randombrein

At the beginning of 2030, I found this essay in my archives. From what I know today, I think it was very insightful at the moment of writing. And I feel it should be published because it can teach us, Rust developers, how to prevent that sad story from happening again.

What killed Haskell, could kill Rust, too

What killed Haskell, could kill Rust, too. Why would I even mention Haskell in this context? Well, Haskell and Rust are deeply related. Not because Rust is Haskell without HKTs. (Some of you know what that means, and the rest of you will wonder for a very long time). Much of the style of Rust is similar in many ways to the style of Haskell. In some sense Rust is a reincarnation of Haskell, with a little bit of C-ish like syntax, a very small amount.

Is Haskell dead?

Preamble

There is a longstanding issue/missing feature/bug with sockets on Docker on macOS; it may never work; you'll need to use a network connection between Docker containers and X11 on macOS for the foreseeable future.

I started from this gist and made some adjustments:

the volume mappings aren't relevant/used, due to the socket issue above.
this method only allows X11 connections from your Mac, not the entire local network, which would include everyone on the café/airport WiFi.
updated to include using the host.docker.internal name for the the container host, instead.
you have to restart XQuartz after the config change.

Now available here: https://github.com/y0ast/pytorch-snippets/tree/main/fast_mnist

Here's a list of mildly interesting things about the C language that I learned mostly by consuming Clang's ASTs. Although surprises are getting sparser, I might continue to update this document over time.

There are many more mildly interesting features of C++, but the language is literally known for being weird, whereas C is usually considered smaller and simpler, so this is (almost) only about C.

1. Combined type and variable/field declaration, inside a struct scope [https://godbolt.org/g/Rh94Go]

struct foo {
   struct bar {
 int x;

Preparation

In open Ubuntu 18.04 machine click Parallels Actions -> "Install Parallels Tools"
A "Parallels Tools" CD will popup on your Ubuntu desktop.
Open it by double mouse click, copy all the content to a new, empty directory on a desktop, name it for e.g. "parallels_fixed"
Open terminal, change directory to parallels_fixed (cd ~/Desktop/parallels_fixed)
Make command line installer executable (chmod +x install)
Change directory to "installer" (cd installer)
Make few other scripts executable: chmod +x installer.* *.sh prl_*

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

Electron apps

Slide deck frameworks supporting Markdown source

	// This script is deprecated, use https://github.com/OverlappingElvis/blob-opera-midi instead!

	const fs = require(`fs`)
	const _ = require(`lodash`)
	const { Player } = require(`midi-player-js`)
	const blessed = require(`blessed`)
	const contrib = require(`blessed-contrib`)
	const Alea = require(`alea`)

	const VOWELS = _.range(4)

	# This file contains a collection of workarounds for missing TFLite support from:
	# https://github.com/tensorflow/magenta/tree/master/magenta/music
	# as posted in https://github.com/tensorflow/tensorflow/issues/27303
	# Thanks a lot to github.com/rryan for his support!

	# The function for testing MFCC computation given PCM input is:
	# - test_mfcc_tflite
	# Please not that the output has not yet been compared to the one produced by the respective TF functions.

	# This file also contains test code for other problems in the context of audio processing with TF and TFLite:

	import cv2
	import numpy as np
	import torch
	from torchvision import models, transforms
	from torch.autograd import Variable
	import torch
	import torch.nn as nn
	import pickle
	import os
	import argparse