Ken

This 100MW data center in UAE is the largest solar powered datacenter in the world. There are currently 1,300 data centers in the world that are bigger than this one, but this one is the largest solar powered one. That’s 10 square kilometres of solar panels you can see. The datacenter itself is 0.02 square kilometres, so a solar powered datacenter is ~500x larger than a data center using any other form of power. A five hundred times larger site. UAE has some of the highest solar irradiance anywhere on Earth, it is an inhospitable desert. Averaging 9.7 hours of sunlight per day with average irradiance above 2,200 kWh/m^2. If you build this somewhere else, you need more solar panels because your irradiance will almost certainly be lower. Even if the world had an infinite supply of free solar panels, solar power will not be free. Anyone who has ever done major capital projects, who looks at where data centers need to be in the next 5 years and the next 10 years… we know it aint solar. Sorry. You struggle to even build a train track that’s 100 miles long and 10ft wide anywhere in the West, there is zero chance of build 100 square mile solar farms for GW compute. This is why people are talking about space compute. Deploying into space is one strategy to solve the constraints. But there are faster and more scalable strategies, that get you to mass deployment of multi GW data centers. There are strategies that also allow you to power the 10 billion robots and their newtonian actuators, that immediately follow the inference demand cycle. Step back and look at the full cycle of this industrial revolution… There will be billions of chips, but there will be trillions of actuators. This biggest part of this revolution is the embodiment cycle, and it’s big by a factor of 20 or 50x over the stuff that comes before it. There is no analogy in human history for the scale of this economy, of the demand it will place on energy and commodities. The humans own the Earth, and if you exist inside their legal system, they won’t let you turn the surface of their planet into glass. But they do want your chips and your actuators to serve their needs and desires. There is a way to do all of this, and so it will happen.

Reporting live from the minions attempting a Scientology speed run

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LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.