runvnc

# Standards-Based Architecture Concept

## Timeline for Incorporation of xAPI Standard

1. Finish a few things with the functionality of the current demo, such as 
checking free-form answers to see if they match choices, and the reading page
mysteriously not working now (seemed to work when tested locally a few days ago).

2. Start implementing the UI improvements in a way that ideally can be 
transferred relatively easily to the new architecture.

runvnc

{
  "basics": {
    "name": "Jason L.",
    "label": "Software Engineer",
    "email": "runvnc@gmail.com",
    "location": {
      "city": "Mission",
      "region": "TX",
      "countryCode": "US"
    },

runvnc

import {getIndexer} from './access.mjs'
import delay from 'delay'
import fs from 'fs/promises'

const print = console.log
//const error = console.error

let indexer = getIndexer('MAINNET')

const getKey = (addr, type) => addr + '.'+type

runvnc

import os, openai
openai.api_key = os.environ.get("OPENAI_API_KEY", None)

def chat(prompt):
    msgs = [{"role": "system", "content":"You are a helpful assistant."},
            {"role": "user", "content": "Hello."},
            {"role": "assistant": "content": "Hi, how can I help you?",
            {"role": "user", "content": prompt}]

    res = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=msgs)

runvnc

{
  "metadata": {
    "version": 4.5,
    "type": "Object",
    "generator": "Object3D.toJSON"
  },
  "geometries": [
    {
      "uuid": "1f40d935-e779-49a7-9af1-98a7b29c19f6",
      "type": "BoxGeometry",

runvnc

<div id="deck" class="stack">
  
  <div id="card3" class="card" style="display:block;">
    <div id="back3" class="card back bg">
     </div>
    <div id="front3" style="display:none;" class="card fg reversed" >Third Card </div>
  </div>   
   
  <div id="card2" class="card bottom" style="display:none;">
    <div id="back2" class="card back bg">

runvnc

 let inquirer = require('inquirer')
 let prompt = inquirer.prompt

 inquirer.registerPrompt('search-list', require('inquirer-search-list'))

 let print = console.log

 let cfg = {}, fn = {}

 let projs =

runvnc

2d to 3d reconstruction computer vision idea

Key idea is that especially for (man-made) indoor environments, there are many very regular shapes. So should be able to model much of the environment efficiently and relatively accurately using high-level shapes.

Need a very fast GPU-based CSG renderer.

• Start with various indoor scenes and break them down into surfaces
• So could use mesh data and convert to CSG representation
• This is just a way to bootstrap the CSG data based on indoor objects and arrangements

runvnc

# Note: This is NOT the final version to be audited. Planned improvements in the next few releases:
# 1. Check AssetSender in 'SafetyCheckAll'.
# 2. Properly close Escrow to return transaction/asset holding funds to Seller (around 0.3 ALGO) at end of auction.
# 3. Allow non-creator auction and include creator fee.
# 4. May or may not submit to audit with Platform Fee.
# 
# Note this code is generated by Tealang which is a high-level language somewhat like Go. The compiler needs work
# to improve the optimization of the code generated which is quite inefficient (but within current compute allowance).
# However, the TEAL output does correspond 100% to the high-level code, which can serve as a very readable guide when
# reviewing the TEAL code.  The high-level code will be provided to anyone auditing the contract.

runvnc

2012-04-05 18:12:28
Full thread dump OpenJDK 64-Bit Server VM (19.0-b09 mixed mode):

"Attach Listener" daemon prio=10 tid=0x0000000002e1a800 nid=0x19ff waiting on condition [0x0000000000000000]
   java.lang.Thread.State: RUNNABLE

"Attach Listener" daemon prio=10 tid=0x0000000002519000 nid=0x19fe runnable [0x0000000000000000]
   java.lang.Thread.State: RUNNABLE

"1610952854@qtp-3981922-153" prio=10 tid=0x00007f29ee0f3800 nid=0x19fd in Object.wait() [0x00007f29f01bc000]