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Your LLM App Is Wasting Money: What Happens When Users Close the Tab?

You build an AI chat application. A user sends: "Explain how distributed systems work." Your server calls an LLM API and starts streaming the answer: LLM │ ├── "Distributed" ├── "systems" ├── "are" ├── ... │ ▼ Browser Everything looks great. Then the user closes the browser tab. The response disappears. But what about the LLM request? Is it still running? If your server doesn't explicitly…

Building an AI chat application involves sending requests to an LLM API. Once a user closes the browser tab, the response disappears. However, the LLM request may still be running if the server doesn't explicitly request cancellation. This can lead to incorrect results and increased costs, as the backend continues paying for unused tokens. In this article, we'll build a cancellation path for a TypeScript LLM server using Hono and Node.js.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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Where the LLM Stops: Deterministic Scoring in an AI-Assisted VAPT Pipeline

Every VAPT report ends the same way: a handful of numbers. A CVSS score. A severity label. A priority rank. Sometimes an aggregate risk score.

  • ONUS, open-source DAST platform, ensures deterministic scoring in AI-assisted VAPT pipeline
  • CVSS scores, vectors, severity levels, and priority ranks derived from deterministic formula
  • ONUS test suite validates CVSS scoring formula against known vectors for reproducibility

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