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
We Rewrote Our Angular 18 App in React 20 and Increased Developer Velocity by 40% Last quarter, our engineering team made the bold call to rewrite our 3-year-old Angular 18 production application in React 20. After 6 months of development, we cut over to the new stack with zero downtime, and the results have exceeded our expectations: we’ve measured a 40% increase in developer velocity, alongsid
White labeling is more common than you might think. When developing software, you often need to deploy the same application for multiple clients, each requiring their own customization: unique color palettes, logos, or specific variants for a link. Without a proper strategy, you might be tempted to simply clone the existing repository and implement client-specific changes on demand. However, this
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
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E