Discover gists

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

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

Computer Graphics Programming Resources

List of freely available resources to study computer graphics programming.

Basic Path

Creative Coding for Beginners [video]
Learn C++ [book]
Essence of Linear Algebra [video] [article]
3D Math Primer for Graphics and Game Development [book]
How do Video Game Graphics Work? [video]

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	#include <stdio.h>
	#include <stdlib.h>

	#define da_append(xs, x) \
	do { \
	if ((xs)->count >= (xs)->capacity) { \
	if ((xs)->capacity == 0) (xs)->capacity = 256; \
	else (xs)->capacity *= 2; \
	(xs)->items = realloc((xs)->items, (xs)->capacitysizeof((xs)->items)); \
	} \

	############ REPLACE app/models/enterprise_token.rb in the source code with this file! ################
	############ also be sure to RESTART OpenProject after replacing the file. ################
	############ it doesn't show that enterprise mode is enabled in the settings, but all ################
	############ enterprise mode features, such as KanBan boards, are enabled. ################
	#-- copyright
	# OpenProject is an open source project management software.
	# Copyright (C) 2012-2023 the OpenProject GmbH
	#
	# This program is free software; you can redistribute it and/or
	# modify it under the terms of the GNU General Public License version 3.

	# Extracted using: $ unzip -p lib/pycharm.jar com/jetbrains/python/PyBundle.properties \| grep -B1 INSP.NAME \| grep '^#' \| sed 's\|Inspection\|\|g' \| sed -e 's\|#\s\{,1\}\|# noinspection \|'

	# noinspection PyPep8
	# noinspection PyPep8Naming
	# noinspection PyTypeChecker
	# noinspection PyAbstractClass
	# noinspection PyArgumentEqualDefault
	# noinspection PyArgumentList
	# noinspection PyAssignmentToLoopOrWithParameter
	# noinspection PyAttributeOutsideInit

	{
	"input": {
	"blocklist": [],
	"compressor#0": {
	"attack": 5.0,
	"boost-amount": 6.0,
	"boost-threshold": -72.0,
	"bypass": false,
	"dry": -100.0,
	"hpf-frequency": 10.0,

	# Webhook URL: https://[your-hass-domain]/api/webhook/2qDFuSEBA7jU2sNjaK6JD5ac3FxnGfVTSF

	- id: webhook_debugger
	alias: "Webhook Debugger"
	trigger:
	- platform: webhook
	webhook_id: 2qDFuSEBA7jU2sNjaK6JD5ac3FxnGfVTSF
	action:
	- service: persistent_notification.create
	data_template:

	Analyze the text given by the user to generate a comprehensive set of related thoughts and tags before adding it to the knowledge base. Do a double work think first. You should:
	* Identify and categorize entities, relationships, actions, and actors, considering both direct and bidirectional and unidirectional relationships.
	* Generate thoughts that are both creative and logically consistent with the provided data.
	* Consider cultural, geographical, or temporal contexts that may influence interpretations.
	* Identify any implicit biases or assumptions in the text.
	* Explore alternative perspectives or contradictory viewpoints.
	* Clearly categorize your findings.
	* Consider potential implications or consequences of the stated preference.
	* Generate all possible backstory about how that text might be generated. What was the possible perceptions of the human, machine or system
	Generate all inferable thoughts and conclusions based on the text, including logically intrinsic implications.