Bram Sugarman

as the cost to produce software gets closer to zero, the value of a true network increases exponentially i guess im a boomer now, but im shocked at how few AI apps companies actually care about / understand network effects

Today we announce Thrive Eternal, a permanent capital holding company that will be concentrated in a small number of assets that we can own and steward over many decades. Across Thrive Capital and Thrive Holdings, we are building and investing through a moment of exponential change; backing emerging technologies, the infrastructure that powers them, and the businesses they can transform. Increasingly, we see a fourth category. These are assets with qualities that cannot be replicated by technology. Iconic franchises and cultural institutions rooted in tradition, identity, and shared experience. In a world shaped by abundant intelligence where creation scales and distribution fragments, we believe they will matter even more. Thrive Eternal is built on the belief that the most enduring of these assets share common characteristics: they benefit from long-term stewardship, they compound through cultural resonance, and they are enhanced by technology rather than displaced by it. Our work at Thrive has always been informed and inspired by a deep appreciation for product, brand, and the ways in which consumers form lasting relationships with the things they love. We have been building towards this for a long time. Our first partnership is expected to be with the San Francisco Giants - an institution built on more than a century of shared identity and community, and among the most iconic sports franchises in America. We have reached an agreement, subject to league approval, to acquire an ownership stake. We feel privileged by the opportunity to be long-term partners to the Giants.

“If I have your money, I’m fucked.” Jimmy Iovine on why entrepreneurs don’t thrive in large companies—and why he had to leave Apple: “I couldn't do me there. I couldn't have that compulsion, obsessiveness, and insanity [that entrepreneurs have] and not have the wheel completely. I needed the ability to make any mistake I wanted. I needed the ability to experiment. I needed the ability to just GO!”

We're looking for a founding ML engineer in Toronto. You'll have a lot of autonomy and compute to make a new genre of social software.

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.

Found the retard.