pcuenca

//
//  MakeContiguousKernel.metal
//
//  Created by Pedro Cuenca on 20220307.
//  Copyright © 2022 LateNiteSoft S.L. All rights reserved.
//

#include <metal_stdlib>
using namespace metal;

dougallj

# IDA (disassembler) and Hex-Rays (decompiler) plugin for Apple AMX
#
# WIP research. (This was edited to add more info after someone posted it to
# Hacker News. Click "Revisions" to see full changes.)
#
# Copyright (c) 2020 dougallj


# Based on Python port of VMX intrinsics plugin:
# Copyright (c) 2019 w4kfu - Synacktiv

petewarden

import re
output_data = bytearray()
with open('tensorflow/tensorflow/lite/micro/examples/magic_wand/magic_wand_model_data.cc', 'r') as file:
  for line in file:
    values_match = re.match(r"\W*(0x[0-9a-fA-F,x ]+).*", line)
    if values_match:
      list_text = values_match.group(1)
      values_text = filter(None, list_text.split(","))
      values = [int(x, base=16) for x in values_text]
      output_data.extend(values)

tclementdev

libdispatch efficiency tips

The libdispatch is one of the most misused API due to the way it was presented to us when it was introduced and for many years after that, and due to the confusing documentation and API. This page is a compilation of important things to know if you're going to use this library. Many references are available at the end of this document pointing to comments from Apple's very own libdispatch maintainer (Pierre Habouzit).

My take-aways are:

• You should create very few, long-lived, well-defined queues. These queues should be seen as execution contexts in your program (gui, background work, ...) that benefit from executing in parallel. An important thing to note is that if these queues are all active at once, you will get as many threads running. In most apps, you probably do not need to create more than 3 or 4 queues.

• Go serial first, and as you find performance bottle necks, measure why, and if concurrency helps, apply with care, always validating under system pressure. Reuse

vincentchu

kendricktan

"""
Dynamic Routing Between Capsules
https://arxiv.org/abs/1710.09829
"""

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as transforms

jesseengel

import os

import librosa
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['svg.fonttype'] = 'none'
import numpy as np
from scipy.io.wavfile import read as readwav

# Constants

yossorion

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

venkatperi

//  CGContextABCD(context!, ...) becomes context?.ABCD(...)

import Cocoa

extension CGContext {
    func saveGState() { CGContextSaveGState(self) }
    func restoreGState() { CGContextRestoreGState(self) }
    func scaleCTM( sx: CGFloat, sy: CGFloat) { CGContextScaleCTM(self, sx, sy) }
    func translateCTM( tx: CGFloat, ty: CGFloat) { CGContextTranslateCTM(self, tx, ty) }
    func rotateCTM( angle: CGFloat) { CGContextRotateCTM(self, angle) }