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.”
Does anyone else have a hard time accepting these calculations? I don’t doubt the serious environmental costs of AI but some of the claims in this infographic seem far-fetched. Inference costs should be much lower than training costs. And, if a 100-word email with GPT-4 requires 0.14 kWh of energy, power AI users and developers must be consuming 100x as much. Also, what about running models like Llama-3 locally? Would love to see someone with more expertise either debunk or confirm the troubling claims in this article. It feels like someone accidentally shifted a decimal point over a few places to the right.
Not doubting the experiences of anyone else, but I have only been a daily user of this site for the past 18 months, but not once has it been down for me in the morning or evening when I reach for a dopamine hit.
Oh please. All of these time estimates, from 24 hours to ten years to 10,000 hours are completely bogus.
The “24 hours” figure is marketing copy designed to unify and differentiate a brand of technical manuals.
The person who coined the 10,000 hours rule (Anders Ericsson) rejects it as an oversimplification of an arbitrary number, noting that half of the violinists in his study fell short of that number. The ten years figure is derived from this flawed rule.
The linked article is well-written, but the comments are giving “kids these days” insecurity and mid-life crisis.
I think you are incorrectly extrapolating to the entire community based on your personal experience. You are assuming that most of the readers at this site are working in a similar professional context that you do. You are also assuming, but all of those people who work in a professional context, do not also “cook at home.”
It’s OK if you did not relate to the article. But I certainly did!
There are different types of universities. While R1 institutions are more focused on research than teaching, there are smaller liberal arts universities which revolve around the undergraduate student experience. These universities still have research expectations as part of tenure and promotion, but faculty aren’t required to crank out research publications. Teaching is hugely important at these schools, both during the hiring process and when evaluating candidates for tenure and promotion.
I have been fortunate enough to work at such a university for the past 20 years. We have a deep endowment, small class sizes, and extensive support for our faculty research projects. Undergraduates at our school are often engaged in research projects as well.
For me, this is like an academic utopia: a blend of teaching and research with a primary focus on teaching. There are many other universities like mine.
The Michigan Post article is mostly speculation and that publication doesn’t have much depth/history to it. Check out their “advertise with us” page.
This whole info dump feels like a mishmash of links to thoughtful things (Stratechery) with links to speculative articles that are clearly biased. Like how is “Influencer Magazine” breaking a story that Wall Street Journal and Kara Swisher are overlooking?
I don’t mean to be a jerk. Just really unconvinced.