> The two modified satellites will test hardware planned for Starlink V3 and will attempt to scan Starship’s heat shield and transmit imagery down to operators to test methods of analyzing Starship’s heat shield readiness for return to launch site on future missions
Hope we get to see those images. Would be awesome to see a 3rd person view of starship in space.
Apple silicon has been an unmitigated success so it makes sense they’d go with Ternus. On a related note Apple needs to add Ternus to their spell check dictionary
So let’s start with a really simple decoder transformer with a single layer and single attention head, and train it to predict the next token in a sequence of text. To predict the next token you need a few things: a query for the very last token in the sequence, and a key and value for every prior token. You take your query and compute a dot product with every prior key (two large vectors in, scaler attention score out). That scaler attention score first goes through softmax, and then becomes the weight you use to compute a weighted average of your values, new value goes through the mlp, mlp output is projected into the logits from which you sample your next token (that’s the general idea at least skipped a few steps).
The last query in the sequence will be new for every new token you predict, but the set of prior keys and values stay the same, ie keys and values are reusable. The key value cache gets bigger and bigger for each new token you add to the sequence, and that’s where compression comes in. You have to store the keys and values in vram, and you’d like to keep the size down by not storing the raw uncompressed tensors. To make this work well your compression needs two things: it needs to be fast so that you can compress and decompress on the fly, and it needs to play well with softmax attention. Prior attempts at compression usually suck at one or the other, either the speed to decompress is too slow and your token/s takes a hit, or you lose important precision and the model output quality suffers. The claim in the paper is that they’ve made progress on both.
The allegations of fraud made by the people invading my home and terrorizing my friends? I’ll take those with a grain of salt.
And for the record, I’m not afraid of ICE, never said I was. ICE is racially profiling people, arresting them without cause, and deporting them without due process. I happen to be white, so that doesn’t apply in my case. It’s also coincidentally the same reason I feel safe posting a comment like this online. Free speech is being chilled in communities that ice targets, and I feel responsibility to relay what I’m hearing.
ICE also just gunned down a US citizen in the street, and that should scare everyone.
I lived my whole life in the twin cities and have a lot of friends, US citizens, who are too scared to go out to eat right now because of the ICE raids. If that wasn’t the point it is certainly the effect. I applaud Walz and Frey, and I will be ranking Frey first next time he’s up for reelection. Something tells me though he will be on to bigger things than mayor of Minneapolis.
> About the second point, I've been under the impression that because LLMs are trained on average code, they infer that the bugs and architectural flaws are desirable
This is really only true about base models that haven’t undergone post training. The big difference between ChatGPT and GPT3 was OpenAI’s instruct fine tuning. Out of the box, language models behave how you describe. Ask them a question and half the time they generate a list of questions instead of an answer. The primary goal of post training is to coerce the model into a state in which it’s more likely to output things as if it were a helpful assistant. The simplest version is text at the start of your context window like: “the following is code was written by a meticulous senior engineer”. After a prompt like that the most likely next tokens will never be the models imitation of a sloppy code. Instruct fine tuning does the same thing but as permanent modifications to the weights of the model.
Humans don’t learn to write messy complex code. Messy, complex code is the default, writing clean code takes skill.
You’re assuming the LLM produces extra complexity because it’s mimicking human code. I think it’s more likely that LLMs output complex code because it requires less thought and planning, and LLMs are still bad at planning.
Image generation and image input are two totally different things. This is about feeding text into LLMs as images, it has nothing to do with image generation.
I don't know if this game was vibe coded, but it certainly could have been. Most notable thing about this post is probably that vibe coded games are good enough now to fool HN.
Same. There are really only two features I care about in a phone: a high refresh rates and weight. At 165 grams the iPhone air is by far the lightest 120hz phone apple has ever made. Second place is the iPhone 15 Pro at 187 grams. Getting ready to ditch my 15 pro.
I would apply that logic to anyone responsible for honest mistakes. Mistakes are a valuable opportunity for learning, and learning doesn’t thrive in a culture of fear.
The same system should definitely exist in law enforcement, to the extent that the mistakes are made in good faith. I would argue that a good percentage of the “mistakes” made by police in the US are malicious in nature, akin to ATC intentionally putting planes in danger. Our law enforcement culture in the US is so rotten that mistakes are neither punished nor understood.
To your point that ATC is notoriously awful for not firing people, I would say their safety record speaks for itself. The impulse for punitive vengeance is what puts lives at risk, not ATC's safety culture.
If the goal is to improve safety firing people is almost always the wrong response. That juvenile preoccupation with punishment only serves to create a culture where mistakes are hidden rather than understood.
If the arm based Macs are anything like the iPad Pro they will be the opposite of sluggish. Apple will likely keep Intel based machines around for software compatibility reasons, but that has nothing to do with performance.
Hope we get to see those images. Would be awesome to see a 3rd person view of starship in space.