Notes a lazy engineer keeps to cut down on trial and error — AI agents, Obsidian plugins, and dev tooling.https://decipher99.com/https://decipher99.com/en/posts/obsidian-plugin/verified-on-a-different-pair-never-rechecked-the-original/https://decipher99.com/en/posts/obsidian-plugin/verified-on-a-different-pair-never-rechecked-the-original/I fixed a false positive in my Obsidian plugin's LLM merge-judgment prompt and even closed the GitHub issues, but never retested the case that caused the false positive in the first place. Rerun days later under the real full-vault conditions, the judgment flipped, and actually performing the merge suggested the original call was likely wrong all along.Thu, 01 Oct 2026 09:20:03 GMThttps://decipher99.com/en/posts/obsidian-plugin/subagent-missed-2-of-4-list-heavy-files/https://decipher99.com/en/posts/obsidian-plugin/subagent-missed-2-of-4-list-heavy-files/A full audit I delegated to Claude Code subagents reported 2 of 56 files as CLEAN when they weren't. An A/B test on the prompt showed that on files dense with bullet lists, 'I read it all' meant a skim rather than an item-by-item check, and a prompt that forces a checklist fixed it.Wed, 30 Sep 2026 09:14:38 GMThttps://decipher99.com/en/posts/tool-review/wrong-but-90-percent-confident/https://decipher99.com/en/posts/tool-review/wrong-but-90-percent-confident/I tried Laya, the open-source reproduction of TypeSafe's Jev, on my Obsidian plugin's duplicate-note decisions (Jev itself only through its docs, since my account isn't open yet). I measured CPU speed, 78% accuracy, and what fine-tuning would take, and the real problem turned out to be broken calibration: confidence around 0.9 on wrong answers.Tue, 29 Sep 2026 11:40:17 GMT