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This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content What I Built A few weeks ago, I was checking my DEV.to dashboard and noticed something weird in the Traffic Sources panel.
This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content What I Built Ask most award-travel tools "what's the cheapest way to fly SFO to Tokyo in business with my points?" and you get a confident number. Ask where the number came from and things get vague.
In the volatile landscape of 2026, manual trading is a relic. The market moves too fast for human reaction times, and the data volume is too vast for unaided analysis. Building a crypto signal bot powered by AI APIs is no longer just an advantage; it is the baseline requirement for serious traders.
This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content. What I Built Ask what happens when a goalkeeper holds the ball too long. Then ask the same question about a match on 1 March 2025. The wording barely changes. The applicable rule does.
If you give an LLM access to your code, you write tools. A tool is a function plus what a model needs to call it: a name, a description, and a schema for the arguments. Then you write the same tools again. The first version uses the AI SDK's tool().
The fastest way to connect an AI agent to application data is often a generic SQL tool. Give the model a schema, accept a SQL string, run it, and return the rows. That is also where a prototype can quietly become a production security and maintenance problem.
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