David

Hi. Professional C/C++ programmer here. The open-source code I can find written by Adam Back and Satoshi Nakamoto don't look remotely similar. Back's code looks typical of academic Unix programmers who also hack their code to run on Windows. Satoshi code was written by a professional Windows programmer who also wrote for Unix. Stylistically, they look nothing alike. There's not enough time between 2005 when I can find the newest Adam Back and January 2009 when Satoshi published Bitcoin/0.1 to account for the change. Both are perfectly competent programmers, but stylistically, they are completely different. The NYTimes tried to compare their English language in posts/emails. I'm compare their C/C++ language in their open-source code. The NYTimes merely points out they both use C++ as if that's another corroborating detail, when the actual code seems to disqualify Adam Back.

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.

🚨 Andrej Karpathy thinks RAG is broken. He published the replacement 2 days ago. 5,000 stars in 48 hours. It's called LLM Wiki. A pattern where your AI doesn't retrieve information from scratch every time. It builds and maintains a persistent, compounding knowledge base. Automatically. RAG re-discovers knowledge on every question. LLM Wiki compiles it once and keeps it current. Here's the difference: RAG: You ask a question. AI searches your documents. Finds fragments. Pieces them together. Forgets everything. Starts over next time. LLM Wiki: You add a source. AI reads it, extracts key information, updates entity pages, revises topic summaries, flags contradictions, strengthens the synthesis. The knowledge compounds. Every source makes the wiki smarter. Permanently. Here's how it works: → Drop a source into your raw collection. Article, paper, transcript, notes. → AI reads it, writes a summary, updates the index → Updates every relevant entity and concept page across the wiki → One source can touch 10 to 15 wiki pages simultaneously → Cross-references are built automatically → Contradictions between sources get flagged → Ask questions against the wiki. Good answers get filed back as new pages. → Your explorations compound in the knowledge base. Nothing disappears into chat history. Here's the wildest part: Karpathy's use case examples: → Personal: track goals, health, psychology. File journal entries and articles. Build a structured picture of yourself over time. → Research: read papers for months. Build a comprehensive wiki with an evolving thesis. → Reading a book: build a fan wiki as you read. Characters, themes, plot threads. All cross-referenced. → Business: feed it Slack threads, meeting transcripts, customer calls. The wiki stays current because the AI does the maintenance nobody wants to do. Think of it like this: Obsidian is the IDE. The LLM is the programmer. The wiki is the codebase. You never write the wiki yourself. You source, explore, and ask questions. The AI does all the grunt work. NotebookLM, ChatGPT file uploads, and most RAG systems re-derive knowledge on every query. This compiles it once and builds on it forever. 5,000+ stars. 1,294 forks. Published by Andrej Karpathy. 2 days ago. 100% Open Source.

Why do people still buy new cars? That $50,000 car you just paid off cost you $63,000 in total payments. Plus, it is now only worth $20,000 so it's lost $43,000 in depreciation....in 5 years. Buy used. Come on people....stop throwing your money away.