*not a complaint post* Issues last night and this morning

Title: Investigating Recent Service Disruptions: A Personal Account of AI Model Performance Challenges

Introduction

In the rapidly evolving landscape of artificial intelligence, even the most robust systems can encounter unexpected issues. While much attention has been given to sensational updates or major platform changes, it’s equally valuable to share and analyze real-world experiences with AI tools, especially when troubleshooting habits are involved. Recently, I experienced some unusual behavior with custom AI models, and I wanted to document the incident to provide insight and perhaps assist others facing similar challenges.

Background on Custom AI Models

I manage several custom GPT instances that I’ve carefully crafted. These models are designed for reliability and stability, incorporating rigorous safety measures such as preflight checks, user access controls, and other safeguards. Typically, they perform consistently without issues, which makes the recent anomalies particularly noteworthy.

The Incident

Late last night and continuing into the morning, I attempted to use one of my custom GPT instances to develop a new instruction set. However, this session quickly devolved into unexpected behavior. The AI started deviating from core directives, generating responses filled with hallucinations, and seemingly experiencing a complete breakdown in coherence. It was as if the model had entered a state of total disarray.

Troubleshooting and Analysis

Curious about the scope of the problem, I performed a series of tests across multiple GPT models, including standard ChatGPT usage, as well as GPT-4 and GPT-5 variants. Throughout this testing phase, I monitored the status of OpenAI’s services via status.openai.com, which consistently reported operational status (“green”) during the incidents.

Despite the status indicators, the behavior observed suggests potential underlying issues that are not immediately reflected in service status pages. This highlights the importance of individual testing and monitoring, especially when working with customized models or complex instruction sets.

Conclusion

While encountering such disruptions can be frustrating, sharing these experiences helps build a clearer understanding of AI system resilience and potential points of failure. It also underscores the importance of thorough testing and monitoring, even when external service status appears normal.

If anyone else has faced similar challenges recently, I’d be interested to hear your experiences and insights. Navigating the reliability of AI models is an ongoing process, and collaborative knowledge can lead to better stability and performance for all users.

Stay vigilant, and happy testing!