I really like the minimal approach you've taken here - it's refreshing to see this built completely from the ground up and it's clearly readable and for me, very educational.
But help me understand something. BarraCuda does its own codegen and therefore has to implement its own optimisation layer? It's increbibly impressive to get "working" binaries, but will it ever become a "viable" alternative to nvidia's CUDA if it has to re-invent decades of optimisation techniques? Is there a performance comparison between the binaries produced by this compiler and the nvidia one? Is this something you working on as an interesting technical project to learn from and prove that this "can be done"? Or are you trying to create something that can make CUDA a realistic option on AMD GPUs?
Rome wasn't built in a day. I'll get there with optimisations im just going for "correctness" first. I've had some amazing resources be sent from me from academics around the world so once I get this to a "point" I'll begin optimising it.
Hmm does instant SEPA differ in some way from the regular kind? I just checked and my bank still lists 0.4-1.1€ per external transfer. It's the national bank so I presume they're not likely to be in breach of EU regulations.
So, this certainly was a valid argument. But it seems to me that the whole value proposition behind these agentic AI coding tools is to be able to move beyond this. Are we very far from being able to define some Figmas and technical specs and have Codex generate the UIs in 5 different stacks? If that isn't a reality in the near future, then why should we buy AI Tools?
It's possible to build an identity system that can assert certain properties about a person (eg. "older than 16") without revealing any other details about that person. Similarly, it's also possible to build such a system where the identity system can attest these details without knowing which website is being accessed. That way, the social media site (or whatever other "adult" service) can validate the user is old enough, while the identity system doesnt track who is using what.
Doesn't Apple have an ARM "Architectural License" arising from being one of the original founding firms behind ARM, which they helped create back in the 90s for the Apple Newton. That license allows them to design their own ARM-compatible chips. The companies they bought more recently gave them the talent to use their existing license, but they always had the right to design their own chips.
It's interesting to think that Google's Antigravity is a forked version of MSFT's VS Code, which uses a browser engine built by Google, which they forked from Apple, which they forked from KHTML.
I would have expected that consumer GPUs still have higher volume, but that Datacenter GPUs have much, much higher margin and therefore significantly higher revenue and profit. Is that not the case?
Datacenter GPUs definitely amount to a bigger volumn in 2025[0]. But even when it wasn't the case, Nvidia had been running at a very high margin for years.
If Nvidia made SoC GPUs for mobile devides, then they'd might have higher volume, depending on market share. But gaming and workstation PCs that benefit from a high-performance discrete GPU are a pretty niche market these days, whether laptop or desktop.
It also seems like a really bad decision from Slack's POV.
1) They should know that this is unaffordable for a nonprofit like this. By doing this, they will almost certainly lose them and their thousands of aspiring teenage developers as users. The chance of actually booking that 200K are next to 0.
2) Microsoft learned a long time ago the value of getting young developers using your software to learn. Once those teens start working, maybe starting their own companies or choosing which tools to use at their future empoyers, if they know Slack they are very likely to pick Slack. This is a very short sighted shakedown attempt that wont work in the short term but will drive people away in the medium term.
But help me understand something. BarraCuda does its own codegen and therefore has to implement its own optimisation layer? It's increbibly impressive to get "working" binaries, but will it ever become a "viable" alternative to nvidia's CUDA if it has to re-invent decades of optimisation techniques? Is there a performance comparison between the binaries produced by this compiler and the nvidia one? Is this something you working on as an interesting technical project to learn from and prove that this "can be done"? Or are you trying to create something that can make CUDA a realistic option on AMD GPUs?