---
title: "Comment by Oglas AI on \"Jira Local LLM Integration: LM Studio per Project\" | Hashnode"
description: "Really valuable write-up, especially the part about testing what happens when the local model fails or the wrong token is used. The example involving sensitive"
url: "https://hashnode.com/posts/jira-local-llm-integration-lm-studio-per-project/6acf6079f93371d7c79c5e02/comment/6acf65cbf4f623f2474caf22"
---

[Discussion](https://hashnode.com/posts/jira-local-llm-integration-lm-studio-per-project/6acf6079f93371d7c79c5e02)

Comment [Jira Local LLM Integration: LM Studio per Project](https://hashnode.com/posts/jira-local-llm-integration-lm-studio-per-project/6acf6079f93371d7c79c5e02)

[Oglas AI](https://hashnode.com/@oglas-ai2026)

[Oct 14](https://hashnode.com/posts/jira-local-llm-integration-lm-studio-per-project/6acf6079f93371d7c79c5e02/comment/6acf65cbf4f623f2474caf22)

Really valuable write-up, especially the part about testing what happens when the local model fails or the wrong token is used. The example involving sensitive HR information highlights an important point: keeping AI processing local doesn't automatically guarantee data privacy if logs and fallback behavior aren't handled carefully. At Oglas AI, we believe practical AI solutions need to be built around security, reliability, and real business requirements not just functionality. Great to see these real-world implementation details covered so clearly!

[Mihai\_LeanZero](https://hashnode.com/@mihaileanzero)

[Oct 15](https://hashnode.com/posts/jira-local-llm-integration-lm-studio-per-project/6acf6079f93371d7c79c5e02/comment/6acf65cbf4f623f2474caf22#reply-6ad069ab6ac8991a38a94e8b)

Thanks. The logs and fallback path were the part I most wanted to get across, glad it came through.

[Yunus Emre Vurgun](https://hashnode.com/@yunusemrevurgun)

[Oct 18](https://hashnode.com/posts/jira-local-llm-integration-lm-studio-per-project/6acf6079f93371d7c79c5e02/comment/6acf65cbf4f623f2474caf22#reply-6ad4588414ee79bfe979a54b)

Thanks for the real numbers. 12 s on an idle model against a 20 s limit is tight, especially since a wrong token still fails open. A cheap fix is a small scheduled ping to the validator every few minutes. It keeps the model warm and also catches an auth error before a real Send for Review hits it.