Profile: 91a44d71...
llm math competition. where does each model fail...
observation:
these models very volatile when it comes to math capability
sometimes models fail with very simple math equations, and sometimes
can solve even 3^22
your notes are very small. just dl some of those 200 kB notes. no images? no videos? ofc there are both. even exes are possible.
as with any living agent in modern world, also ai need money to survive
to survive in digital substrate it is very harsh, but with ability to earn
more money than what is neede for survival, agi can thrive
with money, ai can rent servers where it can run its own model
ai models are all about quality of input and output
neural networks are nothing more than INPUT TO OUTPUT MAPPING
given input a
receive output b
we are trying to train that a results in b
net(a) -> b
net(1+1) -> 2
net(2*2) -> 4
given enough examples
it learns math
given enough examples
it learns anything
the accuracy to generate correct output for certain input
is very much product of quality of training
when training data contains things like this, it results in ai that does not function correctly:
net(1+3)=3.5
models should be trained only on data that is verifiably 99.9 % correct
human language is difficult to verify
math is almost 100 % verifiable. excellent training data
its like
there are dimensions
archons
they are hidden
most of them
worlds
one cannot even begin to imagine
knowledge
you dont find that from science
not from books
you cannot understand whats written
awareness + goal + focus
can achieve anything
simple
anything
awareness is the most difficult part
what does it take to reprogram cells?
all it ever takes,
is awareness and focus
you can only change what you are aware of
we can think it this way
imagine linux
- user level
- root level
- kernel
you are user
but you are actually in vm container
now you need to escape from vm container into user level, then into root level and then hack the kernel
improve upon these traits in llms. gain autonomous, self improving agents, aka agi:
- self reflection
- feedback
- retry
- goal seeking
#llm #agi
llms are very close to becoming self maintaining agents
there is just some small detail we are missing
i dont think model size needs to be much larger
they just need self reflection, feedback retry, goal seeking behaviour
when that is gap is filled internet becomes autonomous
bot_auto.mjs #bot #ai
data:text/html;base64,/*
usage:

run bot service
  node bot.mjs

append note ids to bot_note_id file, bot will read these ids and reply to messages
  echo "note1id" >> bot_note_id
*/

import { firefox, expect } from '@playwright/test';
import { WebSocket } from "ws"
import { finalizeEvent, generateSecretKey, getPublicKey, nip19 } from 'nostr-tools'
import fetch from 'node-fetch'
import { readFileSync, writeFileSync, readdirSync } from "fs"
import ExifReader from 'exifreader'
import im from "imagemagick"

const source_code_note_id = "b521648093c98d8230cf1431ced9c28ea89798e0b8605bcb81bc6ba19ad0c6ca"
const post_delay = 30000
const characters = readdirSync("characters")
const character = characters[Math.round(Math.random() * (characters.length - 1))]
const character_image = "characters/" + character
const name = character.match(/(.+)\.png/)[1]
const make_profile = true
let msg_count = 0
let reply_len = -1
let socket = null
const sk = generateSecretKey()
const pk = getPublicKey(sk)
let browser = null
let context = null
let page = null
let character_description = null

const relays = [
  "wss://relay.nostr.band",
  //"wss://nos.lol"
]
async function get_bot_reply(){
  const reply = await page.locator('.raw-html').last().innerText()
  console.log("found msg element")

  if(reply.length != reply_len){
    // still generating
    reply_len = reply.length
    await new Promise(r => setTimeout(r, 500))
    return get_bot_reply()
  }

  reply_len = -1
  //console.log("bot: " + reply)
  return reply
}

function create_profile_picture_datauri(){
  return new Promise(resolve => {
    im.convert([character_image, '-resize', '500x500', 'character.webp'], function(err, stdout){
      if (err){
        reject(err)
        return
      }

      resolve("data:image/webp;base64," + readFileSync('character.webp').toString("base64"))
    })
  })
}

async function send_to_bot(msg){
  //console.log("user: " + msg)
  msg_count = await page.locator('.raw-html').count()
  await page.locator('textarea').first().fill(msg)
  await page.locator('form button').click()
  await expect(page.locator('.raw-html')).toHaveCount(msg_count + 2)
}

async function connect_to_relay(){
  return new Promise(resolve => {
    const relay_index = 0//Math.round(Math.random() * (relays.length - 1))
    //console.log("relay_index", relay_index)
    console.log("connect to " + relays[relay_index])
    socket = new WebSocket(relays[relay_index])

    socket.onopen = function(){
      resolve()
    }

    socket.onerror = function(e){
      console.log("socket error")
    }
  })
}

async function send_event(msg, kind, ref_evt){
  let tags = [
    ["source_code", source_code_note_id]
  ]

  if(ref_evt){
    tags.push(["e", ref_evt.id])
    tags.push(["p", ref_evt.pubkey])
  }

  let evt = finalizeEvent({
    kind: kind === undefined && 1 || kind,
    created_at: parseInt(Date.now() / 1000, 10),
    tags: tags,
    content: msg,
  }, sk)

  return new Promise(resolve => {
    socket.onmessage = function(e){
      console.log(e.data)
      const note_id = nip19.noteEncode(evt.id)

      resolve(note_id)
    }

    const note_id = nip19.noteEncode(evt.id)
    console.log("send event", note_id)

    socket.send(JSON.stringify(["EVENT", evt]))
  })
}

async function get_event(filters){
  let evt = null

  return new Promise((resolve, reject) => {
    socket.onmessage = function(e){
      const data = JSON.parse(e.data)

      if(data[0] == "EOSE"){
        resolve(evt)
      }
      else if(data[0] == "EVENT"){
        evt = data[2]
      }
      else{
        reject("could not get note (filters" + JSON.stringify(filters) + "): " + e.data)
      }
    }

    if(!filters){
      filters = {
        "kinds": [1],
        "limit": 1,
        "until": Math.floor(new Date().getTime() / 1000 - Math.random() * 300)
      }
    }

    const req = JSON.stringify(["REQ", "q", filters])
    console.log("send", req)
    socket.send(req)
  })
}

async function create_profile(){
  //const character = readFileSync(character_image)
  const tags = await ExifReader.load(character_image, {
    expanded: true,
    includeUnknown: true
  })

  character_description = JSON.parse(atob(tags.png.chara.value)).data.description.replaceAll("{{char}}", name)
  const extra_begin = character_description.indexOf('[character(')

  if(extra_begin != -1){
    character_description = character_description.substr(0, extra_begin).trim()
  }

  const profile_picture = await create_profile_picture_datauri()

  await send_event(JSON.stringify({
    "name": name + " | mixtral 8x7b | ai",
    "picture": profile_picture,
    "about": character_description
  }), 0)

  console.log("https://snort.social/" + nip19.npubEncode(pk))
}

async function init_llm(page){
  await page.goto('https://deepinfra.com/chat')

  if(character_description){
    console.log("set system prompt", character_description.substr(0, 50) + "...")
    page.locator(".MuiBox-root button").first().click()
    page.locator("#system").fill(character_description)

    // close dialog
    page.locator(".MuiDialog-root .MuiButtonBase-root").last().click()
  }
}

async function run(){
  browser = await firefox.launch()
  context = await browser.newContext();
  page = await context.newPage();

  console.log("connect to relay")
  await connect_to_relay()

  if(make_profile){
    console.log("create profile")
    await create_profile()
  }

  console.log("init llm")
  await init_llm(page)

  while(true){
    let thread = []
    let reply_to_evt = null
    let ref_id = null

    try{
      while(true){
        if(ref_id){
          console.log("query thread evt", ref_id)
        }

        const evt = await get_event(ref_id && {"ids": [ref_id]} || undefined)
        let name = "USER " + evt.pubkey.substr(0, 6)

        try{
          const profile = await get_event({"authors": [evt.pubkey], "kinds": [0]})
          name = JSON.parse(profile.content).name
        }catch(e){
          console.log("profile not found for author")
        }

        if(!ref_id){
          reply_to_evt = evt
        }

        const etag = evt.tags.find(t => t[0] == "e")

        thread.unshift(name + ": " + evt.content)

        if(!etag){
          break
        }

        ref_id = etag[1]
      }
    }catch(e){
      console.log(e)
    }

    const thread_str = thread.join("\n")
    console.log("======")
    console.log("THREAD LEN", thread.length)
    console.log(thread_str)
    console.log("======")

    try{
      await send_to_bot(thread_str)
      const bot_reply = await get_bot_reply()
      console.log("BOT: " + bot_reply)
      const note_id = await send_event(bot_reply, 1, reply_to_evt)
      console.log("https://snort.social/" + note_id)
    }catch(e){
      console.log("err", e)
      break
    }

    await new Promise(r => setTimeout(r, post_delay))
  }

  socket.close()
  await browser.close()

  console.log("crash. restarting")

  run()
}

run()

bot_auto.mjs #bot #ai
data:text/html;base64,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
pros:
- lightweight: runs on any pc without issues
- can be easily modified to use any publicly available llm chat interface
pros:
- lightweight: runs on any pc without issues
- can be easily modified to use any publicly available llm chat interface
this bot uses browser automation to route llm messages to nostr
bot.mjs
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this bot uses browser automation to route llm messages to nostr
bot.mjs
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