This blog was originally published on Descope. OpenAI's Custom GPTs offer a powerful way to create AI agents that can interact directly with your APIs through natural language conversations. Imagine you have a deployed FastAPI application that implements DevOps tools such as triggering CI/CD workflows, getting deployment logs, usage analytics, and other operational tasks. While integrating your AP
The cost incident that started this Three weeks after we put our chatbot into production, I opened the OpenAI billing dashboard on a Monday morning and stopped breathing for a second. One session — not one user, one session — had burned through roughly four times the daily budget for the entire app. Over a single afternoon. The session wasn't malicious. It was a test account someone forgot to lo
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Modern yazılım geliştirme ekosisteminde altyapının kod olarak yönetilmesi hız ve ölçeklenebilirlik açısından devrim yaratırken GitOps yaklaşımı bu süreci merkezi bir doğruluk kaynağına bağlamaktadır. Ancak tüm yapılandırma detaylarının tek bir platformda toplanması kritik siber güvenlik risklerini de beraberinde getirmektedir. Nesil Teknoloji olarak TSE A Sınıfı sızma testi yetkimizle endüstriyel
We’ve been running a series of experiments using ChatGPT 5.4 integrated into a website chatbot across different environments: 🌐 a main website 🎯 Goal: simulate realistic user behavior and observe how the model responds over time. ⚙️ Test setup The chatbot is designed to (no self promo here, just context): 📌 answer strictly based on website content (RAG-like approach) Over time, we intentionally
How I added LLM fallback to my OpenAI app in 10 minutes You're running a production app on OpenAI. One Tuesday morning it goes down. Your app returns 500s. You spend an hour refreshing status.openai.com. There's a better setup. Here's how to add provider fallback to any OpenAI-SDK app without rewriting anything. When you call OpenAI directly, you have one point of failure: from openai import Ope
OpenAI revenue is still the number people reach for when they want a leaderboard. But the cleaner frame is different: Anthropic appears to be building a different kind of AI business, one centered on enterprise customers, safety positioning, and less dependence on mass-market fame. That distinction matters because public discussion keeps collapsing three separate things into one scorecard: revenue
LLM Foundry: the boring stack that makes an LLM actually useful Most AI projects are built backwards. People start with the model and only later discover they needed a memory system, semantic retrieval, tool use, tests, and a fallback plan for when one provider decides to nap for no visible reason. That is the part I care about now. LLM Foundry is the workshop around an LLM — not the model itsel