651–660 of 1001 posts
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…
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 suspect it's because it's tailored towards VCs and other similar rich ghouls as a replacement for their overworked and underpaid secretaries
Earlier quoted context omitted.
I’ve found Sol performance to be incredibly spiky. It has tremendous IQ and can fix very difficult bugs. But it is horrible at design (both visual and system design), anything that involves thinking about users or UX, and massively overcomplicates almost all work.
I noticed the same. I wanted a simple crud webapp and suggested an insane techstack involving C#, Razor Pages, MSSQL and more. I went with my planned setup of python flask with an sqlite db which served me well for years. It's still incredibly important to have a human in the loop correcting design decisions and having good taste.
Is a very sane tech stack, you're just biased against Microsoft.
Half the world's enterprise apps run on that combination, or a minor variation of it.
Like Java it is full featured ("batteries included") but unlike Java it is relatively terse and actually pleasant to work with.
Oh, and unlike Python, it is very fast, within spitting distance of compiled Rust and C++ web apps.
OpenAI is killing it now that they are more focused. Killing projects like Sora et al have seen it go from irrelevant to level footing with Anthropic. Sol is so much better than Fable 5. Then we get Astra (yet to use it) few days after Fable 5.1 (which is very impressive). Codex is slightly better than Claude Code. Good on Sam Altman getting back to basics and turning OpenAI around.
Its funny, my experience with Sol has been awful. It really overworks problems and tracks into areas it does not need to... I just dont get how its good for some, and bad for others. It makes me suspect that the models performance is not even against problem sets and it really is just a probabilistic prediction machine. Which then makes me very skeptical of GPT-6 Astra, because if their big claim is Computer Use then…
I've had Claude do the same thing where it goes off and spends 100% of my tokens on 3 functions and an ungodly amount of tests / scaffolding that do almost nothing when I gave it an underdeveloped idea.
Artificial analysis blog https://artificialanalysis.ai/articles/benchmarking-gpt-6-as...
It loses to Muse Spark 1.3? Does anyone really believe this index reflects reality?
Muse spark 1.3 was only released yesterday.
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…
And: Is your work more difficult than finding proofs of or counterexamples to decades-old open problems in mathematics?
OpenAI is killing it now that they are more focused. Killing projects like Sora et al have seen it go from irrelevant to level footing with Anthropic. Sol is so much better than Fable 5. Then we get Astra (yet to use it) few days after Fable 5.1 (which is very impressive). Codex is slightly better than Claude Code. Good on Sam Altman getting back to basics and turning OpenAI around.
I think it mostly shows that there is no moat and the only advantage the U.S companies have over the Chinese is more compute. Qwen Max, Kimi K3, GLM 5.3 are really close to Opus/Sol/Fable/Astra and they are open weights.
For most people, the app IS the AI.
And even for its wonkiness, ChatGPT has had the best UX/UI of them all.
The way to win the AI wars in the eyes of the common folk is through the frontend, to be the Apple of AI, as it were.
For people skeptical of AGI. Consider the following: 15 years ago if you were the sole proprietor of these models, would you be able to hold a dozen remote junior engineer jobs? Maybe even more? These models could certainly pass all interviews with flying colors and even survive independently in a company role. I think sole ownership of AI 15 years ago could be worth north of $10 million per year. Just as rank-and-fi…
Cool. Being sole proprietor of AGI 15 years ago should result in monuments and religions devoted to you today. Cancer should be cured, and we should be a post-quantum interstellar fusion-powered civilization. I wish the AGI crowd would finally shut up now that it's clear no one is even trying for AGI (OpenAI revised that to "$100B in profit") What we're getting is incredible, where we're headed is incredible, but som…
Well by that metric humans aren’t intelligent either!
And how many people could’ve actually invented calculus, relativity, quantum mechanics? Are those who didn’t and couldn’t also not intelligent?
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…