jasondavis

Anime.js Scramble Text playground

A Pen by Jason Davis on CodePen.

License.

jasondavis

<div class='demo'>
<div class='icons'>
  <div class='app-icon app-icon-1'>
    <p>&#9835;</p>
  </div>
    <div class='app-icon app-icon-2'>
    <p>&#59176;</p>
  </div>
    <div class='app-icon app-icon-3'>
    <p>&#9993;</p>

jasondavis

JavaScript GUI libraries

These libraries can be used to quickly create a GUI for configureable parameters using sliders, checkboxes, colors pickers etc

1. Tweakpane https://github.com/cocopon/tweakpane Demo: https://cocopon.github.io/tweakpane/
2. control-panel https://github.com/freeman-lab/control-panel
3. ControlKit https://github.com/automat/controlkit.js
4. guify https://github.com/colejd/guify Main site is down, here is the demo https://jons.website/projects/guify/index
5. oui https://github.com/wearekuva/oui
6. Palette.js https://github.com/lehni/palette.js

jasondavis

function getNumOfGames(campaign) {
	let numOfGames = app.plugins.plugins.dataview.api
        .pages(`"ttrpgs/${campaign}"`)
        .where(page => {
            if (page.type === 'session') {
                if (page.campaign === campaign) {
                    return true;
                }
            }
        }).length

jasondavis

/* 

This script returns a list of activity options you could do (each defined by a Markdown file), 
depending on how much time you have available.

REQUIRES:
- Obsidian: https://obsidian.md
- Obsidian Dataview plugin: https://blacksmithgu.github.io/obsidian-dataview/
- Obsidian Templater plugin: https://silentvoid13.github.io/Templater/
- this folder structure:

jasondavis

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.

jasondavis

/*****************************************************************************
*      Sample program to demonstrate how a program can display a
*      popup window which does not deactivate its parent.  This
*      can be used for things like a magnifying glass window or
*      a pseudo-menu.
*****************************************************************************/ 
 
#define STRICT
#include <windows.h>
#define COMPILE_MULTIMON_STUBS

jasondavis

"""
The most atomic way to train and inference a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

jasondavis

#NoEnv ; Recommended for performance and compatibility with future AutoHotkey releases.
SendMode Input ; Recommended for new scripts due to its superior speed and reliability.
SetWorkingDir %A_ScriptDir% ; Ensures a consistent starting directory.
#SingleInstance, force

CreateMenu(LVMenu, PMenu), CreateHotkey("PGUP", "PGDN")
, qmenu := new MenuProp()
, item := new ItemProp()
, qmenu.Create(), qmenu.Show()
return

jasondavis

<%*
  let raindrop = tp.user.raindrop;
  let collections = await raindrop.collections();
  let collection = await tp.system.suggester((item) => item.title, collections);
  let raindrops = await raindrop.raindrops(collection._id);
  let drop = await tp.system.suggester((item) => item.title, raindrops);
  drop = await raindrop.raindrop(drop._id);
  
  let title = drop.title
  let url = drop.link