LiquidityC

VERSION = \"1.0.0\"

PREFIX ?= out
INCDIR	= inc
SRCDIR	= src
LANG	= c
OBJDIR	= .obj

MODULE		= binary_name
CC		= gcc

kconner

macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

yoavg

Some remarks on Large Language Models

Yoav Goldberg, January 2023

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.

Intro

Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We

paydro

-- Copy/paste this file or execute with `psql -f thisfile.sql`

CREATE ROLE owner CREATEDB LOGIN ENCRYPTED PASSWORD 'secret' CONNECTION LIMIT 3;

ALTER ROLE owner SET statement_timeout = 20000;
ALTER ROLE owner SET lock_timeout = 3000;
ALTER ROLE owner SET idle_in_transaction_session_timeout = 3000; -- v9.6+

CREATE ROLE readwrite_users NOLOGIN;
CREATE ROLE readonly_users NOLOGIN;

fcamblor

FROM tomcat:7-jre8-alpine

# See https://github.com/jvm-profiling-tools/async-profiler/issues/207
RUN apk update && apk add --no-cache libc6-compat perl openjdk8-dbg

RUN mkdir /usr/local/async-profiler/ &&\
      wget -O /usr/local/async-profiler/async-profiler.tar.gz https://github.com/jvm-profiling-tools/async-profiler/releases/download/v1.5/async-profiler-1.5-linux-x64.tar.gz &&\
      cd /usr/local/async-profiler/ &&\
      tar -xvzf async-profiler.tar.gz &&\
      rm -f /usr/local/async-profiler/async-profiler.tar.gz

nave91

# MIT License

# Copyright (c) 2019 Bellhops Inc.

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

Spittal

version: '3'
services:
  fpm:
    image: sbvr/laravel-fpm:2.1.2
    volumes:
      - app:/var/www
    networks:
      - appnet

  worker:

nasrulhazim

Installation

This procedure is tested on Mac OS X 10.12.6

PHP 7.2 installed with Homebrew.

Preparation

Download the following files from Oracle website (yes, you need to create an account and accept terms):

iandees

These are my notes for taking the Microsoft US Building Footprints and splitting them into more manageable chunks based on US Census Tracts.

All of this happened on an m5.xlarge in AWS and used up about ~300GB of EBS over the course of a few hours.

1. Make a filesystem on the EBS volume and mount it:

   sudo mkfs.xfs /dev/nvme1n1
   mount /dev/nvme1n1 /mnt
   

carlosedp

[
  {
    "_id": "74ad2b40-3774-11e8-8bf5-0529df825f82",
    "_type": "visualization",
    "_source": {
      "title": "Amount Errors",
      "visState": "{\"title\":\"Amount Errors\",\"type\":\"metric\",\"params\":{\"addLegend\":false,\"addTooltip\":true,\"metric\":{\"colorSchema\":\"Green to Red\",\"colorsRange\":[{\"from\":0,\"to\":10000}],\"invertColors\":false,\"labels\":{\"show\":true},\"metricColorMode\":\"None\",\"percentageMode\":false,\"style\":{\"bgColor\":false,\"bgFill\":\"#000\",\"fontSize\":60,\"labelColor\":false,\"subText\":\"\"},\"useRanges\":false},\"type\":\"metric\"},\"aggs\":[{\"id\":\"1\",\"enabled\":true,\"type\":\"count\",\"schema\":\"metric\",\"params\":{\"customLabel\":\"\"}},{\"id\":\"3\",\"enabled\":true,\"type\":\"filters\",\"schema\":\"group\",\"params\":{\"filters\":[{\"input\":{\"query\":\"log: Traceback OR log: error\"},\"label\":\"Error Log Entries\"}]}}]}",
      "uiStateJSON": "{}",
      "description": "",
      "version": 1,