jawond

import React from "react"
import { DragDropContext as DragDropContext_, Droppable as Droppable_, Draggable as Draggable_ } from "react-beautiful-dnd"


// Maps

const draggableMap = {}
const droppableMap = {}

jawond

#!/bin/bash

OPENAI_API_KEY="YOUR_API_KEY"
CONVERSATION="/tmp/$(cat /proc/sys/kernel/random/uuid)"

query_open_ai() {
  curl -s https://api.openai.com/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d "{

jawond

<artifacts_info>
The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

# Good artifacts are...
- Substantial content (>15 lines)
- Content that the user is likely to modify, iterate on, or take ownership of
- Self-contained, complex content that can be understood on its own, without context from the conversation
- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
- Content likely to be referenced or reused multiple times

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Minimal Prompt Chainable

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 chian
• 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

jawond

// 3D Dom viewer, copy-paste this into your console to visualise the DOM as a stack of solid blocks.
// You can also minify and save it as a bookmarklet (https://www.freecodecamp.org/news/what-are-bookmarklets/)
(() => {
  const SHOW_SIDES = false; // color sides of DOM nodes?
  const COLOR_SURFACE = true; // color tops of DOM nodes?
  const COLOR_RANDOM = false; // randomise color?
  const COLOR_HUE = 190; // hue in HSL (https://hslpicker.com)
  const MAX_ROTATION = 180; // set to 360 to rotate all the way round
  const THICKNESS = 20; // thickness of layers
  const DISTANCE = 10000; // ¯\\_(ツ)_/¯

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MLX LM with the OpenAI Python Package

1. Install

Install MLX LM and openai:

pip install mlx-lm openai

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# --------------------------------------------------------------
# Customer Support Ticket Classification System
# --------------------------------------------------------------

import instructor
from pydantic import BaseModel, Field
from openai import OpenAI
from enum import Enum
from typing import List

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"""
A minimal, fast example generating text with Llama 3.1 in MLX.

To run, install the requirements:

    pip install -U mlx transformers fire

Then generate text with:

    python l3min.py "How tall is K2?"

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Clone the model

This can take a little while:

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/black-forest-labs/FLUX.1-schnell

jawond

from typing import Any, Dict, List, Type

import instructor
from anthropic import Anthropic
from config.settings import get_settings
from openai import OpenAI
from pydantic import BaseModel, Field


class LLMFactory: