jpawlowski

#!/bin/bash

# Sync Homebrew installations between Macs via Dropbox
#

BREW="/usr/local/bin/brew"

# first get local settings
echo "Reading local settings ..."
rm -f /tmp/brew-sync.*

tgrrr

import React from 'react';
import {Tabs, Tab} from 'material-ui/Tabs';
import Slider from 'material-ui/Slider';
import { withRouter } from 'react-router';
import { BrowserRouter as Router, Route, withRouter } from 'react-router-dom'

class TabsRouter extends React.Component {
  constructor(props) {
    super(props);
    this.state = {

witt3rd

Hask(Cat)

{-# LANGUAGE TypeOperators #-}
 
infix 9 ∘
class Category k where
  ident :: a `k` a
  (∘) :: (b `k` c) -> (a `k` b) -> (a `k` c)
 

disler

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.

disler

Minimal Prompt Chainables

Sequential prompt chaining in one method with context and output back-referencing.

Files

• main.py - start here - full example using MinimalChainable from chain.py to build a sequential prompt chain
• chain.py - contains zero library minimal prompt chain class
• chain_test.py - tests for chain.py, you can ignore this
• requirements.py - python requirements

Setup