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The original was posted on /r/technology by /u/New-Ranger-8960 on 2026-06-09 16:11:52+00:00.

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The original was posted on /r/technology by /u/Federal-Block-3275 on 2026-06-09 16:02:23+00:00.

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The original was posted on /r/technology by /u/MarvelsGrantMan136 on 2026-06-09 15:55:44+00:00.

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The original was posted on /r/technology by /u/rkhunter_ on 2026-06-09 15:51:51+00:00.

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The original was posted on /r/technology by /u/MarvelsGrantMan136 on 2026-06-09 15:41:21+00:00.

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The original was posted on /r/technology by /u/joe4942 on 2026-06-09 15:25:15+00:00.

https://www.marxists.org/reference/archive/stalin/works/1924/foundations-leninism/ch04.htm

YouTube video: https://youtu.be/PvUPDSDZg1k?t=4940 (Invidious just doesn't find this video for some reasons)

Hey listen! I don't know how to feel about this look. I'm happy about the Remake, because it will be realized in full 3D, without the early technical limitations of Nintendo 64. But I guess we need to see this in action in the game, rather than this quick few seconds at non gameplay sequence.

Will this be a re-imagining like Final Fantasy 7 Remake was? Sure the overworld map will be more interesting this time without loading screens in between the areas, with the lessons learned from Breath of the Wild. More than the graphics update, I'm very curious about the gameplay changes and how the world will be layed out. And the soundtrack off course.

How far will Nintendo diverge from the original? BTW I won't buy a Switch 2, so will miss out on this unfortunately.

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they can collect returns as long as they stay in the stocks

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A new paper from Moonshot AI tackles a key bottleneck in how language models handle depth. Standard residual connections just add up the outputs of all previous layers using fixed uniform weights, and uniform addition creates a problem where hidden states grow uncontrollably as the network gets deeper. As a result, the contributions of early layers end up getting completely buried and diluted by the time the data reaches the end of the model.

This happens to be the exact same issue older recurrent neural networks faced over time before attention mechanisms came along. Naturally, they tackle the problem in a similar way using attention residuals instead of a fixed accumulation and applying a softmax attention mechanism over the outputs of preceding layers. Now, every single layer gets a learned pseudo query vector that lets it selectively pick and choose which earlier representations it actually needs to look at. This allows the network to naturally retrieve information from anywhere in its depth depending on the specific input.

However, applying this over every individual layer is called Full AttnRes and it comes with a massive catch which is that saving all those individual layer outputs creates memory and communication bottlenecks during large scale distributed training because the overhead scales linearly with the number of layers. So, in order to make the architecture actually usable they grouped the layers into chunks and summed up the outputs inside each block. The cross layer attention is then only applied over these compressed block level summaries rather than every single layer drastically reducing the memory and communication footprint.

By combining a block structure with a smart cross stage caching system and a two phase computation strategy the setup becomes a practical drop in replacement with practically zero training overhead. Their experimental results show that the performance boost holds up consistently across different model sizes.

So, yes this is selfhosting related. I am working on an n8n flow to pull in weather data so that I can have this data on a dashboard. I can't find any dockerized weather forecasting apps. Most of them connect to a personal weather station, which might be an option in the future. For the time being, this is a little project I'm working on.

Partial JSON snippet:

0	
json	
cod	"200"
message	0
cnt	40
list	
0	
dt	1780693200
main	
temp	29.4
feels_like	29.23
temp_min	29.4
temp_max	29.68
pressure	1019
sea_level	1019
grnd_level	984
humidity	42
temp_kf	-0.28
weather	

I would like to display something like this:

Current temperature: 23.25 °C
Feels like: 24.09 °C
Low / High: 23.25 °C / 23.60 °C
Humidity: 94%
Atmospheric pressure: 1023 hPa (sea level: 1023 hPa, ground level: 988 hPa)
Temperature correction factor (temp_kf): -0.35

Weather: Light rain
Weather code: 500
Short condition: Rain
Icon: 10d

So, this is for you devs or coders out there. I can produce the JSON data. I'm just not sure how to parse it to something meaningful. I'm sure Python will have to be incorporated, but unsure of how to proceed.

Maybe someone could point me in a direction to tuts, articles, or your own experience. Sorry the JSON data doesn't format correctly. Lemmy formatting doesn't seem to allow that.

President Donald Trump has accused Iran of shooting down a United States military helicopter over the Strait of Hormuz, saying that he will respond to the attack.

“I have just been informed by our Great Military that last night the Iranians shot down one of our highly sophisticated Apache Helicopters while patrolling over the Strait of Hormuz,” Trump wrote in a social media post on Tuesday.

“There were two pilots involved, both are safe and uninjured. Nevertheless, the United States must, of necessity, respond to this attack.”

President Donald Trump has accused Iran of shooting down a United States military helicopter over the Strait of Hormuz, saying that he will respond to the attack.

“I have just been informed by our Great Military that last night the Iranians shot down one of our highly sophisticated Apache Helicopters while patrolling over the Strait of Hormuz,” Trump wrote in a social media post on Tuesday.

“There were two pilots involved, both are safe and uninjured. Nevertheless, the United States must, of necessity, respond to this attack.”

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