STYLIANOS IORDANIS stjordanis

Mandelbrot in Mojo with Python plots

Not only Mojo is great for writing high-performance code, but it also allows us to leverage huge Python ecosystem of libraries and tools. With seamless Python interoperability, Mojo can use Python for what it's good at, especially GUIs, without sacrificing performance in critical code. Let's take the classic Mandelbrot set algorithm and implement it in Mojo.

We'll introduce a Complex type and use it in our implementation.

Mandelbrot in python

Asking GPT3.5 to refactor gpt2() into a parallel prefix over inputs with code from:

https://github.com/jaymody/picoGPT/blob/3b7f4d180bb125a76b2f4f7b3a74268e5ec5f131/gpt2_pico.py#L37

def gpt2(inputs, wte, wpe, blocks, ln_f, n_head, chunk_size):
    n_chunks = len(inputs) // chunk_size
    chunked_inputs = np.split(inputs, n_chunks)
    embeddings = []

Using Whisper to transcribe audio

This episode of Recsperts was transcribed with Whisper from OpenAI, an open-source neural net trained on almost 700 hours of audio. The model includes an encoder-decoder architecture by tokenizing audio into 30-second chunks, normalizing audio samples to the log-Mel scale, and passing the data into an encoder. A decoder is trained to predict the captioned text matching the encoder, and the model includes transcription, as well as timestamp-aligned transcription, and multilingual translation.

The transcription process outputs a single string file, so it's up to the end-user to parse out individual speakers, or run the model [through a sec

	import json
	import os

	TRAIN_PATH = 'PCBData/PCBData/trainval.txt'
	TEST_PATH = 'PCBData/PCBData/test.txt'


	def create_data(data_path, output_path):
	images = []
	anns = []

	\documentclass[border=8pt, multi, tikz]{standalone}
	\usepackage{import}
	\subimport{../layers/}{init}
	\usetikzlibrary{positioning}
	\usetikzlibrary{3d} %for including external image

	\def\ConvColor{rgb:yellow,5;red,2.5;white,5}
	\def\ConvReluColor{rgb:yellow,5;red,5;white,5}
	\def\PoolColor{rgb:red,1;black,0.3}
	\def\UnpoolColor{rgb:blue,2;green,1;black,0.3}

	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():

	import openai

	openai.api_key = "YOUR KEY GOES HERE"

	def get_completion(messages):
	response = openai.ChatCompletion.create(
	model="gpt-3.5-turbo-0613",
	messages=messages,
	functions=[{
	"name": "fake",


	function mandelbrot_kernel(c, max_iter)
	z = c
	for i in 1:max_iter
	z = z * z + c
	if abs2(z) > 4
	return i-1
	end
	end
	return max_iter

	# Mathieu Blondel, 2022
	# BSD license

	import numpy as np
	from scipy.ndimage import convolve1d
	from sklearn.metrics.pairwise import euclidean_distances


	def smoothed_conjugate_conv(f, x, eps=1.0):
	"""