dongobread

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.

It is absolutely awful at writing and general knowledge. IMO coding is its greatest strength by far.

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.

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.

They definitely would and do, the vast majority of time series work is not about asset prices or beating the stock market

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.

I'm not sure what would lead to you believe this. I've worked in the data science/ML space for over a decade now and I see the majority of pure analytics projects started in R, including at big tech companies I've worked at recently.

Of course, ML projects and other things that need to result in production-grade models are almost always done in Python. This is currently the most visible form of "data project" due to all the ML/AI hype, but it is far from the only data work going on.

The knowledge distillation is very interesting but generating trillions of outputs from a large teacher model seems insanely expensive. Is this really more cost efficient than just using that compute instead for training your model with more data/more epochs?

Langchain feels very much like shovelware that was created for the sole purpose of parting VCs of their money. At one point the codebase had a "prompt template" class that literally just called Python's f-string.