| (Note: tested on Ubuntu 19.10 host/x86_64 machine) | |
| ~~ | |
| Install dependencies: | |
| apt-get install binutils-aarch64-linux-gnu gcc-9-aarch64-linux-gnu bison flex build-essential kpartx | |
| sudo apt-get install libgmp-dev libmpfr-dev libmpc-dev | |
| cd /usr/bin | |
| sudo ln -s aarch64-linux-gnu-gcc-9 aarch64-linux-gnu-gcc | |
| # Build armstubs: |
| import urllib | |
| import os | |
| import json | |
| if not os.path.exists('./emotes'): | |
| os.makedirs('./emotes') | |
| print('folder: ' + os.path.abspath('./emotes') + '...') | |
| emotes = json.load(urllib.urlopen('https://twitchemotes.com/api_cache/v3/global.json')) | |
| for code, emote in emotes.items(): | |
| print('dl: ' + code + '...') |
| # A python script to turn annotated data in standoff format (brat annotation tool) to the formats expected by Stanford NER and Relation Extractor models | |
| # - NER format based on: http://nlp.stanford.edu/software/crf-faq.html#a | |
| # - RE format based on: http://nlp.stanford.edu/software/relationExtractor.html#training | |
| # Usage: | |
| # 1) Install the pycorenlp package | |
| # 2) Run CoreNLP server (change CORENLP_SERVER_ADDRESS if needed) | |
| # 3) Place .ann and .txt files from brat in the location specified in DATA_DIRECTORY | |
| # 4) Run this script |
| # UPDATED 17 February 2019 | |
| # Redirect all HTTP traffic to HTTPS | |
| server { | |
| listen 80; | |
| listen [::]:80; | |
| server_name www.domain.com domain.com; | |
| return 301 https://$host$request_uri; | |
| } | |
| # SSL configuration |
NOTE I'm trying to find the most optimal fav/touch icon setup for my use-cases. Nothing new here. Read Mathias Bynens' articles on re-shortcut-icon and touch icons, a FAQ or a Cheat Sheet for all the details.
I'd like to hear how you approach this: @valuedstandards or comment on this gist.
You have to include a boatload of link elements pointing to many different images to provide (mobile) devices with a 'favicon' or 'touch icon':
| #!/bin/sh | |
| ############################################################################################################## | |
| # onetime setup script for mac, including homebrew, some apps and some settings # | |
| # full dotfile repository coming soon # | |
| ############################################################################################################## | |
| #!/usr/bin/env python | |
| #install the follow first: | |
| #sudo easy_install pip | |
| #sudo pip install -U boto | |
| #sudo pip install configparser |
| #include <czmq.h> | |
| #define NUM_MESSAGES (50000000) | |
| void no_op(void *unused1, void *unused2) {} | |
| void run_send() { | |
| zctx_t *ctx = zctx_new(); | |
| void *outsock = zsocket_new(ctx, ZMQ_PUSH); | |
| zsocket_connect(outsock, "tcp://localhost:5005"); |
I have some early benchmark results for our work on a high performance NATS server in Go.
Quick Summary:
We can process ~2M msgs/sec through the system, and the ingress and egress are fairly well balanced.
The basics of the architecture are intelligent buffering and IO calls, fast hashing algorithms and subject distributor/routing, and a zero-allocation hand-written protocol parser.
In addition, I used quite a bit of inlining to avoid function overhead, no use of defer, and little to no object allocation within the fast path. I will share more details and the code at a future date.
| // | |
| // GIFDownloader.h | |
| // TheJoysOfCode | |
| // | |
| // Created by Bob on 29/10/12. | |
| // Copyright (c) 2012 Tall Developments. All rights reserved. | |
| // | |
| #import <Foundation/Foundation.h> |