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
What if your Kubernetes cluster simply refused to run unsigned images? I spent some time experimenting with enforcing image provenance in a small Kubernetes setup using MicroK8s. The idea was simple: Only container images with valid cryptographic signatures are allowed to run in the cluster. For this I used: GitLab CI/CD (build + signing pipeline) Cosign / Sigstore (image signing) Kyverno (admissi
Most teams I have worked with have one auth test in their suite. It looks like this: test('valid token verifies', () => { const token = signSync({ sub: 'user-1', aud: 'api://backend' }, secret); const result = verify(token, options); expect(result.valid).toBe(true); }); That test is fine. It is also a smoke test, not a regression suite. It catches the case where verification is completely b
The on-call alert at 02:14 said auth_5xx_rate spiked from 0.01 to 31.4. Not a deploy window. Not a traffic spike. Just thirty-one percent of authenticated requests failing for ~four minutes, then back to baseline. The cause was a JWKS rotation on the issuer side. New keys came in. Old keys went out. Caches in our service didn't refresh fast enough. Tokens signed with the new key were rejected beca
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
On May 7, 2026 — five days from now — OpenAI removes the Realtime API beta. If you have a voice agent, transcription pipeline, or any WebSocket/WebRTC integration with gpt-4o-realtime-preview, you have a long weekend's worth of work to do, and most of it isn't the part the migration guide warns about. The loud failures are easy. The WebSocket returns 401, the WebRTC connection won't establish, you