What are you using to work around inconsistent tool-calling on local models? (li

<!-- SC_OFF --><div class="md"><p>Been using ClaudeCode CLI with Opus 4.6 and many MCP's and honestly its addicting. </p> <p>Just tell it what to build and it does everything —

<!-- SC_OFF --><div class="md"><p>There are thoughts going on about using AI to manage workflows in company, but this might involve feeding the AI database sensitive data. </p>

<!-- SC_OFF --><div class="md"><p>I've been thinking about this as local models get better at </p> <p>tool use — shell access, file system, HTTP requests, MCP. </p> <p>If a prompt

<!-- SC_OFF --><div class="md"><p>Getting local models to make use of tools properly requires that I produce a multi-turn synthetic dataset. I find this process often tedious as I

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1tbzr64/qwen36_just_stops/"> <img alt="qwen3.6 just stops"

<!-- SC_OFF --><div class="md"><p>I’m using OpenCode with a local Qwen3.6-27B Q6_K GGUF model on an RTX 5090 with KV cache in Q8.<br /> For reference my llama.cpp build is

<!-- SC_OFF --><div class="md"><p>I build context/harness optimization tooling, so provider-side serialization quirks actually matter to me. If you're optimizing over prompts, you

<!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I taught Computer Science for 30 years in a French School of Electrical Engineering, Computer Science Department.</p> <p>I recently