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I'm an American who got married to a Mauritian this past May. We had our Hindu wedding in my wife's home country of Mauritius. My family and friends are not Hindu, and certainly had never been to the tiny island of Mauritius (it's off the coast of Madagascar in case you were wondering).

My wife and I arrived in Mauritius only two weeks before the start of the wedding to put the final preparations in place. I dreaded the prospect of answering a billion questions from my guests while trying to do this. I also dreaded them not having a way to find the answers.

So I made an AI app that acted as a guest concierge. At first I simply made a wedding db, seeded it with our info, connected an MCP server with tools to it, connected the agent to it, and gave all my guests an invite link to use it.

Scope creep took over and now I have an entire AI platform for planning weddings and being a guest concierge. The two sides are actually really complementary to each other: to make the ai answer correctly, you need a knowledge base. So the wedding planning aspect just kind of happened as a result.

There's so much more to this and I'm more than happy to discuss everything I've learned -- for example, the number one thing my guests did with the ai on the day of the ceremony was try to jailbreak it.

The demo I'm sharing here is an example wedding where you get to act as a guest and can interact with the ai concierge. I hope you find it interesting!

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I'm not a computer science guy, but I had a play with Jev and found it impressive. It allowed me to offer non-technical researchers the ability to classify arbitrary things their participants enter, without prior training or classification, at near instant speed, and negligible cost. My users may find that very helpful in the qualitative research world.

I showed them an innocuous working example of how they could classify thousands of participant entries as "mentioning Italian food" whether it mentioned "pasta", "penne", "rigatone", etc etc. Jev did this task with high confidence, and showed it could differentiate between the inverse, eg had very low confidence when I switch entry to mentioning "hamburgers". I find that a very useful tool to pass on to non-technical colleagues.

But on HN, there are lots of much more qualified people saying Jev is a con, a step backwards, or bonkers that people are impressed. Will one of them tell me plainly what software was doing this prior to Jev, and the like? I don't doubt it existed, but I never came across it. I'm interested to hear from people more expert than myself.

To be precise, software must be able to: - Classify thousands of entries with arbitrary themes or topics, even if entry doesn't mention that theme or topic directly, with high enough confidence to be useful. - Do it with thousands of entries in 0-3seconds. - Do it at negligible cost, eg sub 0.1cents

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Article URL: https://newhampshirebulletin.com/2026/09/24/as-states-seek-to-rein-in-flock-license-plate-cameras-new-hampshire-considered-a-model/

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Article URL: https://iain.rocks/blog/dont-couple-your-go-code-to-github

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