Someone else commented that this tech might make for a good Bond movie car (maybe better suited to the Roger Moore cheesy era), but I assume nowadays they'd just use AI since it'd be so much cheaper.
> As our monorepo grows, it increasingly doesn’t fit that design space. The history of our main branch alone is almost 100 GB, and a compressed working copy is over 25 GB. We perform on the order of 25 million code checkouts per day for CI, before counting developer environments, agent sandboxes, and research jobs. Fetched naively, that would be over 600 PB of git traffic per day.
I read this and wonder what their agents have committed into the repo that people aren't looking at for _that much size_? Do they have a ton of binary assets in there or something??
We have a fairly low maximum file size limit in our repo and I aggressively pruned all binary content that really wasn't fit for source control since it compresses really poorly and can cause performance problems in git when updating a checkout over many binary file changes.
> These dyes are encapsulated in nanoparticles of polymethyl methacrylate (PMMA) — a medical-grade, glass-like plastic that is already used in medical implants and dermal fillers
Nano plastics? :p
Still pretty neat. I like the example picture in the article too
They are nearly useless. Lasers work with the direct line-of-sight, and the most recent iteration drones can fly just a few _meters_ over the terrain. And they can approach from any direction.
So lasers or turrets can work if you want to defend a military base in the middle of a desert, but not something that can be deployed in cities.
Apparently, the most efficient method so far is interceptor drones.
Saying it repeatedly doesn't make it true. I have no idea why your calling back before 2016 for a definition of AGI when its been in recent years that the leaders of some American labs have been trying to water down the definition AGI for self-enrichment purposes (i.e. IPO cash out).
Your comment prompted me to check what kinds of definitions of AGI were actually in the air back in the mid 2010s. A lot of people conflate AGI with superintelligence. I'm not accusing you of doing that, but it's a common enough mistake that I wanted to assemble these quotes as a record that can be referred to.
Here are some definitions of AGI from back then, quoted verbatim where possible, with my comments added.
Gubrud (1997), first recorded use: "By advanced artificial general intelligence, I mean AI systems that rival or surpass the human brain in complexity and speed ... " Modern LLMs completely blow us out of the water on speed and this has been true for over a year now. As for "complexity", we will forgive Gubrud, but that isn't really a meaningful concept. That said, the biggest modern LLMs are probably comparable to human brains in complexity at the level of analogizing synapse counts to parameter counts. Whether this is the right lens is up for debate.
Goertzel, Journal of AGI review (2014): AGI is a field building “general-purpose systems with intelligence comparable to that of the human mind.” Any leading model satisfies this definition easily, if only because "comparable" is a loose term. If modern LLMs can substitute for any expert in a knowledge work field and even directly teleoperate robots to perform complex tasks without prior training, that already sounds more than "comparable."
MIRI / Muehlhauser (2013): “the ability to achieve complex goals in complex environments using limited computational resources ... ” Well, I don't like this definition at all, but modern LLMs do satisfy it. They may be relatively inefficient in the training phase but they're incredibly efficient in the inference phase. (By "efficient" of course I mean information-efficient, not energy-efficient; this is surely the sense in which the definition was meant.)
Wikipedia in ~2016: “Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can.” This is the most demanding definition of the bunch. But still, are there really any remaining intellectual tasks that cutting edge models can't achieve? If you think that you have an example one - are you sure it isn't just an example of a thing that nobody has bothered to ask, or bothered put a lot of compute behind? The operative concept here is could successfully perform, without regard for compute requirements. This may sound weaselly, but it is extremely relevant. To my mind, the question of "is it AGI or not" is about raw capabilities, not efficiency. Nobody writing sci-fi in the 2000s would have cared too much about the compute requirements of their speculative tech. In fact, a common trope was something like a gigantic mainframe that could be asked one question at a time. The modern instantiation of a gigantic mainframe that can hold 10 million parallel conversations was barely dreamed of.
Yeah no. $200 was the top for me. Which doesn't super matter to me, I pretty much only use luna. I can't tell the difference between luna vs sol/astra other than those larger models do more ambitious things that I don't ask for.
Man, I just do not care to see people’s LLM conversations. Those are not the artifacts. The changes are. It’s like attaching your slack convo with your teammate as a “pr”
Really? You don't want to share those messages at the end of a long day where where you found yourself typing "STOP! WHAT THE HELL ARE YOU DOING THAT FOR? I CLEARLY TOLD YOU..."
I disagree with this. The code alone is not the artifact. How you came to the decision to write the code in a certain way is certainly an important artifact of the development process. Otherwise how would your coworkers know whether or not a particular piece of logic is still relevant?
> How you came to the decision to write the code in a certain way is certainly an important artifact of the development process. Otherwise how would your coworkers know whether or not a particular piece of logic is still relevant
Not really. Why is always more important than how. Even what is more important. Relevancy of a particular block of code is in the specs and some technical aspecs of computing/library/platform, not in the thought process of the developer.
Ok, but folks aren't even writing their own code, you think they'll go back and comment generated code? They often don't even look at the implementation that closely.
Also, while commenting is good, commit messages can stick around longer.
I agree and disagree. It all comes down to how much a human was involved with the code.
If the code is artisan to any degree (authored, edited, reviewed, etc.), it's a bit ridiculous.
It does makes sense to me if the human driver was hands-off the code because the conversation becomes the artifact.
I'm older, and have very mixed feelings about the latter case but it seems inevitable that engineers will be working at a higher level where conversational governance becomes important. We'll stop this charade of hiding AI contributions behind human identities. (Note: I don't think conversation IDs are what we need but it's a step down this path.)
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