
Lashawn
29.7K posts

Lashawn
@nonameblitz
don't let em take ur lunch


@ElectBilzerian I want to organise a large group to go to Florida and campaign door-to-door.







LMAOOOO brilliant work @AwakenWithJP and @RealAlexJones. Alex failed BIGLY.



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.

Jada Pinkett Smith shames Will Smith for slapping Chris Rock, saying they weren’t even together at the time and she wasn’t interested in him, adding she was more concerned about his mental health and immediately apologized to Chris 👀😳

Raw GPS data shows that 44 foreign-registered cellphones were in the immediate area of Charlie Kirk on that sad day: 16 Israeli, 13 Chinese, 12 Russian, and 3 Iranian. Our system just alerted us that 4 of the 16 Israeli devices that were in Utah that day, have arrived in Islamabad last evening, at the Islamabad Serena Hotel'

Exclusive! Alex Jones Responds To President Trump’s Unprecedented Attack Against Him and Almost Every Other Original MAGA Supporter “We Hope and Pray That President Trump Wakes Up From The Mind Control Spell That The Never Trumper Neo-Cons and Netanyahu Have Put Him Under” - Alex Jones P.S. I 100% Support Melania Trump’s Call For A Congressional Investigation Into The Jeffrey Epstein Coverup!




Here, in all its glory, is the exclusive first look at the massive @Tesla Semi factory. Our @corememory crew went to Nevada to see the line come to life, as it gets ready to pump out thousands of all-electric trucks. We saw the new cab and went on a drive too. Wunderbar!









