mmmikael

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

import random
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import *
from keras.optimizers import SGD, RMSprop

pokev25

# Install tmux 2.8 on Centos

# install deps
yum install gcc kernel-devel make ncurses-devel

# cd src
cd /usr/local/src

# DOWNLOAD SOURCES FOR LIBEVENT AND MAKE AND INSTALL
curl -LO https://github.com/libevent/libevent/releases/download/release-2.1.8-stable/libevent-2.1.8-stable.tar.gz

popstas

#!/usr/bin/env node
// replace all UTC dates to local dates in pipe
// usage: docker logs -t container_name | docker-logs-localtime

// install:	
// curl https://gist.githubusercontent.com/popstas/ffcf282492fd78389d1df2ab7f31052a/raw/505cdf97c6a1edbb10c3b2b64e1836e0627b87a0/docker-logs-localtime > /usr/local/bin/docker-logs-localtime && chmod +x /usr/local/bin/docker-logs-localtime	

// alternative: https://github.com/HuangYingNing/docker-logs-localtime

const pad = d => (d > 9 ? d : '0' + d);

Godsing

# 1. 安装tpm: mkdir -p ~/.tmux/plugins && cd ~/.tmux/plugins && git clone https://github.com/tmux-plugins/tpm
# 2. 按 prefix + I(大写) 来安装插件
# 3. 安装 vim-obsession, 用 vundle 安装或: cd ~/.vim/bundle && git clone git://github.com/tpope/vim-obsession.git --depth 1 && vim -u NONE -c "helptags vim-obsession/doc" -c q
##### 以上需手工执行 #####

## 修改 tmux-prefix 键: ctrl+b --> ctrl+a
set -g prefix C-a
unbind C-b
bind a send-prefix
#set-option -g prefix2 `

karpathy

LLM Wiki

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.

The core idea

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.