I preferred o3 for coding and analysis tasks, but appreciated 4o as a “companion model” for brainstorming creative ideas while taking long walks. Wasn’t crazy about the sycophancy but it was a decent conceptual field for playing with ideas. Steve Jobs once described the PC as a “bicycle for the mind.” This is how I feel when using models like 4o for meandering reflection and speculation.
As a professor who teaches courses on media literacy and artificial intelligence, I am obsessed with our relationship with AI interfaces. I got tired of the default sycophantic glaze on ChatGPT and the tedious ritual of manually telling it to be more direct in every new chat. I'm also weary of the endless user engagement questions that GPT asks with every interaction.
As a weekend vibe coding project, I implemented this tool to fix the problem for myself. It’s a lightweight Chrome extension that lets you define and automatically inject a custom prompt to control the AI's personality.
This extension prepends the instructions to the user's message before each fetch request is sent. It's a "simulated" system prompt, but it works surprisingly well and I think this is more effective than relying on custom instructions.
This can also be adjusted mid-conversation without needing to launch a new conversation. It's 100% local. Nothing is ever sent to any server but OpenAI's. It collects zero data.
It's a simple tool for a simple problem. I'm sharing it here because I figure others might find it useful too. I'd love to hear any thoughts or feedback you have.
Really enjoyed this piece. Learned quite a bit about the value of test-time compute and the way that reinforcement learning can be used to train reasoning into a model.
My jaw dropped a tiny bit when I read that “the model discovers on its own the most optimal Chain-of-Thought-like behavior, including advanced reasoning capabilities such as self-reflection and self-verification.”