Harald Kirkerød haraball

Speaker Rider

Last updated 14 April 2021

What is a speaker rider?

As speaking comes with immense privilege, I have crafted a speaker rider to set expectations and boundaries around my engagement. I am grateful to all the conference organisers who have brilliantly hosted me. I would love to continue to exercise this privilege to speak at conferences, and use this privilege to make the landscape more accessible and beneficial to tech's most historically excluded and marginalised communities.

Considerations

😫 I provide a lot of explanations for those of you who never had to consider these things. Most thoughtful conferences I've attended check most of these boxes intrinsically, particularly when conference runners are experienced speakers. They get it.

Genomics - A programmer's guide.

Andy Thomason is a Senior Programmer at Genomics PLC. He has been witing graphics systems, games and compilers since the '70s and specialises in code performance.

https://www.genomicsplc.com

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	#!/bin/bash
	#
	# opkg requirements :
	# opkg install 7z git jq
	#
	# git clone https://github.com/wordset/wordset-dictionary/
	#
	# assumes ~/xochitl-data links to /home/root/.local/share/remarkable/xochitl/
	#
	# warning ~/epubTemplate/ is required. See instead https://gist.github.com/Utopiah/119f5c96fae048609a2091f7f3d81f53

	import os
	import pickle
	import warnings

	import numpy as np
	import pandas as pd
	from sklearn.model_selection import train_test_split
	from tensorflow.keras.callbacks import EarlyStopping
	from tensorflow.keras.layers import Dense
	from tensorflow.keras.layers import Dropout

	#!/usr/bin/env python

	import math
	import sys
	from moviepy.editor import AudioClip, VideoFileClip, concatenate_videoclips



	# Get average RGB of part of a frame. Frame is H * W * 3 (rgb)
	# Assumes x1 < x2, y1 < y2

	#!/bin/sh

	# use this to get predictions on a test.csv located in BERT_DATA_DIR

	export OUTCOME={classification_task_name}
	export NOTEBOOK=/notebooks # don't change me

	docker run --runtime=nvidia -it --rm \
	-v $(pwd):$NOTEBOOK/ \
	-e "BERT_BASE_DIR=$NOTEBOOK/uncased_L-12_H-768_A-12" \

	// "creature" by dave :)

	int[][] result;
	float t, c;

	float ease(float p) {
	return 3pp - 2pp*p;
	}

	float ease(float p, float g) {

	Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc.

	Now on pip! `pip install arrgh` https://github.com/nmwsharp/arrgh