Target: Crack Google L3/L4/L5 by 2026 Duration: 24 weeks (6 months intensive preparation) Daily Time Commitment: 3-4 hours minimum
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| <dict> | |
| <key>PayloadScope</key> | |
| <string>System</string> | |
| <key>PayloadContent</key> | |
| <array> | |
| <!-- PAYLOAD 1: DNS SETTINGS (Quad9 ECS) --> | |
| <dict> |
| --- | |
| title: Title | |
| subtitle: Subtitle | |
| description: | | |
| A qmd quarto template document. | |
| abstract: | | |
| Document abstract may be somewhat long. | |
| author: | |
| - name: Erik Erhardt | |
| url: https://StatAcumen.com/about |
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
A Python automation script that sorts photos into certain directories based on the date they were taken.
The script uses image metadata, specifically the date the photo was taken, to determine what directories to create and where to move the photos.
All new directories are created using os.makedirs() with exist_ok=True, so if you already have a directory named 2022 in the target directory (the place where your unsorted photos are stored), nothing will be deleted or overwritten. However, directories may be added within that directory depending on the sorting method you choose.
One machine, several git identities (personal vs work, GitHub vs GitLab, two accounts on one provider). Each repository picks its own name, email and credentials. In a hurry: Appendix B, once per rule.
A wrong identity does not fail: git commits and pushes under the wrong author. Part 1 ends with the check that catches it.
| Feature | Minimum git |
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| body { | |
| /* background: #FFFAFD; */ | |
| background-color: rgb(245, 245, 245) !important; | |
| font-size: 100% !important; | |
| line-height: 1.5 !important; | |
| font-family: 'Merriweather', Georgia, 'Times New Roman', Times, serif; | |
| margin: 35px !important; | |
| } | |
| header { |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
The expanded prompt from Samraaj's TV makeover video. Identify your TV, give your coding agent eyes through a phone camera, then build and install a home screen for the platform you actually own.
| #!/bin/bash | |
| # mkarchiso-gnome | |
| # Creates a live ISO of Arch Linux with GNOME | |
| # | |
| # This script was created to ease creating live ISOs of Arch Linux with GNOME | |
| # installed. It's highly customized to my personal preferences, thus it's | |
| # likely not very useful as-is for most people, but it could be a good | |
| # starting point for others to create their own custom live ISO. | |
| # | |
| # More about `mkarchiso`: https://wiki.archlinux.org/title/Archiso |