How I added LLM fallback to my OpenAI app in 10 minutes

<h2> LLM API Reliability: The Reality Nobody Talks About </h2> <p>If you have run more than a few thousand LLM calls in production, you have seen the pattern: things work

<p>Your agent hits a rate limit. The fallback logic kicks in, picks an alternative model. Everything should be fine.</p> <p>Except the request still goes to the original model.

<p>For the last few years the tech industry has quietly assumed this<br /><br /> AI is powerful almost free and it will eventually pay for itself </p> <p>Turns out that story is

<p>Last week I was stitching together Serper for web + Tavily for synthesis + a YouTube transcript API just to answer one chat question. Every category was a new API contract, a

<h2> What is NeuralBridge? </h2> <p>NeuralBridge is an <strong>embedded SDK</strong> (not a gateway) that makes your AI agents resilient against LLM failures. It runs inside your

<p>GPT-5.4 shipped on March 5. Seven weeks later, on April 23, 2026, OpenAI released GPT-5.5 — and the pace alone tells you something about where this race is headed. This isn't

<p>When a Dify workflow fails after adding an OpenAI-compatible provider, it is tempting to debug the whole workflow.</p> <p>That is usually too much surface area.</p> <p>Before