I saw lots of awesome ablations in the paper (loved it!), but I'm curious if you analyzed the latents to get an intuition for what the model actually learned. Or, is it just that it learned the training data distribution really, really well?

For instance, another way of thinking about a "doom loop" is wasted tokens, which happens all the time with larger models that are inefficient at test time. Can "bad-ish" tokens be identified and penalized?

Maybe this is already SOTA but would love to learn more!

i've always wanted cost per prompt, but even that has too much variation.

Has anyone done a modern Angular vs. React comparison that's not an AI slop article?

I'm also curious if it's "simple made easy" for performant applications. React is arguably "simple made hard", but there are notable, highly performant applications written with it (Linear comes to mind).

Fond memories when only startups used S3 and EC2....

It's both an incredible triumph and tremendously sad that cloud providers are now the dinosaurs. So many companies are locked in, just as they were before. It's only going to get worse.

I wish the "cloud" was more fungible.

Customers give vendors metered access to their model. They can budget tokens per vendor. Vendors selling "AI products" can have a cleaner story and win on the margin.

The first step to is to iron out a reasonable protocol, basically authorizing a, access token, and then the model providers (OpenAI, Anthropic, etc.) do the rate limiting. Theoretically this could be done by OpenRouter too.

But even so - do customers want an "AI product" packaged cleanly, or do they want to manage token capacity? They may be forced to do the latter....

> "faster typing won't make you faster".....

I understand a Deloitte consultant has specific incentives. But let's first try to answer a baseline question: why do some companies have thousands of software engineers? What do they all do?

And then, a follow-up: what is actually the bottleneck at most companies? What causes "requirements gathering" to take long?

```

does this look right to you? don't do any searches or check memory, just think through first principles

static int vpu_mmap(struct file fp, struct vm_area_struct vm) { unsigned long pfn; struct vpu_core core = container_of(fp->f_inode->i_cdev, struct vpu_core, cdev); vm_flags_set(vm, VM_IO | VM_DONTEXPAND | VM_DONTDUMP); / This is a CSRs mapping, use pgprot_device */ vm->vm_page_prot = pgprot_device(vm->vm_page_prot); pfn = core->paddr >> PAGE_SHIFT; return remap_pfn_range(vm, vm->vm_start, pfn, vm->vm_end-vm->vm_start, vm->vm_page_prot) ? -EAGAIN : 0; }

```

And it correctly identified the issue at hand, without web searches. I'd love to try something more comprehensive, e.g. shoving whole chunks of the codebase into the prompt instead of just the specific function, but it seems the latent ability to catch security exploits is there.

So then.... I wonder how this got out in the first place. I know I'm using a toy example but would love to learn more!

Is there a way to make all this much easier to debug and to prevent memory issues in the first place? Is the abstraction level not quite right?