Grok @Bot has made a few simple yet powerful technical decisions that I believe make it easy and enjoyable to use. 1. The best UI is none at all. The product interface is dramatically simpler than alternatives without sacrificing functionality. How is this possible? It's one of the first products designed for current frontier model capabilities and has a UI restrained enough to remain easy to use as models improve exponentially. Everyone knows how to text. 2. A thin harness for the client, a thick harness for the server. You might have noticed the app feels very fluid to use, even for a beta product. This is primarily because of everything we didn't have to build. The app harness is essentially a single tool to send messages between the client and server. The complexity moves to the server, where you can still use the coding agent harness with specialized tools as needed. This helps make the UI fast and responsive on desktop and mobile. 3. An always-on computer. Most coding agents and assistants today start fresh with every question you ask. Some of these sessions are on your local machine and others happen in the cloud. We believe strongly that cloud is the future, which is why it's the only option. Further, rather than spinning up virtual machines for every conversation, your bots connect to their own computer. This means you can still run agents on the bot's persistent filesystem. It's closer to what programmers have been doing by using Tailscale from their phones to connect to a remote computer and run an agent TUI. You get those capabilities without the hassle. 4. Your bots can use the browser. Coding agents have shown that most work on a computer can be expressed and run as code. You can ask for a task in natural language and the agent will decide to write a script to complete it. This is amazing, but there's still many tasks which can't be completed without logging into a website and clicking around the browser. Models and harnesses are now good enough to reliably handle this. The combination of writing code and using browsers means you can automate almost any task on a computer. Further, you can ask Grok Bot to record you doing the task, and then turn it into something repeatable.

🚢 Marin 535B-A23B started training this week! As usual, the whole process is open. Voyage plan: pretraining (80%) + midtraining (20%) on 18.75T tokens on 11 x GB200 NVL72 for ~3 months (2.7e24 FLOPs). Post-training will follow. Before kicking off the run, we trained a 4-rung scaling ladder from 1.6B-A61M (48B tokens) to 27.7B-A1.2B (926B tokens) to debug issues, and to make a forecast of our hero run. This is by far our biggest run, so definitely expecting the unexpected.

An excellent history of scaling laws from @jietang. In 2020, we explored the limits of sparsity in Switch Transformers by routing each token to only 1 out of 2048 experts (in retrospect, a bold choice). The model had fewer than 3B activated parameters, but 1.6T total parameters (comparable to today's frontier models). The 1.6T model achieved better C4 perplexities than the T5 models using far less compute, set a new SOTA on TriviaQA, but was dumb as bricks on reasoning tasks like SuperGLUE. The lesson was that the optimal tokens-per-parameter ratio is highly task-dependent. Or as @NShazeer had already intuited: FLOPs were intelligence; parameters were knowledge!

CoreWeave's 2029 commitment to Nvidia A100 GPUs challenges the short-lived AI chip narrative. https://bit.ly/4wkKn8t