dongobread

7 ms·

1.
by dongobread 7mo ago
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
2.
by dongobread 1y ago
It is absolutely awful at writing and general knowledge. IMO coding is its greatest strength by far.
3.
by dongobread 1y ago
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.
4.
by dongobread 1y ago
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 tha
5.
by dongobread 1y ago
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 perfo
6.
by dongobread 1y ago
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.
7.
by dongobread 2y ago
The paragraph immediately after that paragraph explains that the study was based off faulty analysis (and links to the below article). https://www.vox.com/future-perfect/2019/6/4/18650969/married...
8.
by dongobread 2y ago
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 benchma
9.
by dongobread 2y ago
They definitely would and do, the vast majority of time series work is not about asset prices or beating the stock market
10.
by dongobread 2y ago
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 replace
11.
by dongobread 2y ago
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
12.
by dongobread 2y ago
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
13.
by dongobread 2y ago
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
14.
by dongobread 2y ago
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 mor
15.
by dongobread 2y ago
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.
16.
by dongobread 2y ago
I'm skeptical of that. Most western Europe countries have similarly low birth rates to Japan despite having some of the lowest working hours in the world.
17.
by dongobread 2y ago
Assuming you already know some basic linear algebra and calculus, know Python (or R), and have a decent-but-not-advanced grasp of statistics, I'd recommend working through these books. They are very readable and focus on intuitive und
18.
by dongobread 2y ago
We tried using a multi-agent system for a complex NLP-type task and we found: - Too many errors that just propogate on top of each other, if a single agent in the chain generates something even a little bit off then the whole system goes of
19.
by dongobread 2y ago
Their benchmark results seem roughly on par with Mistral 7B and Llama 3 8B, which hardly seems that great given the increase in model size. https://huggingface.co/tiiuae/falcon-11B https://huggingface.co&#x2
20.
by dongobread 2y ago
Legality aside, I think the "payment" people get from posting free knowledge on the Internet is the human connection, and the satisfaction of knowing that other people are reading and appreciating it directly. Injecting an LLM mid
21.
by dongobread 3y ago
Seems sort of misleading marketing as it seems to imply 1% of GDP = 1% of transactions. By that metric there's probably 5-10 companies who each process 5%+ of world GDP - e.g. https://en.wikipedia.org/wiki/FIS_(com
22.
by dongobread 3y ago
I don't think either of those theories is right. (1) doesn't explain the rise in corporate profits, and (2) is of course silly. Here is my theory: Consumers generally have an "acceptable" price range in their head for ea
23.
by dongobread 3y ago
This isn't apples to apples - they're taking the optimal prompting technique for their own model, then using that technique for both models. They should be comparing it against the optimal prompting technique for GPT-4.
24.
by dongobread 3y ago
There's a bunch of research on how optimism bias (which I imagine is very closely linked to overconfidence) can be evolutionarily driven, e.g. https://www.sciencedirect.com/science/article/pii/S096098221.
25.
by dongobread 3y ago
I don't think the target market for this is people looking for extremely knowledgeable LLMs that can handle deep technical tasks, given that you can't even finetune these models. I'd guess this is more of an attempt to poach
26.
by dongobread 3y ago
Clickbait unfortunately, this is median not mean. If you check the source data[1], 1m net worth is at roughly 90th percentile of US households. Also interesting in the original survey - for the median household, over >80% of their wealth
27.
by dongobread 3y ago
The idea of splitting off payment plans into its own abstraction is super interesting. Personally I would really like if the abstraction layer also was independent of the payments processor, currently it seems heavily dependent on Stripe. E
28.
by dongobread 3y ago
San Francisco is in fact the fastest declining US city by population[1] and has seen one of the biggest drops in housing prices in the country[2] [1] https://www.census.gov/library/stories/2023/05/large-c
29.
by dongobread 3y ago
As someone who's worked in time series forecasting for a while, I haven't yet found a use case for these "time series" focused deep learning models. On extremely high dimensional data (I worked at a credit card processor
30.
by dongobread 3y ago
Oh boy, a giant seed round with BCV leading. I'll stick with data.table + Clickhouse.

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