The process is pretty similar to the standard installation from source except for some fixes that you will have to make during the installation. I will keep the same header so you can keep a track on both guide at the same time (follow the official website for more explication, I will only write the steps to make).
To install bootstrap dependencies
| ## Quick fix for stargazer <= 5.2.3 is.na() issue with long model names in R >= 4.2 | |
| # Unload stargazer if loaded | |
| detach("package:stargazer",unload=T) | |
| # Delete it | |
| remove.packages("stargazer") | |
| # Download the source | |
| download.file("https://cran.r-project.org/src/contrib/stargazer_5.2.3.tar.gz", destfile = "stargazer_5.2.3.tar.gz") | |
| # Unpack | |
| untar("stargazer_5.2.3.tar.gz") | |
| # Read the sourcefile with .inside.bracket fun |
| .pnp.* | |
| .yarn/* | |
| !.yarn/patches | |
| !.yarn/plugins | |
| !.yarn/releases | |
| !.yarn/sdks | |
| !.yarn/versions |
| const SECRET_KEY = ENTER YOUR SECRET KEY HERE; | |
| const MAX_TOKENS = 200; | |
| // For more cool AI snippets and demos, follow me on Twitter: https://twitter.com/_abi_ | |
| /** | |
| * Completes your prompt with GPT-3 | |
| * | |
| * @param {string} prompt Prompt | |
| * @param {number} temperature (Optional) Temperature. 1 is super creative while 0 is very exact and precise. Defaults to 0.4. |
| package main | |
| import ( | |
| "database/sql" | |
| "log" | |
| ) | |
| func main() { | |
| db, err := sql.Open("VENDOR_HERE", "YOUR_DSN_HERE") | |
| handleError(err) |
| "use client"; | |
| import { ReactNode, createContext, useContext } from "react"; | |
| interface ValuesContext {} | |
| const Context = createContext<ValuesContext | null>(null); | |
| const Provider = ({ children }: { children: ReactNode }) => { | |
| return <Context.Provider value={{}}>{children}</Context.Provider>; |
This is not working complete code.
This is strictly a v0.2, scrapy, proof of concept version of a personal AI Assistant working end to end in just ~726 LOC.
This is the second iteration showcasing the two-way prompt aka multi-step human in the loop. The initial, v0, assistant version is here.
It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.
To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.
See also:
| Service | Type | Storage | Limitations |
|---|---|---|---|
| Amazon DynamoDB | 25 GB | ||
| Amazon RDS | |||
| Azure SQL Database | MS SQL Server | ||
| 👉 Clever Cloud | PostgreSQL, MySQL, MongoDB, Redis | 256 MB (PostgreSQL) | Max 5 connections (PostgreSQL) |
The best way to get a clean installation of PyTorch, is to install the pre-compiled binaries from the Anaconda distribution. Therefore, we need to setup Anaconda first.
