ProbableOdyssey | Blake Cook

One of my favourite CLI tools is FZF by junegunn.

I spend most of my time in the terminal these days, but it wasn’t always that way.

The Mythical Man-Month by Fred Brooks is widely regarded as one of the foundational books on software engineering.

It’s an exciting time for machine learning and AI.

ML experiments essentially boil down to a single script, for example: # ml_experiment.py # # Executed with `python ml_experiment.py` # def main(): dataloader = ...

Collaboration is often the bottleneck in software development.

The DRY principle is a widely accepted guideline for writing maintainable and readable code.

Recently someone sent me this article from futurism where the fintech company Klarna is reversing course on their drive to replace a large portion of their marketing workforce with AI over the last two years.

Editing /etc/fstab and the gcsfuse option -o allow_other solves the issue of users not being able to read a bucket mounted by gcsfuse during a startup script (which runs a root) Ordinarily one would mount a bucket on startup using a script like #!

Today we’ll illustrate how machine learning works in a lightning-fast tutorial on how to use PyTorch – one of the leading Python frameworks for machine learning.

Collaborating on projects using Git can be challenging – especially for scientists, academics, and those without a software engineering background.

The “Great Leap Forward” in 20th-century China was a period during which the economy contracted, and a famine ensued that killed between 15 and 55 million people.

In an effort to collate all my runtime options and parameters into a single source of truth, I initially created a src/globals.py file with the values and their defaults: import os FOO: str = os.get("FOO", "my_value") BAR: int = int(os.get("BAR", "55"))…

This is one of the tricky bugs I fixed relatively early in my career.

A list of functions can be composed using this pattern: # Some example functions def foo(x): return 3 * x def bar(x): return x + 3 def baz(x): return x**3 # Function to compose functions def compose(*funcs): def composition(x):

When I was on Tinder many years ago, the app prompted me to enter my Spotify details to my profile.

The hard part of teaching someone how to do machine learning usually isn’t the machine learning itself – it’s installing Python.

In some high school psychology classes, students are shown a video that demonstrates selective attention.

In my last post I explored using Python and nccurses to asynchronously render streamed content from curl.

Recently I was experimenting with writing a CLI interface from scratch.

This might be a bit of a basic one, but I was testing a codebase that implemented an ML model behind an endpoint with Flask at /api/redact, which accepted a json input and returned a json output.

I was recently pointed in the direction of this paper: Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance Having looked through the paper, they seem to really only make a claim

There’s a few situations that can arise when you’d want to reverse committing a file to your repo: Accidentally committing secrets Accidentally committing data or artifacts and running into a Git LFS warning when pushing Accidentally committing a Jupyter…

I had a project that required updating a legacy web app (built with dash) hosted on GCP Cloud Run.

Makefiles have been a staple of the C/C++ toolchain for decades.

Threads can be used to provide a progress wheel for long-running python functions import concurrent.futures as cf import sys import threading import time def slow_function(x: int | float) -> str: time.sleep(x) return f"Operation took {x} seconds"

Tuner is a web app I’ve been working on the side for the past 3 months.

I was setting up an instance with the clearml-server for my team to start tracking their data science experiments and versioning their datasets and tracing artifacts.

Use git diff with the --no-index flag: git diff [<options>] --no-index [--] <path> <path> Previously I’ve found this useful for when comparing ML configuration files where one is a modified copy of another.

Something I’ve wanted to do for a while is to make a tool to make it easier to write content for this blog.

Ever wanted to have multiple git branches checked out at the same time?

This is the start of a project I’ve wanted to do for a while and only just got around to now.