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Use a templated prompt, and get ChatGPT to rewrite the prompt over time to include summaries of its aggregated knowledge. So the prompt includes the instruction to, and the conclusion fo the session, rewrite the prompt using the latest knowledge.

For example, the templated prompt includes a growing list of key entities relevant to the topic at and: for instance, key people you encountered in your day to day. As more entities are added, their detail summaries will have to be more condensed. It's possible that a model would even invent a representation to get around the limits of human language.

So it's an adaptive dynamic prompt that aims to capture the best of ChatGPT's intellignece.

We call it: smart context.

Perhaps it can even be used on dumber models to boost their perforamnce. The idea is like recursive improvement, and should converge in the limit to the optimum intelligence output of the system.

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means.

In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit

	<html>
	<head>
	<title>Windows 98</title>
	<meta charset="UTF-8">
	<link rel="stylesheet" href="https://unpkg.com/98.css">
	<style>

	/* Disable image filtering */
	img {
	image-rendering: optimizeSpeed; /* STOP SMOOTHING, GIVE ME SPEED */

	#!/bin/bash
	# requirements: apt install nmap htop aha
	# (aha is ANSI color to HTML converter)
	ncat -k -l -p 9999 -c "echo 'HTTP/1.1 200 OK\nContent-type: text/html\nconnection: close\\n'; echo q \| htop \| aha --black --line-fix"

	const Table = {};
	let Id = 0;


	class TypeClass {
	constructor() {
	const funcs = Object.getOwnPropertyNames(this.__proto__);
	for( const funcName of funcs ) {
	if (typeof this[funcName] === 'function' ) {
	const typeSignature = Table[funcName];

	// requires
	// npm i --save express cookie-parser ws
	// node built-in imports
	import fs from 'fs';
	import os from 'os';
	import path from 'path';
	import https from 'https';
	import crypto from 'crypto';

	// 3rd-party NPM imports

	// TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript
	// (c) 2008 Taku Kudo <[email protected]>
	// TinySegmenter is freely distributable under the terms of a new BSD licence.
	// For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt

	function TinySegmenter() {
	var patterns = {
	"[ä¸€äºŒä¸‰å››äº”å…ä¸ƒå…«ä¹åç™¾åƒä¸‡å„„å…†]":"M",
	"[ä¸€-é¾ ã€…ã€†ãƒµãƒ¶]":"H",
	"[ã-ã‚“]":"I",

	from hashlib import md5

	# See https://chromium.googlesource.com/chromium/src/+/master/components/bookmarks/browser/bookmark_codec.cc

	def regen_checksum(roots):
	digest = md5()

	def digest_url(url):
	digest.update(url['id'].encode('ascii'))
	digest.update(url['name'].encode('UTF-16-LE'))

	const fs = require("fs");
	const path = require("path");

	function newItem(name, url) {
	return { name, url };
	}

	const bookmarkPath = path.join(
	process.env.HOME,
	"/Library/Application Support/Google/Chrome/Default/Bookmarks"

	class Enum extends Function {
	static opts = { enumerable: true, writeable: true, configurable: false };
	constructor() {
	super();
	const names = (this.constructor+'').split('{')[1].split('}')[0].split(';').map(n => n.trim()).filter(n => n);
	names.forEach((name, i) => {
	Object.defineProperty(this.constructor, name.slice(1), { get: () => i, set: () => true, ...Enum.opts });
	});
	return this.constructor;
	}