What a strangely hostile statement on an open weight model. Running like 20 benchmark evaluations isn't trivial by itself, and even updating visuals and press statements can take a few days at a tech company. It's literally been 5 days since this "new generation" of models released. GPT-5.3(-codex) can't even be called via API, so it's impossible to test for some benchmarks.
I notice the people who endlessly praise closed-source models never actually USE open weight models, or assume their drop-in prompting methods and workflow will just work for other model families. Especially true for SWEs who used Claude Code first and now think every other model is horrible because they're ONLY used to prompting Claude. It's quite scary to see how people develop this level of worship for a proprietary product that is openly distrusting of users. I am not saying this is true or not of the parent poster, but something I notice in general.
As someone who uses GLM-4.7 a good bit, it's easily at Sonnet 4.5 tier - have not tried GLM-5 but it would be surprising if it wasn't at Opus 4.5 level given the massive parameter increase.
How up to date are you on current open weights models? After playing around with it for a few hours I find it to be nowhere near as good as Qwen3-30B-A3B. The world knowledge is severely lacking in particular.
This is a little misleading. The data they quote is based on their previous article[1], which just uses this analysis[2] provided by a VC company. Funnily enough the same VC company put a seperate clickbaitish article just a year before that one, claiming the exact opposite findings (about startups ditching SV).
I would guess a lot of these annual trends are just random fluctuations in their dataset, though to be honest I wonder how they're even trying to estimate this kind of information.
The corporate politics at Meta is the result of Zuck's own decisions. Even in big tech, Meta is (along with Amazon) rather famous for its highly political and backstabby culture.
This is because these two companies have extremely performance-review oriented cultures where results need to be proven every quarter or you're grounds for laying off.
Labs known for being innovative all share the same trait of allowing researchers to go YEARS without high impact results. But both Meta and Scale are known for being grind shops.
The US has crashed its own stock market, tanked its own government's approval ratings, and had its own business leaders speak out against the government. This definitely does not increase leverage.
I'm very skeptical on this, the paper they linked is not convincing. It says that GPT-4 is correct at predicting the experiment outcome direction 69% of the time versus 66% of the time for human forecasters. But this is a silly benchmark because people are not trusting human forecasters in the first place, that's the whole purpose for why the experiment is run. Knowing that GPT-4 is slightly better at predicting experiments than some human guessing doesn't make it a useful substitute for the actual experiment.
I think what you say is true when comparing transformers to CNNs/RNNs, but not to MLPs.
Transformers, RNNs, and CNNs are all techniques to reduce parameter count compared to a pure-MLP model. If you took a transformer model and replaced each self-attention layer with a linear layer+activation function, you'd have a pure MLP model that can model every relationship the transformer does, but can model more possible relationships as well (but at the cost of tons more parameters). MLPs are more powerful/scalable but transformers are more efficient.
Compared to MLPs, transformers save on parameter count by skimping on the number of parameters devoted to modeling the relationship between tokens. This works in language modeling, where relationships between tokens isn't that important - you can jumble up the words in this sentence and it still mostly makes sense. This doesn't work in time series, where relationships between tokens (timesteps) is the most important thing of all. The LTSF paper linked in the OP paper also mentions this same problem: https://arxiv.org/pdf/2205.13504 (see section 1)
From experience in payments/spending forecasting, I've found that deep learning generally underperform gradient-boosted tree models. Deep learning models tend to be good at learning seasonality but do not handle complex trends or shocks very well. Economic/financial data tends to have straightforward seasonality with complex trends, so deep learning tends to do quite poorly.
I do agree with this paper - all of the good deep learning time series architectures I've tried are simple extensions of MLPs or RNNs (e.g. DeepAR or N-BEATS). The transformer-based architectures I've used have been absolutely awful, especially the endless stream of transformer-based "foundational models" that are coming out these days.
I get what this piece is trying to say, but it's ignoring the fact that schools are trying to maximize learning with pupils who often don't want or care about learning (unlike with athletes or musicians who are generally learning their craft by choice).
A significant part of teaching disinterested students (not just in a grade school but in general) is about making the subject interesting enough that students will want to spend time on learning and continue to delve further in their free time.
If you're trying to teach someone web development, would you have them churn through a stack of predetermined bootcamp-style projects, or would let them try to build something they have personal interest in? I bet the latter method would turn out much better for the student in the long run.
It does not surprise me at all that performance ratings are correlated with office presence, but not for the reasons they imply. The performance evaluation system at Meta is heavily biased towards political visibility and grandstanding and these are simply much easier to achieve in a physical workplace.
I notice the people who endlessly praise closed-source models never actually USE open weight models, or assume their drop-in prompting methods and workflow will just work for other model families. Especially true for SWEs who used Claude Code first and now think every other model is horrible because they're ONLY used to prompting Claude. It's quite scary to see how people develop this level of worship for a proprietary product that is openly distrusting of users. I am not saying this is true or not of the parent poster, but something I notice in general.
As someone who uses GLM-4.7 a good bit, it's easily at Sonnet 4.5 tier - have not tried GLM-5 but it would be surprising if it wasn't at Opus 4.5 level given the massive parameter increase.