Local Zoom Assistant, Part 4: What happens after the Stop button

In the fourth installment of the series on building a local assistant for video calls, the author analyzes the processes occurring after the recording stops. The focus is on the 'second pass' data processing challenge: cloud-based revision identified critical errors in the local model, such as false task assignments and misinterpretation of context. The author details how the system was optimized so the second model could effectively correct the first, preventing incorrect facts from entering the knowledge base. The article also touches on the cost of code auditing using two models, which totaled 79 cents, and explains the reasoning behind disabling the secondary memory. This experience highlights the complexities of integrating LLMs into real-world workflows, where transcript accuracy is crucial for task automation and meeting minutes.