611–620 of 1001 posts
It's fun, but every new model release makes me even less interested to create cool stuff. Like, what's the point, if the next AI can do it in 5 seconds?
That hero video is interesting. A projector and speech. Maybe I'm in the minority here, but I find speech to text / text to speech (but not live audio mode) is quite comfortable and effective for coding now. The speech to text part can be frustrating if your local tts model does not have word match context for coding. Codex desktop does this remotely well but is slow. I've been experimenting with local software for m…
It is still a quite fast. In fact, I just typed this in using this app.
What is going to become of life for those of us who do not work at AI labs and are unlikely to be hired by AI labs, despite all the years we put into learning coding, math, etc, as we were told to do? Those of us who made the mistake of studying anything other than machine learning. How will we make a living? (We don't live in a world that seems likely to distribute gains widely instead of largely to the handful of a…
>as we were told to do? This is such a childish take I hear getting thrown around all the time on the internet. If you really have just been listening to whoever is telling you how to be successful, then you were always doomed to fail at some point. Like, have some self-respect and own your own life, for better or worse. >Those of us who made the mistake of studying anything other than machine learning. How will we m…
Yes, these problems are systematic. That is what I am saying. That doesn't make it any nicer.
There will be a small handful of billionaires claiming that AGI is just around the corner ad infinitum just to serve themselves at this moment in time, and capitalise from the hype.
There is no "AGI" endgame. This is shitty ass hypercapitalism in action and nothing more. I'll repeat: snake oil.
I want to take a step back: So, this is GPT-6 -- the natural number version release comparable to GPT-4 and GPT-5 from the past few years. The ARC-AGI-3 score is obviously impressive at 99.9% (we'll need to wait for more details on how they used the response API harness on GPT-6 Astra, wrt reasoning retention and compaction), but every other benchmarks seems to be a relatively modest improvement, comparable with any…
This is a very mundane release compared to GPT-4 and GPT-5. I think they probably scaled back a bit after the lukewarm response to the GPT-5 announcement. But it still very weird that there wasn't even a livestream,
I can’t help but notice how much this echoes Francois Chollet’s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed into the training distribution, and increasingly strong performance within that surface area. It seems more about coverage-driven competence. So…
They can do new tasks with in-context learning but its obviously limited by context window
I have nothing to say about the actual model, but unrelated--why do so many of these demos include people buying things autonomously? Even if I did trust an AI to get everything right, it's not like the AI can read my mind. If I was ordering food normally and without AI, I would want more control over the process--looking over the options, prices, thinking about what I really want. People don't know what they really…
I am really confused on how it can saturate ARC-AGI but still perform poorly on aggregated benchmarks: https://artificialanalysis.ai/models Perhaps if it was allowed this custom harness for all benchmarks it would similarily saturate?
The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…
Only someone who doesn't do any work of any meaningful difficulty could think these models have anything to do with AGI. Today I spent half a day trying to solve a moderately interesting software engineering problem. I was switching between GPT-5.6 Sol and Fable 5.1 to check each other's work in Cursor. And the result was gradually driving me insane. As the models struggled to find a solution that would actually work…
I want to take a step back: So, this is GPT-6 -- the natural number version release comparable to GPT-4 and GPT-5 from the past few years. The ARC-AGI-3 score is obviously impressive at 99.9% (we'll need to wait for more details on how they used the response API harness on GPT-6 Astra, wrt reasoning retention and compaction), but every other benchmarks seems to be a relatively modest improvement, comparable with any…
We've had AGI (artificial general intelligence) probably since the first release of ChatGPT, and certainly since the first agentic harnesses. They're just finally acknowledging what the term means.