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[
    {
        "test_case": "Getting weather information",
        "models": [
            "mistralai/mistral-7b-instruct",
            "mistralai/mixtral-8x7b-instruct",
            "openai/gpt-3.5-turbo-1106",
            "anthropic/claude-2.1"
        ],
        "steps": [

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event - visual conversation

Visual Conversation: An AI-Assisted Silent Dialogue

Join us for an extraordinary journey into the realm of silent communication, where words are replaced by images, and the language of the eyes becomes the bridge to a deeper understanding.

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Company	Focus	Funding	Valuation	Revenue	Description
SpaceX	Launch services, spacecraft manufacturing, satellite internet	$7.2B	$127B	$2.8B (2021)	Develops Falcon 9, Falcon Heavy rockets, Starlink internet satellites
Blue Origin	Launch services, rocket engines, space tourism	$3.7B	$75B	-	Develops New Shepard for space tourism, New Glenn rocket
Virgin Galactic	Space tourism	$1.3B	$3.8B	-	Operates SpaceShipTwo for suborbital space tourism
Rocket Lab	Launch services, rocket manufacturing	$698M	$4.1B	-	Small satellite launcher, builds Electron rocket
Sierra Space	Next-gen space infrastructure, space stations	-	-	-	Developing Dream Chaser spaceplane, LIFE habitat
Axiom Space	Human spaceflight services, space stations	$150M	-	-	Building private space station, provides ISS services

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const createJob = async (image) => {
    const url = 'https://api.hume.ai/v0/batch/jobs';
    const formData = new FormData();
    formData.append('file', image);

    const response = await fetch(url, {
        method: 'POST',
        headers: {
            'X-Hume-Api-Key': process.env.HUME_API_KEY,
            'Accept': 'application/json',

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SELECT 
    (COUNT(DISTINCT CASE 
        WHEN (raw_session_replay_events.click_count = 0 AND raw_session_replay_events.active_milliseconds < 60000) 
        THEN raw_session_replay_events.session_id 
        ELSE NULL 
    END) * 100.0) / COUNT(DISTINCT properties.$session_id) AS bounce_rate
FROM 
    events
INNER JOIN 
    raw_session_replay_events ON events.properties.$session_id = raw_session_replay_events.session_id

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import data
import torch
from models import imagebind_model
from models.imagebind_model import ModalityType
import uvicorn
from fastapi import FastAPI
import requests


device = "cuda:0" if torch.cuda.is_available() else "cpu"

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import Bundlr from "@bundlr-network/client";
import fs from "fs";
import Arweave from "arweave";
import { Neurosity } from "@neurosity/sdk";
const jwk = JSON.parse(fs.readFileSync("wallet.json").toString());

const neurosity = new Neurosity();

neurosity.login({
  email: process.env.NEUROSITY_EMAIL!,

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from websockets import connect
import json
import numpy as np
from sklearn.decomposition import IncrementalPCA as PCA
import streamlit as st
import plotly.graph_objects as go

# Create initial figure
fig = go.Figure()

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Today, I bring to you a thought-provoking concept: the amalgamation of technology and psychology, or more specifically, the application of System 1 and System 2 LLMs in AI programming. This idea was born from two main observations I made.

Firstly, there is the revolutionary technology of "embeddings" that has seen its usage skyrocket since 2022. It allows us to create a programmable memory for AI, thus eliminating the need to grapple with prompt engineering. I began to ponder if there could be a more efficient alternative to embeddings, one that utilizes fast and economical LLMs.

Secondly, the theory of System 1 and System 2 in psychology, which speaks about two different decision-making processes in our brain, caught my attention. System 1 corresponds to our intuitive, automatic, often subconscious thought process, like recognizing facial expressions. On the other hand, System 2 is deliberate, slower, and more logical, like solving complex math problems.

Inspired by this theory, I began to explore the po

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const { exec } = require('child_process');
const recursive = require("recursive-readdir");
const { Configuration, OpenAIApi } = require("openai");
const readline = require('readline');
const fs = require('fs');
const path = require('path');


async function listModels() {
    const response = await fetch("https://api.airtable.com/v0/appwJMZ6IAUnKpSwV/all", {