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Running in Shorts on Warm Christmas Eve (and Other Seasonal Confusions)

Brian’s fitness journal after a brain stroke

It’s eerily warm this Christmas Eve—warm enough that I ran in shorts. Seasonally inappropriate, yes. Thermodynamically accurate, also yes.

When I woke up, my nose felt congested. After one decisive blow, it started bleeding. Festive. I’m blaming the unusually low humidity we’ve had over the past few weeks. My skin has also been itchy enough to qualify as a minor distraction, though lotion keeps things from escalating.

This Christmas in Nashville has been strange. One day we hit the high 60s Fahrenheit, which immediately reminded me of Vancouver, where we lived briefly. Vancouver summers rarely go above 72–73°F, so a nearly 70-degree day there feels like a heatwave. Today had that same confused energy—winter pretending to be spring.

I did pause to worry about the nosebleed. These days, anything involving blood earns a moment of concern. Nosebleeds can signal high blood pressure, but after checking, mine was fine. Dryness seems to be the real culprit.

My wife, ever the source of oddly specific medical trivia, once told me she used to get nosebleeds from eating too much chocolate. She also had frequent nosebleeds during sudden temperature or pressure changes—so frequent, in fact, that she had the nasal veins cauterized in her teens. She hasn’t had a nosebleed since, though she remains cautious around chocolate and rapid weather shifts.

I worry more than I used to. Knowledge does that to you. Once you know what could be wrong, your brain insists on checking every possibility.

Unfortunately, my run didn’t go particularly well either. I felt distracted and held back, partly because I was worried my nose might start bleeding again if I pushed too hard. Running in shorts usually feels like an automatic speed boost, but not today.

Still, it wasn’t a total loss. I matched Monday’s pace, which means there’s at least some improvement from earlier this week. And with three more runs before the week ends, I still have chances to hit my target pace.

So:

  • Warm Christmas Eve ✔️
  • Shorts in December ✔️
  • Festive nosebleed ✖️
  • Perfect run ❌

Not ideal—but manageable. And on Christmas Eve, that’s good enough.

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Adding a Splash of Genre Flair

Day 48 of 100 Days Coding Challenge: Python

After days of tinkering with this book tracker like a mad librarian with a barcode scanner, I realized something important: I don’t want to be the poor soul manually updating the genre-color dictionary every time I read something new. Let’s face it—“User-friendly” doesn’t mean much when the only user is me, and I still find myself muttering, “Why isn’t this thing purple?” every time I forget to update the genre list.

So today’s quest? Build a feature that lets me add new genres and assign them pretty little colors on the fly—without breaking the whole program or sending myself into a debugging spiral. It’s not just about aesthetics. It’s about freedom. Genre freedom. Color-coded, quarter-sorted, reader-powered freedom.

Today’s Motivation / Challenge

Sometimes, your biggest obstacle is your past self—the one who hardcoded things thinking they’d never change. Past Me didn’t expect Present Me to branch out from Fantasy and Sci-Fi. But here we are, reading historical dramas and the occasional poem. Rather than letting my program judge me for my evolving tastes, I figured it was time to teach it some manners. Today’s challenge was about flexibility: giving my tracker the power to grow as my reading list does.

Purpose of the Code (Object)

This update makes the book tracker more interactive and adaptable. Instead of manually editing the genre-color dictionary every time I pick up a new type of book, I can now add a new genre and assign it a color from within the program itself. It’s one small line of code for me, one giant leap for my sanity.

AI Prompt:

“Add a function that allows the user to update the genre-color dictionary during runtime without editing the code directly.”

Functions & Features

  • Add and assign a new genre with a custom color
  • Auto-updated pie and bar charts include the new genre
  • Prevents duplicate genres by checking existing entries
  • Keeps your genre list as fresh as your reading habits

Requirements / Setup

You’ll need:

pip install matplotlib pandas

Python 3.8+ is recommended, but it should work on most modern versions.

Minimal Code Sample

def add_new_genre_color(genre_color_dict, new_genre, new_color):

    if new_genre in genre_color_dict:

        print(f”{new_genre} already exists with color {genre_color_dict[new_genre]}.”)

    else:

        genre_color_dict[new_genre] = new_color

        print(f”Added new genre: {new_genre} with color {new_color}.”)

This simple function updates the dictionary in real time, without opening the script file.

Book Tracker

Notes / Lessons Learned

One of the biggest challenges I’m facing now is the trail of code left behind by my earlier, less organized self. Back then, I hardcoded colors like a kid with a new box of crayons and zero aesthetic restraint. The result? A genre list that doesn’t always match my pie chart, or my mood.

Today, I tested the new feature by adding “Mystery” as a genre. I even fabricated a “Mystery Book” just to make sure it worked. Spoiler: it did. And yes, it showed up beautifully in the pie chart too. Eventually, I might scrap the rigid GENRE_COLORS dictionary altogether and switch to a fully dynamic model—one that builds as I read. One genre at a time.

Optional Ideas for Expansion

  • Save updated genres and colors to a JSON file so they persist after the script closes
  • Add a feature to suggest random colors if the user can’t pick one
  • Let users rename or delete genres via a menu option

Day 47
Day 49

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Quarterly Exhaustion: Which Genres Drain Your Brain?

Day 47 of 100 Days Coding Challenge: Python

I read for all kinds of reasons—personal growth, research, curiosity, or just to escape reality for a few hundred pages. And because of that, my bookshelf looks like it belongs to someone with multiple personalities. I’ve always known that I gravitate toward science fiction and fantasy (Stormlight Archive, anyone?), but lately I’ve had a sneaking suspicion: fantasy books might secretly be way longer than anything else.

So today, I decided to test that theory by adding a feature to calculate the average number of pages per genre per quarter. If fantasy is the literary equivalent of leg day, I want to know. And if I’m dragging by the end of Q3, it’s probably Brandon Sanderson’s fault.

Today’s Motivation / Challenge

You know that feeling when you finish a 900-page fantasy tome and think, “Why am I so tired?” Well, data can tell you why. Today’s challenge was to turn that vague feeling into a measurable trend. Think of it like a quarterly report—but instead of sales revenue, we’re analyzing book-induced fatigue.

Purpose of the Code (Object)

This code analyzes your reading log and breaks down the average number of pages you’ve read per genre, grouped by quarter. It helps you discover seasonal trends in your reading habits—like whether you go hard on historical fiction in spring or slow-burn through sci-fi during winter.

AI Prompt:

Add the function to calculate the average page count per Genre, grouped by quarter.

Functions & Features

  • Calculates average page count per genre, grouped by quarter
  • Displays the data in both printed table form and as a bar chart
  • Helps you discover which genres are “page-heavy” per season
  • Adds one more analytical layer to your personal book dashboard

Requirements / Setup

pip install pandas matplotlib

Minimal Code Sample

df[‘Quarter’] = df[‘Date Finished’].dt.to_period(‘Q’)

avg_pages = df.groupby([‘Quarter’, ‘Genre’])[‘Pages’].mean().unstack().fillna(0)

avg_pages.round(1).plot(kind=’bar’)

This snippet groups books by quarter and genre, calculates the average pages, and plots a bar chart.

Book Tracker

Notes / Lessons Learned

I’m officially over my fear of virtual environments—no more mysterious typos or wondering why the environment won’t activate. Adding this new function was pretty smooth. One thing I still find slightly annoying is juggling the menu options. Now that “exit” is option 7 and this new analysis is 8, my orderly brain twitches a little. But hey, renumbering is just cosmetic, right?

Honestly, the more I work on this program, the more I feel like it’s becoming mine. I’m not just learning Python anymore—I’m building something I actually want to use. That’s got to be the most rewarding part. Well, that and finally proving that fantasy books are absolute beasts.

Optional Ideas for Expansion

  • Let users filter by author to see who writes the longest tomes
  • Export the genre-per-quarter averages to a CSV file for archiving
  • Add a “reading fatigue index” based on total pages per week

Day 46
Day 48

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Holiday Baking for Family, and the Quiet Joy of Making Pie

Brian’s fitness journal after a brain stroke

Today’s most important task wasn’t glamorous—but it was meaningful:
I peeled, sliced, and macerated apples for tomorrow’s apple pie.

We’re heading to my sister’s house for a Christmas party, and my official contribution is two pies: one apple, one pumpkin. Sadly, my mother won’t be able to come this year because she has the flu and doesn’t want to share it with the rest of us. That’s disappointing—but also considerate. Germs are not festive.

I was still excited, though. I used this same apple-pie process for Thanksgiving, and my brother-in-law—a genuinely excellent cook—complimented it. That is high praise. When someone who regularly feeds everyone beautifully enjoys something you made, it hits differently.

So yes, I’m happily attempting a repeat performance.

I always prep pies two days ahead. Pies, like good ideas, improve with a little patience. The day before baking, I macerate the apples—letting sugar and spices pull out their juices and soften them overnight. Tomorrow, all I have to do is assemble and bake.

The pumpkin pie required a small compromise this year. We didn’t make our own pumpkin purée like usual. Everyone was too busy, and even applesauce didn’t happen. So we bought purée from the store. Is it as romantic? No. Is it acceptable? Absolutely.

I love baking for family gatherings. It’s how I show up. I’ve loved baking since I was a teenager, and after my brain stroke—when I couldn’t even draw a proper clock—I still baked my wife a birthday cake with my father’s help. Baking gave me structure, sequencing, and purpose. In a very real way, it became part of my rehabilitation.

There’s something deeply grounding about measuring, mixing, waiting, and watching something become whole.

I can’t believe the year is almost over. The best parts of the holidays are still ahead. My wife is already excited to see her niece—she only gets that chance during family gatherings because life is so busy for everyone.

For now, I’m content with bowls of spiced apples resting quietly in the fridge, doing their slow magic.

It feels good to contribute something made with care to people I care about—even if it’s just pie.

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Quarters, Colors, and the Gospel of Granular Reading Data

Day 46 of 100 Days Coding Challenge: Python

I’ve been tinkering with this book tracker for almost five days now, and let me tell you—things are starting to look serious. Not “move-in-together” serious, but close. As the code grows chunkier, I’ve been extra cautious about how I add each new function. Rather than throwing in five features and hoping nothing explodes, I take the accountant’s route: one entry at a time, reconciled and balanced.

Why the sudden urge to filter by quarter? Well, in the world of corporate accounting—where I live and breathe—we worship the fiscal calendar. Quarterly reporting is practically a sacrament. Automotive manufacturers slow down in summer and December, and trust me, so do I (with a good fantasy novel in hand). But that’s just a hunch. I wanted cold, clean, spreadsheet-worthy evidence that I read more Gothic tales as leaves fall and pumpkins rise. Gut feeling isn’t GAAP-approved. So today, we brought seasonal analysis into the Book Tracker universe.

Today’s Motivation / Challenge

Today’s challenge was simple but essential: how do you filter your data like a pro and still keep your chart options open? I wanted to analyze my reading habits by year and season, just like I do with budgets and forecasts. This time, instead of revenue trends, I’m tracking fantasy epics and Gothic drama. Same logic. Less coffee.

Purpose of the Code (Object)

This update adds the ability to view genre summaries filtered by year and, optionally, by quarter. The user can pick a year (and a quarter if they want) and choose between a pie chart or a bar chart to visualize what they’ve been reading. It’s like a financial report, but for books—and it’s way more colorful.

AI Prompt: 

“Add a menu option that lets the user filter the genre summary by year and quarter, and display the data in either a pie or bar chart based on their choice. Keep the code clean and beginner-friendly.”

Functions & Features

  • Lets users filter reading data by year and quarter
  • Visualizes genre distribution as either a pie chart or bar chart
  • Builds on existing color-coded genre system
  • Detects invalid input and guides the user gently (and politely)

Requirements / Setup

pip install pandas matplotlib

Python 3.9 or later recommended for date handling and plotting.

Minimal Code Sample

year = int(input(“Enter year to analyze: “).strip())

df = df[df[‘Date Finished’].dt.year == year]

quarter_input = input(“Enter quarter (1–4) or press Enter to skip: “).strip()

if quarter_input:

    df = df[df[‘Date Finished’].dt.quarter == int(quarter_input)]

This snippet filters your book log by the chosen year and optional quarter.

Book Tracker

Notes / Lessons Learned

The more I add features, the more I see the architecture underneath the code. This little book tracker has evolved into a proper app—with clearly separated layers: imports, genre definitions, graphing logic, and menu options. Honestly, that’s kind of magical. It’s like finally understanding how your espresso machine works after years of just pressing the button.

There’s a quiet thrill in building something you actually want to use. This wasn’t just another tutorial—I created a tool tailored to my reading life. And somewhere between bug-fixing and plotting pie charts, I realized: 46 days in, I’ve gone from curious coder to confident tinkerer. Not bad for someone who thought Python was just a snake when this all started.

Optional Ideas for Expansion

  • Add filters for author or language (in case you read in multiple languages)
  • Include average pages per genre per quarter to see which genres exhaust you
  • Export filtered results as a CSV report for sharing or archiving

Day 45
Day 47

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Pie, Bar, and Book Nerd Bliss

Day 45 of 100 Days Coding Challenge: Python

I’m still tinkering with my book log program—not just because it’s part of my Python learning project, but because, let’s be honest, I want this thing to actually work. I mean, really work. As in “track-my-life’s-reading-habits-and-give-me-pretty-charts” work. Reading is sacred in my world, and keeping a log isn’t just a nerdy pastime—it’s a habit that brings me joy, perspective, and a little control over my chaos.

Today’s feature? I added a toggle so users (okay, me) can switch between a pie chart showing total genre distribution and a bar chart showing monthly genre patterns. Why? Because I read more Gothic and fantasy novels in October, and it’s satisfying to see that mood shift visualized. Also, toggles are cool. They make me feel like I’ve built a dashboard, not just a Python script.

Today’s Motivation / Challenge

Ever feel like you’ve built something, and it’s almost useful… but not quite delightful yet? That was me. So, today’s mission was simple: add a little UX sparkle. Let the user choose how to view their genre stats—like a buffet for data nerds. Pie or bar? Whichever suits the literary mood.

Purpose of the Code (Object)

This code adds a flexible summary feature to a book tracking app. It lets users choose between a pie chart showing the overall breakdown of genres they’ve read or a bar chart showing how many pages they read per genre by month. It’s a compact, practical way to visualize your reading habits—and yes, it’s way more fun than a spreadsheet.

AI Prompt:

“Add an option to toggle between a pie chart and a bar chart when viewing genre summaries in a Python book log app.”

Functions & Features

  • Add a book and track its genre, author, page count, and completion date
  • Import books from a CSV file
  • View a genre summary chart as either a pie or bar graph
  • Filter chart data by genre or month
  • See trends in pages read by month and genre

Requirements / Setup

pip install pandas matplotlib  

Minimal Code Sample

def show_genre_summary_chart():

    print(“Choose chart type:”)

    print(“1. Pie Chart (Overall Genre Distribution)”)

    print(“2. Bar Chart (Pages per Genre by Month)”)

    choice = input(“Enter 1 or 2: “).strip()

    if choice == “1”:

        plot_genre_pie_chart()

    elif choice == “2”:

        plot_progress()

    else:

        print(“Invalid choice.”)

This function lets the user decide how they want their genre stats served: sliced (pie) or stacked (bar).

Book Tracker

Notes / Lessons Learned

I’m getting much smoother with activating my virtual environment. Looking back, I can’t believe I fumbled so much with cd errors and typos just last week. Progress!

Today, even the smallest update required a little code renovation. First, I added a new function (show_genre_summary_chart) above the main() block. Then I gave the user a choice:

print(“Choose chart type:”)

print(“1. Pie Chart (Overall Genre Distribution)”)

print(“2. Bar Chart (Pages per Genre by Month)”)

Finally, I updated the main menu to replace the old genre graph option with this smarter version. It works like a charm.

Honestly, I’ve used so many apps over the years without fully appreciating what went into them. Now that I’ve written code that sort of behaves nicely, I’m grateful to all the developers who did this before the age of AI. I’ve got help, but they? They had raw logic, coffee, and probably a lot of bugs.

Optional Ideas for Expansion

  • Add a date filter for the pie chart (e.g., genre distribution by year or season)
  • Let users export the pie/bar charts as PNG files for their own book reports
  • Display top 3 most-read genres alongside the charts

Day 44
Day 46

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When Getting Out of Bed Is the First Workout of the Day

Brian’s fitness journal after a brain stroke

Some mornings invite you to crawl back under the covers and negotiate with the universe. Today was one of those mornings. I was still half-asleep when the alarm went off, but I got up anyway—mostly because I’ve learned that negotiating with fatigue never ends well.

Ever since my brain stroke, sleep has been… complicated. In the early days, I could sleep almost indefinitely. My occupational therapist responded by giving me a very firm schedule, and my wife enforced it with the seriousness of a NASA launch director. Her rule was simple: never give up your agency. Losing control of your body is hard enough—don’t also surrender control of your will.

Kafka would’ve understood.

Being trapped in a body that doesn’t cooperate is emotionally brutal. At first, I was scared. Insecure. Stripped of mobility and confidence all at once. But slowly, painfully, I got it back. The will to live returned. I realized my wife needed me—but more importantly, I needed me.

Now, most of what I do is for myself: running, strength training, and learning. People can change. I’m living proof of that. So even on tired mornings, I stick to my routine.

Today was no exception.

I made my way to my office, fed our cat, and started my morning exercises before breakfast. My wife had already left for work at 6:30 a.m., as usual, powered by her own internal stoic engine.

Being Monday, the schedule called for pull-ups.

I knocked out the first 10 without dropping off the bar, then after a few seconds of dramatic oxygen negotiations, finished the remaining 9. Nineteen total. Next week’s target is 20, which conveniently marks the end of my weekly increase streak.

That opens an interesting question:
Do I push further into three sets of ten?
Or do I hold the line and focus on maintaining this strength?

I have two weeks to decide. That feels fair.

For now, I’m allowing myself a short pause before the next act of today’s production: my run. Fatigue may still be hanging around, but discipline has already clocked in for work.

And that makes all the difference.

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Slicing Up Genres (With Pie, Not Precision Tools)

Day 44 of 100 Days Coding Challenge: Python

Yesterday’s mission? Make a colorful genre-based bar chart that didn’t look like a monochrome mess. After some wrestling with the code (and maybe whispering a few stern words to matplotlib), I decided to step back and take a wiser approach—add one function at a time. Think of it like seasoning a dish: too many spices at once and you can’t tell if the problem is the salt or the cinnamon.

So today, instead of fixing every chart in one go, I added something deliciously simple: a pie chart. Not the edible kind, unfortunately, but a visual feast for genre distribution. It’s a nice way to get a bird’s-eye view of what I’ve been reading—am I balanced, or living in a fantasy bubble? This project is starting to feel like a real book journal app, and that’s both exciting and mildly terrifying.

Today’s Motivation / Challenge


Histograms are great if you’re obsessed with dates (hi, it’s me), but sometimes you just want to know what you’ve been feeding your literary diet. A pie chart offers a big-picture summary of your reading preferences—like stepping back from the bookshelf and realizing 60% of it is purple dragon covers. Today’s goal? See the forest, not just the monthly genre trees.

Purpose of the Code (Object)


This updated code adds a neat little pie chart showing the proportion of each genre in your reading log. It uses the data already stored in your CSV file and the color settings from yesterday’s code. The result? A clean visual breakdown of your book habits, so you can brag about how “diverse” your library is—or realize it’s time to mix in something besides historical fiction.

AI Prompt:


Make it readable. Make it modular. And for the love of Python, add one feature at a time.

Functions & Features

  • Add books to a CSV file with metadata like title, author, pages, and genre
  • Show reading progress over time (bar chart by month and genre)
  • View genre-specific reading patterns
  • Display a pie chart of your overall genre distribution
  • Import book lists from another CSV file

Requirements / Setup

  • Python 3.x

Install the required library:

pip install matplotlib pandas

Minimal Code Sample

def plot_genre_distribution():

    df = pd.read_csv(FILE_NAME)

    df[‘Genre’] = df[‘Genre’].str.strip().str.title()

    genre_counts = df[‘Genre’].value_counts()

    colors = [GENRE_COLORS.get(g, ‘gray’) for g in genre_counts.index]

    plt.pie(genre_counts, labels=genre_counts.index, colors=colors, autopct=’%1.1f%%’)

    plt.title(‘Overall Genre Distribution’)

    plt.axis(‘equal’)

    plt.tight_layout()

    plt.show()

This function creates a genre pie chart based on the books you’ve read.

Book Tracker

Notes / Lessons Learned


What I added today was the code to create a pie chart, showing the distribution of genres. Then, I gave it a shiny new menu option. It worked surprisingly smoothly—probably because I didn’t try to fix five other things at the same time.

I’m finally getting used to virtual environments, which means fewer self-inflicted typos and wild goose chases through the wrong folders. The genre color dictionary I built yesterday made the pie chart almost too easy. That’s a nice surprise in coding: sometimes past-you actually set present-you up for success.

Also, here’s a bit of wisdom from building queries in SAP (and now in Python): change one thing at a time. If you break something, at least you know which line of code betrayed you.

Optional Ideas for Expansion

  • Let users toggle between the pie chart and the bar chart from the same menu
  • Allow subgenre tracking with drill-down options
  • Export the pie chart as an image to share or embed in a blog

Day 43
Day 45

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Color Me Curious: Giving Genre Some Style

Day 43 of 100 Days Coding Challenge: Python

I kept tinkering with the tracker I built yesterday, chasing a dream—a dream of colors. Specifically, I wanted each book genre to appear in a different color in the graph from my Python book tracker genre visualization. That’s when I realized: my genre list was… how should I say it… ambitious. Turns out, tracking every tiny genre variation from “Science Memoir” to “Neo-Futurist Romance” makes the visualization look like a unicorn exploded on a bar chart.

To simplify, I added a new column for broader genres—less chaotic, more chart-friendly. My vision was to see a beautiful stacked histogram by genre color, but the code didn’t quite cooperate at first. It also made me realize something bigger: if I want this program to be truly useful, I’ll have to rethink how I track my books moving forward. Retroactively cleaning up all that old data? That’s a future-me problem. For now, this code is more about going forward than going backward.

Today’s Motivation / Challenge

If data is the new oil, then visualizing it is like turning that oil into fuel. Today’s challenge was about clarity—seeing reading habits at a glance. Colors help your brain grasp information faster, and genre-based colors are like putting your bookshelf on a rainbow diet. Also, it’s just more fun to look at.

Purpose of the Code (Object)

This program reads your book-tracking log and turns it into a visual chart, showing how many pages you’ve read each month. It highlights your genre diversity using color-coded bars, so you can instantly see if you’re stuck in a romance rut or finally diversifying your literary diet.

AI Prompt:

Add the existing Python code a color code for the following: Fantasy, Science Fiction….. History Fiction. 

Functions & Features

  • Add a new book to your reading log
  • Import books from another CSV file
  • List all books you’ve logged
  • Show a reading progress chart by month
  • Show a stacked genre-colored chart to compare reading trends

Requirements / Setup

You’ll need:

pip install pandas matplotlib

This code runs on Python 3.9+ (but most 3.x versions will do).

Minimal Code Sample

df[‘Genre’] = df[‘Genre’].str.strip().str.title()

df = df[df[‘Genre’].isin(GENRE_COLORS.keys())]

Standardize the genre format and filter out undefined genres so colors work correctly.

Book Tracker

Notes / Lessons Learned

Today, I had a brand new flavor of problem. You know the kind—where the code technically runs, but nothing happens? Turns out, I completely forgot to add:

if __name__ == “__main__”:

    main()

So the script just… stared at me, judging my oversight in silence. Classic rookie move.

But wait, there’s more. My bar chart decided that “colorful” meant “one color only.” Why? Because my genre strings were a mess—some had leading spaces, others were lowercase, and a few were practically jazz solos. After several rounds of trial and error (and a little sulking), I discovered this trick:

df[‘Genre’] = df[‘Genre’].str.strip().str.title()

instead of the other way around. And when I added this line to filter only the genres I’d actually assigned colors to:

df = df[df[‘Genre’].isin(GENRE_COLORS.keys())]

it finally worked. The chart bloomed into the colorful visualization I’d hoped for—like spring after debugging winter.

Optional Ideas for Expansion

  • Add a pie chart showing the overall genre distribution
  • Include a “favorite author” stat based on frequency
  • Let users define their own genre-color mappings in a config file

Day 42
Day 44

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Why My Kidneys Just Banned My Favorite Melons

Brian’s fitness journal after a brain stroke

today my kidneys staged a small but decisive coup.

My nephrologist’s office called to inform me that my latest bloodwork shows I’ve been consuming too much potassium. The culprits? Cantaloupe and honeydew. Two of my favorite, innocent-looking fruits. Apparently, they’ve been quietly plotting against me this whole time.

When your kidneys aren’t working properly, the list of things you have to watch becomes impressively long. Protein. Potassium. Phosphate. Even foods that sound healthy—like spinach and other green vegetables—can become problematic. You don’t just eat what’s “good”; you eat what your kidneys will tolerate.

Over the summer, I was told I was eating too much icecream (sugar), so melons became my workaround. Light, refreshing, hydrating—what could go wrong? Well, potassium. That’s what.

Fortunately, it’s not summer anymore, and I’m not doing as much physical activity. That means I can get away with smaller snack volumes, which makes adjusting a little easier.

Kidney disease is not a casual hobby. It demands attention, planning, and frequent dietary grief. So now, melons are off the table—for a while, at least.

After some research, I discovered that strawberries and carrots are much friendlier options for a low-potassium diet. My wife, always the strategist, suggested rotating foods instead of banning them forever: melon one week, berries the next. That way, nothing gets permanently exiled unless it absolutely has to.

Still, losing another favored snack stings. And it’s not just melons. Cheese and chocolate—two of life’s most reliable joys—also need to be carefully rationed when kidneys are involved. Apparently, the universe believes character is built through dietary restraint.

So for now, it’s goodbye to honeydew and cantaloupe. Hello to berries and carrots.

I’ll keep paying attention to potassium levels, rotating foods when possible, and doing my best to eat in a way that keeps my kidneys cooperative—even if they have a flair for dramatic food bans.

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