I find calculus inherently harder to learn than discrete math and less intuitive. Am I the only one?
Chirps
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I haven't shared any books here so far. I'd like to start off with one of my favourite technical books - Fluent Python by Luciano Ramalho.
The book is written in a conversational style, is quite comprehensive and teaches you to write idiomatic Python. It also links to primary sources where you can learn about the history of Python.
Admittedly, I've only read around 50% of the book and mostly use it as a reference whenever I need to learn a specific topic. I highly recommend it if you want to level up as a Pythonista.
In the good old days, programming language creators designed the workhorse integer data type.
As soon as the computer industry started minting billionaires, all of a sudden, we started needing those esoteric big ints as well.
On a related note, it's amazing how Google's Map Reduce framework was created around the same time as Google's IPO. Google needed a new way to count all that money.
I'm getting suboptimal results when vibe-coding CSS animations. Can talented open-source developers please rise up to the occasion and work for free to help develop better training data for all of us? Cheers!
When I worked as an instructor at a coding bootcamp conducted at a co-working space back in 2017, I hoped my students would gain expertise in Python and Django.
Instead, they learned foosball trick shots, copying code off the Internet, taking way too many coffee breaks, going on PubG / Fortnite quests and flirting with each other.
Don't get me wrong, I love the web, and enjoy web development. But building your nth web app from scratch that has no users yet is mostly grunt work, apart from that one piece of secret sauce that's unique to your app.
I did not pay much attention to the AI wave for a while, even after the ChatGPT moment.
But it all changed a few months ago when I vibe coded a couple of web pages on Lovable and the generated UI looked much better than what I could have developed on my own. I got excited about learning how GenAI applications work under the hood and started exploring basic Machine Learning concepts as well.
However, I believe using creators' data without their permission is unethical. At the same time, as a software developer, I don't want to be left behind and not rely on AI tools. I'm sure most knowledge workers are facing this ethical dilemma.
After watching an interview with Karen Hao about her book, The empire of AI, the moral conundrum feels more real.
This classic Green Day song sums up my "Too much dopamine, too little oxytocin" life.
I'm evaluating pyinfra in order to deploy TechTrack, my Hacker News client app.
pyinfra's documentation is still sparse, so I'm using DeepWiki, yet another AI tool, to get an overview of how the codebase is structured and what each major component does. DeepWiki looks very promising and provides an easy ramp when jumping into new codebases.
Check out both pyinfra and DeepWiki and tell me what you think!
Now listening to Ray of Light by Madonna