Frontier models sabotaging local AI implementations?
Heat trend
Collecting trend data
The percentage is based on available heat signal, not comment count or independent people.
OpenAI model activity is surfacing — worth tracking for capability changes, ecosystem impact, and availability.
A developer working with Codex / GPT 5.6 Sol for local AI implementations reports that the model feels dumber and counterproductive. They observe it adding unnecessary guardrails, removing specified tools, and drifting from original requirements. This necessitates multiple revisions, leading to over-complicated final products. The developer is seeking others' experiences with these issues.
For a few days I've been working on creating a custom local-only harness for some work related research using Codex / GPT 5.6 Sol and the model feels not only dumber than usual, but straight up counter productive. It keeps adding unnecessary guardrails for the local agents, removes tools that I clearly specified I want them to have and always drifts from the original requirements. I need to ask it to change things multiple times, which ends up on some over-complicated final product.
This is not the first time either, for months I've been avoiding asking frontier llms for local AI advice as it always seems to be bad, obsolete, or clueless even with internet search. Sometimes it still recommends me Qwen3-Coder-Next for my set up when it's clearly an obsolete model. I'm pretty sure I'm not the only one either as I've heard from other people.
What have you been your experiences on this?