California's pragmatic center is growing. Little-tech founders, nurses, teachers, immigrant business owners, public-safety leaders—united not by ideology, but by a desire for government that actually works. Garry's List is turning that energy into donors, volunteers, and candidates. @garrytan explains why November's ballot represents both an opportunity and a challenge for this growing movement:

how a simple llm wiki compares to gbrain second brains are getting popular fast, they're one of the main enablers for ai and agents right now. the context you give an agent is what makes it good two frameworks I've been using are the LLM Wiki and Gbrain. here's how they compare, and how to use both underneath they're the same idea, karpathy's llm wiki: you compile raw sources into linked markdown pages your agent reads, instead of redoing RAG from scratch every time both ingest your sources, build a graph out of them, and answer with citations, so the real question is what's actually different an LLM wiki is just markdown and your agent: > it reads your sources and writes linked pages > you ask a question and it reads those pages to answer > you keep it healthy with a lint pass > there's no database, just files, and one user it works well, and karpathy even points out where it starts to break down: > the synthesis drifts after a lot of updates > the context cost grows as the wiki gets big > a wrong claim can harden into fact over time gbrain is that same wiki with an engine built for those exact problems: > better retrieval, vector plus graph plus a reranker, instead of the agent reading pages > it runs on postgres, so it scales past what you could ever read yourself > a 24/7 loop enriches and fixes the wiki on its own, so there's no manual lint > every answer comes with sources and an honest note on what it doesn't know yet > it's multi-user, with access scoped per person and team when to reach for each: > use an llm wiki for smaller projects, to gather and store the context an agent will use later on. when it grows up, you can ingest it straight into gbrain > use gbrain for the consistent, shared things, a company brain or a client brain, especially once more people are involved so it's not wiki vs brain, it's the same wiki run by you on a small project, versus the same wiki run by an engine at scale for a team start simple, then move to gbrain when you outgrow the files

The worst-case scenario for the United States is becoming increasingly realistic, and I will briefly explain why. @quxiaoyin raised many valid points, and I agree with her. First of all: -China certainly does not place such strong emphasis on open source because it cares so deeply about humanism, but because it is a strategy to attract many users, gain market share, put pressure on US models, and also because the models are increasingly being trained on Huawei hardware (think of DeepSeek 4), allowing China to host the entire stack domestically. -But the underlying logic is far more important: The United States is still building too few data centers to meet future demand. @ChrisGillett wrote an outstanding analysis on this, which I shared a week ago. In short, based on SemiAnalysis data, demand is greater than what is currently being built in terms of data centers. -Even more importantly, however, the United States lacks sufficient energy and grid capacity. This is a problem that will become much more severe in the near future. China, by contrast, is addressing the issue through a massive expansion of its energy supply. Solar capacity: in 2025 alone, China installed as much solar capacity as the United States did in 10 to 15 years. China is also building 36 nuclear power plants, significantly more than the United States, and is installing them faster. -In addition, China is managing to become more independent through Huawei chips, even though the country still lags far behind NVIDIA. But here, China is betting on quantity rather than quality. In short: China is a real threat in the AI race, and the situation for the United States is becoming increasingly precarious. This is also the main reason why China is to be kept away from SOTA LLMs at all costs, so as not to jeopardize the lead under any circumstances.