Kiran Adimatyam seshakiran

Adaptive Streaming has become the neccessity for streaming video and audio. Unfortantely, as of this post, there isn't a whole lot of tutorials that accumulate all of the steps to get this working. Hopefully this post achieves that. This post focuses on using Amazon Web Services (AWS) to transcode for HLS and DASH and be the Content Delivery Network (CDN) that delivers the stream to your web page. We'll be using Video.js for the HTML5 player as well as javascript support libaries to make Video.js work with HLS and DASH.

	git clone https://github.com/stitionai/devika.git
	conda create -n devika python=3.10
	conda activate devika
	which python
	/usr/local/anaconda3/envs/devika/bin/python -m pip install -r requirements.txt
	playwright install --with-deps
	npm install
	bun run dev
	open new terminal
	conda activate devika

	git clone https://github.com/OpenDevin/OpenDevin.git
	cd OpenDevin
	conda create -n od python=3.10
	conda activate od
	docker ps
	(optional) install docker if not already installed
	docker pull ghcr.io/opendevin/sandbox
	export OPENAI_API_KEY={your key}
	(optional I had to install rust) curl --proto '=https' --tlsv1.2 -sSf [https://sh.rustup.rs](https://sh.rustup.rs/) \| sh
	(optional) restart terminal

	from langchain.chat_models import ChatOpenAI
	from pydantic import BaseModel, Field
	from langchain.document_loaders import UnstructuredURLLoader
	from langchain.chains.openai_functions import create_extraction_chain_pydantic

	class LLMItem(BaseModel):
	title: str = Field(description="The simple and concise title of the product")
	description: str = Field(description="The description of the product")

	def main():

	// Requires the gpt library from https://github.com/hrishioa/socrate and the progress bar library.
	// Created by Hrishi Olickel ([email protected]) (@hrishioa). Reach out if you have trouble running this.

	import { ThunkQueue } from '../../utils/simplethrottler';
	import {
	AcceptedModels,
	Messages,
	askChatGPT,
	getMessagesTokenCount,
	getProperJSONFromGPT,

	#!/bin/bash

	# Locate the history file in your profile, and copy it to the same folder as this script.
	# On Mac: ~/Library/Application\ Support/Google/Chrome/Default/History
	# On Windows: C:\Users\YOUR USER NAME\AppData\Local\Google\Chrome\User Data\Default\History

	sqlite3 History <<!
	.headers on
	.mode csv
	.output out.csv

Kiran Adimatyam seshakiran

So Here's what you need:

Set up three S3 buckets

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD