ruvnet

ruvnet

import os
import asyncio
import subprocess
import importlib
import sys
from dotenv import load_dotenv
from lionagi import Session
from e2b_code_interpreter import CodeInterpreter
from llama_index.core import (
    VectorStoreIndex,

disler

Setup

0. Create a new directory with these three files (requirements.txt, main.py, README.md)
1. python -m venv venv
2. source venv/bin/activate
3. pip install -r requirements.txt
4. python main.py
5. Update main() to run the example prompt chains

ruvnet

Incorporating a Hypergraph Lambda Calculus (HLC) based model as part of a larger mixture of experts system could provide several benefits and enhance the overall capabilities of the model:

1. Improved Reasoning Capabilities:

   • HLC's higher-order logic and lambda calculus foundations enable more sophisticated reasoning capabilities.
   • The model can handle complex dependencies, abstractions, and quantification, allowing it to perform advanced inference and deduction tasks.
   • This can complement other experts in the mixture that may focus on pattern recognition, data-driven learning, or specialized domain knowledge.

2. Enhanced Expressiveness:

   • HLC's hypergraph-based representation allows modeling complex structures and relationships that may be difficult to capture with traditional graph-based or vector-based representations.

• The model can express and reason about intricate domain knowledge, logical rules, and constraints.

ruvnet

// Pricing per 1 million tokens, converted to per-token cost
const modelPricing = {
  'GPT-4': { inputCost: 30.00 / 1e6, outputCost: 60.00 / 1e6 },
  'GPT-4 32K': { inputCost: 60.00 / 1e6, outputCost: 120.00 / 1e6 },
  'GPT-4 Turbo': { inputCost: 10.00 / 1e6, outputCost: 30.00 / 1e6 },
  'GPT-3.5 Instruct': { inputCost: 1.50 / 1e6, outputCost: 2.00 / 1e6 },
  'GPT-3.5': { inputCost: 0.50 / 1e6, outputCost: 1.50 / 1e6 },
  'Claude 3 Opus': { inputCost: 15.00 / 1e6, outputCost: 75.00 / 1e6 },
  'Claude 2': { inputCost: 8.00 / 1e6, outputCost: 24.00 / 1e6 },
  'Claude 3 Sonnet': { inputCost: 3.00 / 1e6, outputCost: 15.00 / 1e6 },

ruvnet

# Unified Semantic Network Representation with Comprehensive Details in TOML
# Document models the professional achievements and contributions of Reuven Cohen.

[concepts]
C1 = { name = "Cloud Computing Innovation", description = "Foundational work in cloud computing and infrastructure services." }
C2 = { name = "Entrepreneurship and Leadership", description = "Founding and leading technology startups and companies." }
C3 = { name = "Advisory and Mentorship", description = "Providing expert guidance to startups, accelerators, and technology councils." }
C4 = { name = "Industry Recognition and Awards", description = "Recognition by industry bodies for contributions to technology and innovation." }
C5 = { name = "Technology Advocacy and Thought Leadership", description = "Influencing the direction of cloud computing, open standards, and technology innovation." }
C6 = { name = "Educational Contributions", description = "Participation in executive education and design thinking at prestigious institutions." }

rf5860

// ==UserScript==
// @name         HuggingFaceGGUFModelToClipboard
// @namespace    https://huggingface.co
// @version      0.1
// @description  Add a button to copy direct-download links for GGUF models to the clipboard
// @author       rjf89
// @match        https://huggingface.co/*
// @grant        GM_setClipboard
// @grant        GM_addStyle
// ==/UserScript==

adrianhajdin

import { NextResponse } from "next/server";

import { getLowestPrice, getHighestPrice, getAveragePrice, getEmailNotifType } from "@/lib/utils";
import { connectToDB } from "@/lib/mongoose";
import Product from "@/lib/models/product.model";
import { scrapeAmazonProduct } from "@/lib/scraper";
import { generateEmailBody, sendEmail } from "@/lib/nodemailer";

export const maxDuration = 300; // This function can run for a maximum of 300 seconds
export const dynamic = "force-dynamic";

Charlesmendez

import pandas as pd
import requests
import time
from datetime import datetime
from ide_utils.input_tables import load_dask
from ide_utils.output_table import send_to_output_table

API_TOKEN = "YOUR TOKEN"
base_url = "https://cloud.iexapis.com/v1"
endpoint = "/stock/{}/chart"

mberman84

# Must have conda installed
# It costs approximately $0.2 (in GPT-4 API fees) to generate one example with analysis and design, and around $2.0 for a full project.

conda create -n metagpt python=3.11.4
conda activate metagpt
npm --version # to check you have npm installed
# optional: install node if you don't have it
npm install -g @mermaid-js/mermaid-cli
git clone https://github.com/geekan/metagpt
cd metagpt