I was thinking the same. Airflow does exactly the same thing. The only benefit here is that it’s their little workflow engine so they can get all their little edge case accounted for…
Wishful thinking has it that we rally our representatives to let OpenAI and consorts rot. The last thing people should do is bail these delusional people out. Let them have it worse then WeWorks and let’s see if their self crowned AGI can help them out of their misery
Ory Hydra was one of the few tools I remember being actually good and lightweight and useable. Tried setting up and using KeyCloak for a while, absolute nightmare
When the title said PR spam my first thought was the massive amount of scam posts released by Antrophic, OpenAI and a gazillion AI tools that solve all your problems (supposedly). Much to my surprise this was also an ad for an AI tool. Lowkey disappointing
While I think it’s a noble idea I think much more could be achieved with much smaller amounts of money. Actually zero. Regulate sugar, introduce a HIGH sugar tax. Introduce higher nicotine and alcohol tax. Introduce stricter environmental controls for poisonous materials and water and air pollution.
All these things cost essentially zero to implement, they even bring in money and all of them are credible ways to significantly reduce health problems world wide.
But eh, I’m not part of a lobbying organisation, so what do I know.
> However, once again, we are seeing a pattern whereby first, models are helpful to humans. Then, humans are helpful to models. Finally, models are largely able to do things themselves. We have seen this in cybersecurity and now the same dynamics are starting to take shape at the intersection of AI and the physical world.
It’s good they are the one seeing those things because otherwise no one else would have. Now if only seeing things would translate into getting any actual economic value out of them… instead of losing billions. But hey, who am I to do a reality check on this shameless piece of hype.
I remember that cryogenesis was deemed viable in the 80ies but essentially surface area is your enemy. Anything larger than a cat can’t be resurrected. It’s pretty bizarre really, they froze mice and microwaved them back to life.
At that scale it is almost always easier to run your own infrastructure. Like, I’m not kidding, kubernetes will handle it fairly easy. Get a DevOps engineer or a good consulting agency and run your cluster on Hetzner. This saved us insane amounts of money. No need to buy infrastructure outright but simply moving off the cloud will easily squash your bill by 50% if not more.
> The shortest path for the model is to implement it completely
Have you worked with LLMs???????????
“I disabled the test so it’s not run so now all the tests pass” is not a hypothetical it’s pretty common. LLMs frequently do shortcut learning. The reason why reviews are expensive is because you still need to do all the steps in order to understand if a shortcut is justified.
I think it’s astounding that the author can’t see that individual goodness or badness doesn’t necessarily mean you will not cause societal harm. He lists AirBnB and Facebook as successful billion dollar companies, in which likely the CEO did not have malicious intentions, but these companies are harming society at humongous scale unlike we have ever seen before (addiction and housing crisis).
Given that this post reads like a really bad contradictory hyperbole that neglects real world consequences pretty much entirely.
Or you just force people to rent out vacant flats. You have 60 days once a formal letter is received and you have to rent it to someone in that time, or you’ll be fined. That’s one way to force supply of a good that isn’t actually scarce but has artificial scarcity. Worked pretty well in some of the European metropolita and it works in New York now too. We have to stop treating housing like a simple commodity
I don’t have this particular use case right now but if anything it feels like LLMs and their distilled on prem models are starting to kill SaaS simply because it becomes more and more tenable to build a “complete software” in a short time frame. That’s freaking awesome. Good idea and love the return of the good old you buy, you own it mentality
Then again we have high rates of abandoned or unused housing as investment properties that sit idle even with uncapped markets… might as well cap the market if the effect is the same but people pay less rent
> This isn't for the person using Git. This is for someone trying to build a tool that wants to use parts of Git, which is different.
I’m going out on a limb here but I’ll say that you are over engineering for the wrong problems. I’ve done it before, I tried libgit for some use case. At the end of the day it really is much simpler to use git. If you don’t want git at runtime use something like the git-gradle-properties plugin or the likes for your build system of choice.
I really can’t think of a super duper use case where forking processes is a massive enough issue that I’d want to instead port over all of git to another language.
Git for the most parts also offers a wide variety of export formats such that you get machine readable output too. If you really really need to fiddle with its internals, git pack lets you browse through the index fairly well.
Again, my humble opinion, but you’re trying to solve the wrong problems
This reminds me of OpenAI which allows you to overcommit on prepaid tokens and then tries to force you into paying overpaid charges. Only they really can’t. You can’t make someone pay for a service they didn’t agree to with billing that doesn’t exist. I wish OpenAI best of luck with their shenanigans
I’m all for memory safety and such but honestly what’s the use case for this? Showing off agentic development? In 10+ years git has never failed on a memory overflow or else. Sometimes software is “good as is” and I’m pretty confident git classifies as such. I’ve also never really hit the limitations of git, even with teams of 20+ developers and lots of binary artefacts. You got to really stretch git limitations, in which case you might need to move away from git, and a rust rewrite will not help in any way whatsoever. So again … why?
My first thought as well, docker desktop overhead is pretty bad, would be awesome to see this land natively in DD. By my estimate this could happen, seeing as Docker has historically tried to improve performance but quickly had to accept platform limitations… would only be natural to settle DD over to containers
It’s not the only language but it’s notorious for having the worst equality operator to the point where you are not even supposed to use == because it’s that awful
“false” === true because it’s not an empty string. It’s the same now that it used to be 10 years ago. The PHP equality operator is a meme, not even a joke
In a nutshell this is what statistical learning theory says. For any dataset there is an optimum given a prediction task. It follows from entropy. As the commenter pointed out “evolution has this backed in”. There once was a research direction of evolutionary distribution estimation algorithms but basically we know nothing about evolution, and scaling ede to multidimensional data is much harder than optimising objectives and trying to squeeze the inductive bias. For all it’s worth I think much of the current AI research is focused entirely on the wrong questions. Can machines learn? Sure, inductive bias FORCES them to learn. Given basically unlimited data can computers pick appropriate inductive biases to do anything useful, “survive” if you want to call it that… probably not, at least no one has really asked these questions for a couple of decades
> Human brains do this by deep double descent-style overparameterization, and adopting a scaling strategy of extremely high-learning-rate training of extremely overparameterized models on small diverse highly-filtered datasets.
That’s an extremely steep claim with no source other than vibes. Last time I checked my biology notes, model parameters are neurons, and they cost a ton of energy to maintain. Your hypothesis is really far removed from any actual neuroscience. Also, where are those filtered datasets coming from? Do you think genetics hands them to us? There’s about zero evidence for this claim as well.
I like new concepts for ML research but please do not make up theories of human cognition when you clearly have no idea about it.
I for one am not protesting because I know that this is bullshit marketing nonsense. Look at reliability metrics of OpenAI, they’re terrible. Everyone knew a long way ahead that it’s a scam, now they’re cranking up pricing and trying to rug pull. There will be a lot of developers who will come out very well once the stock tanks. That’s my two cents