As configured in my dotfiles.
start new:
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start new with session name:
james b38tn1k
tsiege
/ The Technical Interview Cheat Sheet.md
Last active
July 16, 2026 19:50
This is my technical interview cheat sheet. Feel free to fork it or do whatever you want with it. PLEASE let me know if there are any errors or if anything crucial is missing. I will add more links soon.
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
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honnibal
/ theano_mlp_small.py
Last active
March 1, 2023 15:10
Stripped-down example of Multi-layer Perceptron MLP in Theano
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| """A stripped-down MLP example, using Theano. | |
| Based on the tutorial here: http://deeplearning.net/tutorial/mlp.html | |
| This example trims away some complexities, and makes it easier to see how Theano works. | |
| Design changes: | |
| * Model compiled in a distinct function, so that symbolic variables are not in run-time scope. | |
| * No classes. Network shown by chained function calls. |
karpathy
/ min-char-rnn.py
Last active
July 17, 2026 19:47
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
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| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
zlorb
/ linux_fun.md
Last active
June 21, 2025 14:20
— forked from marianposaceanu/linux_fun.md
How to have some fun using the terminal.
How to have some fun using the terminal.
- Install cowsay [0] via :
sudo apt-get install cowsay - Install fortune [1] via :
sudo apt-get install fortune - Install figlet [3] via :
sudo apt-get install figlet - Make sure you have Ruby installed via :
ruby -v - Install the lolcat [2] via : gem
gem install lolcat - (option) Add to .bash_profile and/or .bashrc
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.