My whole thesis is that AI products are defined by their failure cases. So here's a live simulation of the resilience patterns I ship — and a chaos console to attack them with. Kill a provider mid-stream. Trigger a rate limit. Throttle the network. Watch the answer survive.
Everything below runs in your browser — a deterministic simulation of a production LLM pipeline, no API keys involved. The interesting part isn't the model; it's the state machine wrapped around it.
Pick a question below. While the answer streams, use the chaos console to kill the provider serving it — then watch the stream fail over and resume mid-sentence.
Every token is committed as it arrives, so a failover resumes mid-sentence instead of restarting the answer.
OpenAI → Anthropic → Google, with backoff between attempts. One vendor's bad day never becomes the user's.
TTFT and throughput are measured live. When the budget blows, that's a signal — not a mystery.
When everything is down, serve the cache and say so. A labeled stale answer beats an infinite spinner.
These aren't demo tricks — they're the patterns keeping Tututor.ai responsive for 17,000+ students and teachers while the AI behind it has a bad day.
Tututor's real architecture, live. Play a scenario to watch a single request travel the system hop by hop — the AI path and the CRUD path never touch, which is the whole reason a slow model can't slow down the rest of the product. Drag any service to rearrange it.
Pick a scenario to watch it travel the system — or hover a service to see what it does.