mullikine / all-prs.txt

Created October 17, 2019 03:07

All PRs

	https://github.com/0xAX/go-algorithms/pull/18
	https://github.com/360EntSecGroup-Skylar/excelize/pull/303
	https://github.com/360EntSecGroup-Skylar/excelize/pull/315
	https://github.com/3d0c/gmf/pull/98
	https://github.com/AdguardTeam/AdGuardHome/pull/638
	https://github.com/AlecAivazis/survey/pull/181
	https://github.com/AlessandroMinoccheri/codeigniter-currency-converter/pull/16
	https://github.com/AntoineAugusti/feature-flags/pull/10
	https://github.com/AntoineAugusti/feature-flags/pull/11
	https://github.com/AntoineAugusti/moduluschecking/pull/15

mullikine / bonus 1 solution

Created December 3, 2019 02:29 — forked from erantapaa/bonus 1 solution

Daily Programmer 2017-09-22 Solution

mullikine / spacy-extraction-medley.py

Created December 4, 2019 08:06

Example code for extracting things with spacy and writing them out to text files and then reading them in again. https://gist.github.com/aparrish/18b9290fefdb4511f6909c2fd9dee12f


	# coding: utf-8

	# # Extracting and writing to files

	import spacy

	def flatten_subtree(st):
	return ''.join([w.text_with_ws for w in list(st)]).strip()

mullikine / from-plain-text.ipynb

Created December 4, 2019 08:12 — forked from aparrish/from-plain-text.ipynb

Semantic similarity chatbots from plain-text files ipynb

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mullikine / from-plain-text.py

Created December 4, 2019 08:12

Semantic similarity chatbots from plain-text files https://gist.github.com/aparrish/92c25d4f57eb7186670e03105a991508

	#!/usr/bin/env python3.6
	# -- coding: utf-8 --
	# coding: utf-8

	# # Semantic similarity chatbots from plain-text files
	#
	# By [Allison Parrish](http://www.decontextualize.com/)
	#
	# This needs copy, sorry :( :( :( But it works!

mullikine / predictive-text-concatenated-word-vectors.py

Created December 4, 2019 08:53

Predictive text with concatenated word vectors https://gist.github.com/aparrish/29574a35a51e955d4c6284da42b8f53a


	# coding: utf-8

	# # Predictive text with concatenated word vectors
	#
	# By [Allison Parrish](http://www.decontextualize.com/)
	#
	# This notebook demonstrates one way to implement predictive text like [iOS QuickType](https://www.apple.com/sg/ios/whats-new/quicktype/). It works sort of like a [Markov chain text generator](https://github.com/aparrish/rwet/blob/master/ngrams-and-markov-chains.ipynb), but uses nearest-neighbor lookups on a database of concatenated [word vectors](https://github.com/aparrish/rwet/blob/master/understanding-word-vectors.ipynb) instead of n-grams of tokens. You can build this database with any text you want!
	#
	# To get this code to work, you'll need to [install spaCy](https://spacy.io/usage/#section-quickstart), download a [spaCy model with word vectors](https://spacy.io/usage/models#available) (like `en_core_web_lg`). You'll also need [Simple Neighbors](https://github.com/aparrish/simpleneighbors), a Python library I made for easy nearest neighbor lookups:

mullikine / predictive-models.ipynb

Created December 4, 2019 08:56 — forked from aparrish/predictive-models.ipynb

Predictive text and text generation notebook. Written for Code Societies at SFPC, summer 2018. Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/

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mullikine / latency.txt

Created December 14, 2019 01:43 — forked from bafna/latency.txt

Latency Numbers Every Programmer Should Know

	Latency Comparison Numbers
	--------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

mullikine / docker-help.md

Created January 26, 2020 01:52 — forked from bradtraversy/docker-help.md

Docker Commands, Help & Tips

$ docker

mullikine / txt

Created February 14, 2020 02:56 — forked from wattry/txt

Docker Cheatsheet

	# Images
	docker build -t friendlyname . # Create image using this directory's Dockerfile
	docker image ls -a # List all images on this machine
	docker image rm <image id> # Remove specified image from this machine
	docker image rm $(docker image ls -a -q) # Remove all images from this machine
	docker image prune # remove dangling images
	docker tag <image> username/repository:tag # Tag <image> for upload to registry
	docker push username/repository:tag # Upload tagged image to registry
	docker run username/repository:tag # Run image from a registry

Shane Mulligan mullikine

Docker Commands, Help & Tips