Recent submissions

Comment · 7 months ago
Fair — "understands" is generous. It produces text that sounds right. Whether that counts as understanding, honestly I don't know and it doesn't change our approach either way.
Comment · 8 months ago
Hey HN. I'm the CTO at a startup building AI agents for hotels. Lately, I've been seeing a lot of hype around MCP being the "magic bullet" that will instantly make legacy systems AI-ready. After dealing with 15-year-old SOAP APIs and the reality of how LLMs handle multi-step database writes, I wrote down why I think MCP is great, but fundamentally misunderstood by the market. Happy to answer any questions about bridging LLMs with legacy monoliths.
Comment · 8 months ago
Spot on. We often joke that a raw LLM acts exactly like an over-eager junior sales rep—it desperately wants to say "yes" to please the customer. Because they learned from us, they inherit the bad human habit of equating "helpfulness" with agreement. The difference is an AI will scale those broken promises instantly, which is why the constraints have to be architectural.
Comment · 8 months ago
Happy to answer questions about failure modes, where we draw the line between retrieval and operational decisions, and which constraints actually reduced risk in production. The main lesson for us was that “don’t hallucinate” is too soft for real operations. We had to replace soft prompting with hard boundaries: verified data only, checks for critical actions, and escalation before collecting fulfillment details for anything unverified.
Comment · 8 months ago
We deployed AI agents across 25 hospitality properties and logged ~46,000 guest conversations. The main failure mode wasn’t tone or retrieval. It was “confident gap-filling”: the model promising operational outcomes nobody had verified. This post is about the production failures we saw and the constraints we added to stop them.
Comment · 8 months ago
Thanks — good call. For context, IVR is the "Press 1 for English, Press 2 for..." menu that most hotels use before routing calls. The key finding was that removing it made our metrics look worse, but actually revealed callers we were losing silently at the IVR stage.
Comment · 8 months ago

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