Tools — 3 available
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Machine Learning
How LLMs Actually Work
Stanford CS229 — Yann Dubois
The foundational lecture on building large language models — tokenization, training loss, perplexity, data pipelines, and post-training alignment. From the actual transcript.
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California DMV
California Driver Handbook
CA DMV — Official Handbook
Everything you need to pass the California driver's license test — traffic laws, road signs, right of way rules, and safe driving practices. Built from the official handbook.
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AI & Privacy
What They Don't Tell You About AI
AI Privacy — Data Rights & Corporate Accountability
What AI companies actually collect, how your data is used, what your rights are, and what questions you should be asking. The things the terms of service don't explain clearly.
What this is
Most explainers simplify things until they're wrong. Most primary sources are impenetrable without a guide. These tools sit in between — built directly from the source, structured for active learning rather than passive reading.
Each tool covers one source, completely. Concepts, structure, vocabulary, and questions that make you prove you understood it.
The method
- 01 Find the primary source
- 02 Extract the actual content
- 03 Build concepts, map, glossary
- 04 Write quiz questions from source
- 05 Interrogate until it sticks