zziuni

# source : http://code.google.com/p/natvpn/source/browse/trunk/stun_server_list
# A list of available STUN server.

stun.l.google.com:19302
stun1.l.google.com:19302
stun2.l.google.com:19302
stun3.l.google.com:19302
stun4.l.google.com:19302
stun01.sipphone.com
stun.ekiga.net

ikegami-yukino

# -*- coding: utf-8 -*-
import unicodedata

for unicode_id in xrange(65536):
    char = unichr(unicode_id)
    normalized_char = unicodedata.normalize('NFKC', char)
    if char != normalized_char:
        if len(normalized_char) == 1:
            print u'[%d] %s -> [%d] %s' % (unicode_id, char, ord(normalized_char), normalized_char)
        else:

object-kazu

#compdef cordova
#autoload

typeset -A opt_args
local context state line

_cordova() {
  local context state line curcontext="$curcontext"

  if (( CURRENT > 2 )); then

CamDavidsonPilon

{
  "lines.linewidth": 2.0,
  "examples.download": true,
  "patch.linewidth": 0.5,
  "legend.fancybox": true,
  "axes.color_cycle": [
    "#30a2da",
    "#fc4f30",
    "#e5ae38",
    "#6d904f",

voluntas

https://gist.github.com/voluntas/a1d39c2b2a4392956ff69732dc493e39

peterjliu

Shortlink: goo.gl/wSuuS9

Supplementary Materials for Generating Wikipedia by Summarizing Long Sequences

The github repository can be found at https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikisum

Maharshi-Pandya

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

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.

k16shikano

name	cognitive-rhythm-writing
description	説明的な文章に緩急を設計するための規範。緩急を装飾ではなく認知モードの切替（観察→逡巡→断定→再観察）と未回収の緊張の管理として扱い、文の拍、段落の密度波形、節の入り方、緩みと駄文の判別、執筆後の機械的な点検手順を定める。読み物として読ませたい章・記事・解説文を生成するとき、または「密度はあるが平坦でおもしろくない」文章を診断・修正するときに使用する。

認知リズムを生むための日本語ライティング規範

密度の高い文章が退屈になるのは、情報が多いからではなく、全文が同じ認知モードで書かれているからである。 この規範は、読者の認知モード（観察する、迷う、確信する、確かめ直す）を意図的に切り替え、常に「続きを読む理由」を維持することで、読み進める推進力を作る。