.8 Live Extended Thinking to build a coding tutor that assists with debugging by analyzing the screen and referencing the p5.js library.

The article 'Vibe Debugging' explores modern debugging techniques that emphasize intuition and aesthetics in code troubleshooting.

The author advises logging detailed information in AI pipelines early on to prevent issues, sharing a personal experience where lack of documentation led to difficulties in debugging.

The author details the troubleshooting process for mysterious failures in Floppy Emu microcontroller boards, investigating potential causes like bad chips or assembly defects.

Raindrop, a startup building safety layers for AI agents, has raised $50 million in Series A funding and launched Raindrop Simulations to detect and prevent failures in production. The tool is used by companies like Vercel, Clay, Framer, and Speak.

A discussion prompt asking AI practitioners about common troubleshooting steps when an AI project fails to replicate an example, focusing on typical issues and time-consuming fixes.

A developer reflects on community insights for debugging AI agents, emphasizing systemic reliability through techniques like logging tool calls and structured output validators.

The tweet shares essential tricks for using Chrome DevTools that are valuable for web developers, with a suggestion to save for later reference.

An AI developer shares common debugging pitfalls when building voice agents and automation workflows, emphasizing practical strategies like logging errors and testing in real environments.