Dave Kwon

Elon Musk thinks the entire education system is built on a broken assumption. That every student should learn the same thing. At the same speed. In the same order. At the same time. Musk: “Everyone goes through from like 5th grade to 6th grade to 7th grade like it’s an assembly line. But people are not objects on an assembly line.” The model was designed for a factory economy. Standardized inputs. Predictable outputs. That economy is gone. The assembly line is gone. But the education system still runs on its logic. A student who masters algebra in two weeks sits through eight more weeks because the calendar says so. A student who struggles gets dragged forward because the schedule doesn’t wait. Neither is being served. Both are being processed. Musk: “Allow people to progress at the fastest pace that they can or are interested in, in each subject.” AI doesn’t teach a classroom. It teaches a student. One at a time. Every time. It skips what a student already knows. It finds where they’re stuck and approaches it from a different angle. It adjusts in real time. Not at the end of a semester when the damage is already done. A student obsessed with basketball learns fractions through shooting percentages. A student who builds in Minecraft learns geometry through architecture. The subject doesn’t change. The entry point does. No teacher with thirty students can do this. Not because they lack skill. Because the math doesn’t work. AI doesn’t have that constraint. Musk: “You do not need to tell your kid to play video games. They will play video games on autopilot all day. So if you can make it interactive and engaging, then you can make education far more compelling.” The brain isn’t broken. The format is. Kids learn complex systems and strategic thinking for hours voluntarily. Then walk into a classroom and can’t focus for twenty minutes. That’s not a discipline problem. That’s a design problem. Musk: “A university education is often unnecessary. You probably learn the vast majority of what you’re going to learn there in the first two years. And most of it is from your classmates.” Four years. Six figures of debt. And the real value comes from the people sitting next to you. Not the institution charging you. The degree doesn’t certify knowledge. It certifies endurance. Musk: “If the goal is to start a company, I would say no point in finishing college.” The system was built to train employees. If you’re not trying to be one, it has nothing left to offer you. Every lecture. Every textbook. Every curriculum. Now available instantly. Personalized to any learner. Adapted to any pace. The question isn’t whether the old model survives. It’s how long we keep forcing students through it while the replacement already exists.

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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.