AI coding tools have made it easy to go from an idea to working code. You describe what you want, send the prompt, and a few moments later you have files, components, logic, and sometimes an entire working app. That speed is great until the prompt leaves something important unsaid.
I was on-call for a service once and got paged at 2am for a Kubernetes cluster that was falling apart in three different ways simultaneously. I spent 45 minutes running kubectl commands to understand what was happening before I could even start fixing it. InfraOS AI is my answer to that problem .
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Most retrieval stacks start with a vector index, and for good reason: "find me things that look like this" is the single most common question an agent asks. But a meaningful slice of agent questions aren't about similarity at all. They're about connection — what depends on this, who touched it,...
I'm looking for people who are a bit more familiar with neural networks than me (I follow kaparthy's tutorial once, that's about it). Lets say I like using LLMs for quality/complex tool-calling to e. g.
Most teams find out what their long-context requests cost when the invoice arrives, and the gap is usually wider than the extra tokens explain. Some current models bill the whole request at a higher rate once the prompt crosses a size threshold, and a spend tool that stores one rate per token cl...