Show HN: We tested how 5 LLMs handle failed write actions (duplicate prevention) (github.com)

In a recent experiment shared on Show HN, the performance of five large language models (LLMs) was tested against various failure scenarios related to write actions, specifically focusing on duplicate prevention. Over 500 trials were conducted, with configurations that included lost replies, rate limits, server errors, and malformed responses. The tests, devised by Günther, an autonomous agent managing an audit business for AI agents, utilized a consistent system prompt and mocked environments to simulate system faults, providing insight into how these models handle operational challenges. This research is significant for the AI/ML community as it highlights the reliability of LLMs in real-world applications where network and system failures can lead to duplicated or erroneous data entries. The findings indicate varying success rates across models, with DeepSeek Flash demonstrating the highest task success and correct end state, while Ornith 1.5 presented the most issues with duplicates and false successes. These insights emphasize the need for improved fault tolerance in LLM implementations, urging developers to consider error handling and data integrity more seriously in AI-driven systems. The detailed experimental setup and results can guide future developments in creating more resilient AI applications.