thegeomaster

I'm careful to not make any assumptions here - as it might very well "be different this time" - but by the same logic, you could say it's totally obvious that we will need tons of people to build and diffuse AI systems and workflows into large orgs and SMEs. I have so many friends (in traditional industries) who could easily 5x their efficiency with AI but they have no idea and they need help. I don't see this changing nearly as quickly as AI becoming more capable.

For what it's worth, before I hurl such an accusation I always check the post in Pangram (https://pangram.com). It always detects the text at 90+% AI generated.

Notably, Pangram is very conservative, and it's not difficult to manually get an LLM generated passage of text to turn human-written. So a score of near-100% AI generated means the writer didn't do even very light editing for a large part of the text.

There are extremely good reasons to be skeptical of fully LLM-written content. Our attention spans and our online platforms were built in a time where a long, data-supported article with references was expensive to produce. The time to write it was vastly longer than the time to read it, which means you could usually rely on some good faith, baseline level of accuracy and thinking on the writer's part.

With LLM-generated content, it's very difficult to know if 5 minutes, 5 hours or 5 days went into writing of the content. On the surface, it all looks similar, but the 5 minute version usually communicates very little or very shallow ideas, makes factual errors, and is generally lacking a lot of context. It's fast food writing.

These low effort versions of content take way more to read than they take to write. And combined with the obtuseness of the writing style, it all places undue burden on the reader to figure out the underlying message, because a lot of it has been mangled by the writing process.

I think LLMs are hugely helpful for writing, but to use their proper potential one needs to use them for feedback and engage with them at a level deeper than simply "write an article about X" or "rewrite this paragraph", and the text then doesn't obviously read AI generated as a bonus - I think nobody really has a problem with this.

Probably has something to do with the fact that the blog post is not human-written.

Pangram is a very reliable tool and it does clock the text as AI generated.

If even Earendil is publishing AI generated blog posts...

Looks great, even has Linux support!

> `from opentele while import trace`

FYI, this to me points to an inference bug, bad sampling, or a non-native quant. OpenRouter is known to route requests to absolutely terrible, borked implementations. A model like DeepSeek V4 Flash shouldn't be making syntax errors like this.

Alexandr Wang on Twitter [0] mentioned open source plans:

"this is step one. bigger models are already in development with infrastructure scaling to match. private api preview open to select partners today, with plans to open-source future versions. incredibly proud of the MSL team. excited for what’s to come!"

https://x.com/alexandr_wang/status/2041909388852748717

What's the "attention window"? Are you alleging these frontier models use something like SWA? Seems highly unlikely.

And it seems they've decided to go closed-source for their largest, best models.

Tried on a few of our production prompts and got comparable speeds to what we normally get with Fireworks Serverless (Kimi K2.5), but at a better price. Rooting for you!

Thank you so much for the kind words and for the feedback!

1. Duly noted on USDC and other payment options - I have to see how easy this is do to as we're using stripe.

2. Teams and orgs are very high on the priority list and we hope to have something on this front very soon.

To keep up with our development, Discord is probably the best place: https://discord.gg/ptDRKRJpPV

Thanks for the feedback! I'm trying to fix that. The trouble is actually that changing the src of an iframe on a page pushes an entry into the history implicitly. Since we use iframes to display the contents of designs, and they can update, this results in a lot of state history pollution. Will prioritize!

Not the parent commenter, but in my testing, all recent Claudes (4.5 onward) and the Gemini 3 series have been pretty much flawless in custom tool call formats.

I actually ran this one. It measures some 700k lines of code, and seems to contain things like a full VBA implementation, complex currency and date parsing, etc. But the UI is extremely basic, doesn't seem to expose any of this advanced functionality, and and is buggy to the point of being unusable. Focus will jump around as you type, cells will reset to old values, it will stop responding to keyboard events, etc.

Article talks about all of this and references DeepSeek R1 paper[0], section 4.2 (first bullet point on PRM) on why this is much trickier to do than it appears.

[0]: https://arxiv.org/abs/2501.12948

You could think of supervised learning as learning against a known ground truth, which pretraining certainly is.

It's interesting to also compare this to getting a bare metal instance and provisioning microVMs on it using Firecracker. (Obviously something you shouldn't roll yourself in most cases.)

You can get a bare metal AX162 from Hetzner for 200 EUR/mo, with 48 cores and 128GB of RAM. For 4:1 virtual:physical oversubscription, you could run 192 guests on such a machine, yielding a cost of 200/192 = 1.04 EUR/mo, and giving each guest a bit over 1GiB of RAM. Interestingly, that's not groundbreakingly cheaper than just getting one of Hetzner's virtual machines!

You didn't include the amortized cost of a Blackwell GPU, which is an order of magnitude larger expense than electricity.

Warning: LLM-generated article, terribly difficult to follow and full of irrelevant details.

Well this was a trip down the memory lane. I built a small game on Irrlicht at the time and I remember these discussions also.

Irrlicht had its editor (irrEdit), a sound system (irrKlang), and some basic collision detection and FPS controller was built right into the engine. This was enough to get you a considerable way through a fully featured tech demo, at the very least. (I even remember Irrlicht including a beautiful first-person tech demo of traversing a large BSP-partitioned castle level.)

However, for those not afraid to stitch these additional parts from other promising libraries (or derive them from first principles, as was fashionable), OGRE offered more raw rendering prowess: a working deferred shading system (this was the heyday of deferred shading), a pop-less terrain implementation with texture splatting, and more impressive shader and rendering pipeline support, with the Cg multi-platform shading language. I remember a fairly impressive ocean surface and Fresnel refraction/reflection demos from OGRE at the time.

What an astounding achievement. In 6 years, this person has written not only a very well-designed microkernel, but a build system, UEFI bootloader, graphical shell, UI framework, and a browser engine.

The story of 10x developers among us is not a myth... if anything, it's understated.

Common sense:

- The compute requirements would be massive compared to the rest of the industry

- Not a single large open source lab has trained anything over 32B dense in the recent past

- There is considerable crosstalk between researchers at large labs; notice how all of them seem to be going in similar directions all the time. If dense models of this size actually provided benefit compared to MoE, the info would've spread like wildfire.

tok/s cannot in any way be used to estimate parameters. It's a tradeoff made at inference time. You can adjust your batch size to serve 1 user at a huge tok/s or many users at a slow tok/s.

There's no way Sonnet 4 or Opus 4 are dense models.

Are you saying that you think Sonnet 4 has 100B-200B _active_ params? And that Opus has 2T active? What data are you basing these outlandish assumptions on?