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!"
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!
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.
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!
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.
- 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.