Mar 31, 2025 • 3 min read

Styling Hacks

Tailwind CSS

Utility-first classes—p-4 bg-blue-600 etc.—replace custom CSS.
Responsive helpers (sm:, md: …) kick in automatically when you resize.

Bootstrap

Uses a content wrapper (<div class="container-xxl">) for layout.
Handy if you need pre-built navbars, alerts, grids fast.

Theme / Layout Shell

The outer chrome (header, footer, sidebar).
A responsive theme adapts as the viewport changes; no manual media queries.

Jekyll Layout Cascade

Each page declares its layout in YAML front-matter.
Layouts can nest, so a post layout can extend default.

Mar 31, 2025 • 2 min read

DevOps

Legal & Ethical Concerns Hacks

Popcorn Hack 1 – IP Basics

Patents, copyrights, trademarks, DRM → protect creators and motivate innovation.

Popcorn Hack 2 – MIT Licence

“Do whatever you want, just keep my copyright & licence notice.”

Popcorn Hack 3 – Creative Commons & Fair Use

CC licences pre-declare remix rules; fair-use exceptions cover education, parody, archives, public domain.

Popcorn Hack 4 – Avoiding Infringement

Create your own assets or verify licences/permissions before publishing.

Homework Hack – My Licence Choice: Apache 2.0

Retains my copyright.
No obligation to open-source future changes.
Still lets others study/extend the code (with attribution).
See licence file → LICENSE

Extra Credit – MediPulse Project

ML-powered hospital recommender for San Diego. Apache 2.0 lets us innovate safely while remaining transparent to partners.

Apr 8, 2025 • 4 min read

DevOps

Lists Hacks 🐍

movies = ['minecraft movie', 'star wars', 'the matrix', 'interstellar']
print(movies)

movies[1] = 'spiderman'      # replace an item
print(movies)

movies.append('endgame')      # add to the end
print(movies)

ages = [15, 20, 34, 16, 18, 21, 14, 19]
ages_voting = [num for num in ages if num >= 18]
print(ages_voting)      # ➜ [20, 34, 18, 21, 19]

ages = [15, 20, 34, 16, 18, 21, 14, 19]
ages_voting = [num for num in ages if num >= 18]
print(ages_voting)      # ➜ [20, 34, 18, 21, 19]

import pandas as pd
df = pd.read_csv("Spotify_2024_Global_Streaming_Data.csv")
big_hits = (df.dropna()
              .query("`Total Streams (Millions)` > 10")
              .sort_values(by="Total Streams (Millions)", ascending=False))

top_streams = big_hits["Total Streams (Millions)"].tolist()
print(big_hits.head())

Review Q&A What are Python lists and how do you manipulate them? Ordered, mutable sequences. Add (append, insert), remove (pop, remove), slice, sort, or rebuild with comprehensions.

Real-world filter scenario? Spotify filtering tracks with > 10 M streams before recommending them to users.

Why study algorithm efficiency? Large-scale filters need to be fast and memory-efficient to keep user experiences smooth and infrastructure costs low.

pgsql Copy Edit

List homework blog

List 2, Digital Divide