Cross-Check: An Agent That Catches Outdated Website Content Before AI Assistants Repeat It to Buyers

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This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content We asked our own website 87 questions that buyers had emailed us. It fully answered only 8 of them. What it is. Cross-Check is an agent that reads our website through a Sanity Knowledge Base and compares it with two things: the questions prospects actually ask us, and the answers our sales team actually gave them. Where the site is silent, unclear or says something different, it files a finding with a suggested fix that an editor reviews in Sanity Studio.

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Hello, DEV Community! Estou chegando por aqui 👋

Estou ingressando aqui pela primeira vez e fico muito feliz em fazer parte desse espaço. Espero aprender bastante com vocês, trocar experiências e, aos poucos, contribuir com a comunidade também.

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ProgrammingDEV Community ·

Most of my traffic is coming from USA!!!

It's day 4 after launching my website, and I noticed that most of my users are coming from the USA. This is surprising because I have done more marketing in Kenya, but since it's an AI tool, it makes sense that more USA residents would be interested. I will keep an eye on this and will probably focus my marketing efforts abroad as opposed to locally. Here is the site I'm talking about: https://aiwatchdog.vercel.app/

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ProgrammingDEV Community ·

Don't Buy an All-in-One AI Subscription Without Running These Checks

The landing page looks compelling. A dozen tools, familiar model names, a fair price. But that page is marketing copy, not a description of how the tool fits into your weekly workflow. A plan can look great in a screenshot and still be a poor match for your actual daily work. That gap matters now because AI is no longer a single-task tool.

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ProgrammingDEV Community ·

Vanam by Mahaveer Varma

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass I built Vanam, an offline AI field guide for identifying birds and plants in the field. It uses the device camera, microphone, and GPS, and runs AI models directly on the device without uploading data. The results are checked through a confidence policy, written using facts from the local database, and saved to a trail. https://vanam-app-mahaveervarma.netlify.app/ https://github.com/VegirajuMahaveerVarma/Vanam I built Vanam with separate layers for the core logic, data storage, and user interface. The app f

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