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wholesome rule (lemmygrad.ml)
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LMAO (lemmygrad.ml)
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I've probably tripped around 20 times throughout my life. I did it mostly because I bought into the idea that psychedelics would improve my life and habits in some way. I don't believe that anymore, and I don't think psychs really changed my life at all except it gave me lots of cool memories. I kind of feel some level of nostalgia for that reality-bending feeling of being on psychs, even though the last time I took it was only a couple months ago. It's just so different from everyday experience.

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hmm... (lemmygrad.ml)
[-] Neodosa@lemmygrad.ml 2 points 2 years ago

This will definitely turn the tide of the war ๐Ÿคฏ

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chino (lemmygrad.ml)
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Many have the impression that China is a very car-polluted country with heavy traffic and wide streets occupying much of the cities. I think this impression mostly comes from the fact that much of the imagery one sees while hearing news about China is that of multi-lane streets going through cities. What you don't see in these shots however, are the enormous blocks that lie in between these streets. You can look at the map of any Chinese city, and you will see that the blocks are usually around 500x500 meters. In Soviet fashion, these blocks are big enough to have all of the services one would need during the day, as well as green space. At the same time, there are usually larger parks in the vicinity as well hosting various community activities. All of this is reflected in the fact that China has a very low motorization rate.

If you're wondering about why these wide streets exist in the first place, one has to understand that these cities are big, and these wide streets are kind of a rare sight when looking at the cities as a whole (although they are very much necessary for car traffic). I would much rather like to see wider streets at a lower cadence than frequently having to wait at shorter crossings. Here in Stockholm, crossings feel like such a headache since they're so frequent.

These green space developments are most obvious when you go just outside the city center. Here, you will usually find very high density tall housing as seen in the picture above. Having these tall buildings then leaves good amount of space for greenery.

This is a picture of Shijiazhuang, which is far from a tier-one city, and it is also far from being known as a city with greenery (it is stereotypically a polluted city). After looking around a bit in the city center, this was pretty much the most concrete-looking part I could find. You can still see, however, that each block has some amount of green space, and besides, there are big parks just outside of this frame. Shijiazhuang is the city which I will be living in during the coming year due to my upcoming exchange year.

As for public transport, I think we all have an idea of what the situation looks like.

[-] Neodosa@lemmygrad.ml 15 points 2 years ago

Some real 'we were always at war with Eurasia' shit ๐Ÿ˜‚

[-] Neodosa@lemmygrad.ml 21 points 2 years ago

I'm guessing Prigozhin just gets some more money and he'll get away without severe charges.

[-] Neodosa@lemmygrad.ml 1 points 2 years ago

First off, as someone who has programmed GPT stuff since way before ChatGPT, we don't even need to train our own model. That is overly expensive and unnecessary for our purpose. What is much smarter to do in this case is to take all of the Marxist works and let a chatbot access the contents of the works using semantic search. The way we do this is to convert the works into small chunks which we then convert into embedding vectors. When the user sends a message to the chatbot, the message and the context of the message will be converted into an embedding vector. We then run a dot-product between the message of the user and the chunks of the texts in order to find the most relevant chunks to the question which the user has asked. Then a pre-trained model can make use of the information fetched in order to answer the user's question.

Of course, training one's own model can be good if we want it to be even more accurate and familiar with the material, however a good starting point would be to use semantic search.

Neodosa

joined 3 years ago