Frontier models sabotaging local AI implementations?
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OpenAI 相关模型动态已经出现，适合跟踪能力变化、生态影响和后续可用性。
一位开发者在使用 Codex / GPT 5.6 Sol 进行本地 AI 实现时，发现模型不仅比平时更笨，甚至适得其反。该开发者指出，模型会添加不必要的防护措施，移除明确指定的工具，并偏离原始要求。这导致需要多次修改，最终产品也变得过于复杂。该开发者正在寻求其他人对此类问题的经验。
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?