• mbp16
  • screen sharing
  • finder go to network
  • ifconfig | grep inet
• Lunar Calendar
  • https://www.travelchinaguide.com/intro/focus/calendar.htm
  • https://www.bbgender.com/gregorian-lunar-calendar-converter/
• courses
  • https://bnmin.github.io/nlp2021sp/index.html
  • https://tinman.cs.gsu.edu/~raj/4330/sp20/
• Research
  • http://plato.asu.edu/guide.html
  • https://users.cs.duke.edu/~rongge/research.html
• piano
  • http://www.juliawu.me/about/#Voltaire

December

November

Today, an interviewer finally asked the question I've been waiting for: Can you explain what a p-value means in the context of an A/B Test?

My answer:

The p-value represents the conditional probability that we would arrive at our results (or more extreme results) given that the null hypothesis is true.

If this probability is less than our alpha, there are low odds of finding our results under the conditions of the null hypothesis, and we can reject it.

If the p-value is greater than our alpha, we fail to reject the null hypothesis.

Assuming the alternative hypothesis represents a new feature we're testing, a low p-value indicates that this feature is achieving its goal.

October

• https://research.utk.edu/oried/2022/09/29/qa-with-artificial-intelligence-expert-lynne-parker/

September

It's kinda crazy that I use dp, tries, segment trees, Fibonacci heaps, binary search, linked lists, dfs, bfs, Fenwick trees, kmp, top sort, Edmonds-Karp, Floyd-Warshall, backtracking, sieve of Eratosthenes, union-find, Dijkstra's, and bitmasks every day on the job at Google.

Thank goodness these are the kinds of things that are tested in coding interviews. But to be clear, something I never do on the job is glue frontends and backends together with APIs and pass protos around.

August

• /Users/dwyk/d/s/m6/wk
• Conda & VirtualEnv

Services

• lsof -i -P | grep -i "listen"

• jupyter

• airflow

• lstudio

• http://localhost:8888/lab/workspaces/auto-1

• http://localhost:8080/lab/workspaces/auto-1

• http://localhost:8081/

AirFlow

airflow

• https://kirenz.github.io/codelabs/
• https://airflow.apache.org/docs/apache-airflow/stable/start/local.html
• conda create --name air22 --clone base
• pip install --upgrade pip
• pip install virtualenv

# Airflow needs a home. `~/airflow` is the default, but you can put it
# somewhere else if you prefer (optional)
export AIRFLOW_HOME=~/airflow

# Install Airflow using the constraints file
AIRFLOW_VERSION=2.3.3
PYTHON_VERSION="$(python --version | cut -d " " -f 2 | cut -d "." -f 1-2)"
# For example: 3.7
CONSTRAINT_URL="https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt"
# For example: https://raw.githubusercontent.com/apache/airflow/constraints-2.3.3/constraints-3.7.txt
pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"

# The Standalone command will initialise the database, make a user,
# and start all components for you.
airflow standalone

# Visit localhost:8080 in the browser and use the admin account details
# shown on the terminal to login.
# Enable the example_bash_operator dag in the home page

• regex
  • https://www.autoregex.xyz/
• dynamic programming
  • https://labuladong.gitbook.io/algo-en/
• spacy

conda install -c conda-forge spacy
conda install -c conda-forge spacy-model-en_core_web_sm

• http://www.javanibble.com
• https://www.mtsamples.com
• https://github.com/spotify/luigi