this! - the reason Zig defaults to one semantic analysis thread is due to current (to be improved) limitations; some ideas for Zig also will require one compilation unit - the restricted function pointers proposal being a good example: https://github.com/ziglang/zig/issues/23367
The bun team tried to parallelize Zig's semantic analysis a while back - and it DID compile about 4x as fast... YET the builds were no longer deterministic, which is a hard requirement for lots of things. More effort will need to be spent here than naively adding a thread pool. Hopefully Zig's Sema.zig gets faster, but Rust is also super slow at compiling and needs to improve. Oh - one last thing to note, Zig has very fast debug compilation and incremental compilation, it's a real nice thing to work with that you simply can't approach in most other languages right now.
> The bun team tried to parallelize Zig's semantic analysis a while back - and it DID compile about 4x as fast...
I actually collected a build with multi-threading turned on [1]. It helped quite a bit, cutting the Zig object time from 7m49s to 4m05s. But because of the reason you mentioned (and the post was already so long!) I decided not to bring it up.
> Oh - one last thing to note, Zig has very fast debug compilation and incremental compilation, it's a real nice thing to work with that you simply can't approach in most other languages right now.
Yes, in fact I also tested just running bun run build / bun run build:release and captured traces for both! The clean release build took 3m36s for Zig versus 5m50s for Rust [2][3]. Clean debug was closer: 3m07s versus 3m27s [4][5].
I also tried incremental debug builds: adding a comment to output.zig / output.rs took 48.6s / 65.4s to rebuild. Ordinary developer builds certainly gave a different picture from CI.
It's reasonable to believe not, because OpenAI has money and is using it to get what they want. Example: OpenAI is declining the 1 million USD from the navier stokes millennium problem prize, which is essentially a "bribe" because it would now cost one million USD to go with the other side in this controversy.
I used a similar (same?) service that claimed no data was uploaded and "all processing happens on-device"... I decided to record network packets with wireshark and found out the tool DID upload a significant amount of data (about 2 megabytes) through its lifetime. I wouldn't trust these tools farther than you can throw them.
I have not, do not, and will continue to not allow my data to be used for AI training... Yet for some reason my blog website is still scraped by some AI data slurper every 10 seconds. If 99% of people don't care (or know) about their data being ingested, it doesn't justify the scraping, because there is still a (rightly infuriated) 1% who do care.
I'd imagine that OpenAI would try to stagger their announcements, rather than publishing them recently close to each other. Is this because their previous post (about navier stokes problem) was met with controversy?
I genuinely think that AI has accelerated so many different things that announcements from all companies will be incredibly common and fast.
In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.
In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.
Not only that, we're making so many tiny improvements and bug fixes that improve the experience but we don't even bother to make those announcements anymore. They don't feel "grand" enough anymore. The goal post has shifted a lot in the last 6 months.
I apologise if this sounds mean spirited, I find posts like these making grand claims without taking the time to present facts that back the magnitude of these claims simply add noise to the discussion.
You could've stopped with just the first sentence and I've would learned just as much as I did reading that comment to the end.
Do you also have a graph for more useful metrics, like number of requested features delivered? Commits, lines of code, headscratch count... there are a lot of metrics you can use, but LLMs are notorious for increasing code verbosity - which adds noise to the already imprecise metric you linked.
In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.
In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.
But I suspect you want our project management pipeline? Maybe I can just ask our coding agent to search and summarize all the features and fixes for you and then build a dashboard for you. Better yet, my email is in my profile. Email me, we'll get on a call, and I'll show you. /s
Let me ask you. What are you doing such that your velocity hasn't been greatly accelerated in the last 6 months? Can you prove that it hasn't been accelerated with facts?
My bad - I misread your original post as general company announcements, rather than feature announcements. Now my question is, if your internal staff are not requesting these features and are struggling to adapt fast enough, how useful are they? To your last question - what do you mean by velocity? Velocity is speed and direction. You may have speed, but beware the brownian motion that is stochastic predictive models, as that will give you net zero velocity. I have my goals and know what I am doing, and to be frank, it matters more that I read and understand my own code than race to a local optimum.
The current prices the largest players set for their models are not profitable, they bleed money. Eventually they will "fix" it. It could end up making their services less affordable and it could cascade other businesses and services that are dependent on them go out of business
You don’t understand what a bubble is. How good the technology is is irrelevant. That has nothing to do with an economical bubble. It’s all about massive capital misallocation driven by a frenzy of FOMO, which is specifically the case for AI investments. Economically speaking what is happening is the most obvious bubble possible, it follows everything that would be expected from a bubble where companies are chasing an ill-defined grandiose dream, based on a new technology we don’t understand and has very dubious ROI, selling some vague future utopia, allocating massive amount of capital to build infrastructure dedicated to a very early versions of that technology.
As things mature there will be a correction, ie the bubble will pop.
As long as the data centers are utilized and generating revenue, I see no reason for a correction or any misallocation of capital for the infrastructure buildout.
And today these data centers are fully utilized. OpenAI tweeted today that they may need to disable new signups for the Pro subscription in the near future due to capacity constraints.
A year from now, who knows what the situation is going to be like. It seems quite possible that robotics, self driving, research, etc. drive even more demand and revenue.
Stating with any certainty that allocating capital to build infrastructure is a mistake and that there is a correction coming seems unserious.
> A year from now, who knows what the situation is going to be like.
That cuts both ways, we are building datacenters for an immature technology that is quickly evolving. We have no idea what AI will look like in the next 5-10y. Everything that is planned to be built is based on the demand we see right now, not what it will be in the future. That means different GPUs that require different cooling systems, different power supplies, etc. NVIDIA already broke backward compatibility with their new cards, which requires a different infrastructure.
What is unserious is the opposite position: believing that we already know what will be valuable in the future and bet the entire economy on it, without any proof of positive ROI.
"Compute" is not one generic commodity. Filling your datacentres with ASICs that do nothing but compute SHA256 for Bitcoin mining was a smart move in 2015 but today that hardware is worthless e-waste.
What does the depreciation curve look like for nvidia cards purchased today? How long will it take to recoup the investment on this buildout? Will those datacenters pay for themselves before they're scrapped?
Put succinctly, if a mismatch between the timelines of the bonds in the bond markets and the revenue streams occurs, it's a bubble. This increases the risk to current and future AI investments and buildouts. If the mismatch is strong enough, contagion in other sectors/areas of the world can cause a run to security and pop the bubble.
Just because the bond markets 1/2/3/5-year bonds are bloated does that mean the bubble will pop. It just increases the risk.
You should see all the misallocation that was put towards valve-based computing. Why didn't they just all arrive at the correct answer without investing in discovery first?
Its resource allocation problem, same happened with .com bubble, lots investors put tons of money into dark fibers. Were they useless? No its very useful.
If you want check a example company from the .com days check cisco, their stock peaked at 75 then crashed hard and only managed hit that again thanks for the AI bubble.
So this means that the law is only enforced partially and only benefits big corporations which van ignore it. So yet another reason to abolish copyright.
That's like comparing apples to pears. OpenZL is not general purpose. You specify a format for data and it compresses that format. Specifying a general "could be anything" format would be interesting, but I doubt it would compress as well.
Correct me if I am wrong, but I'm pretty sure they could get away with this because nitter is not an archival software, but rather a frontend or alternative twitter proxy.
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