A brief infrastructure note on reducing attack surface during routine maintenance
RewindOS is built to prioritize observable behavior and minimal assumptions—not just in cultural analysis, but in infrastructure as well.
During a hosting migration over the weekend from porkbun to rocket.net for better stability and control over the site itself, I reviewed authentication traffic patterns due to unusual login attempts on my site. Note: this is NOT something that every hosting provider (Namely cheaper ones) provide. So thanks to rocket.net. This review concluded repeated automated login attempts originating from non-U.S. bot traffic.
I used it as an opportunity to apply layered security controls:
Reduced attack surface by obscuring default login pages
Implemented rate-limiting on failed login attempts
Disabled legacy remote-procedure endpoints that are no longer required for site operation
After applying these changes, automated login traffic ceased, and authentication logs returned to baseline behavior.
This process reinforced a simple principle that applies beyond WordPress: I could have left the site as is, but doing a little research and installing some much-needed plugins to close outdated loopholes is a must for everyone running their own site.
RewindOS continues to favor minimal surface area, observable behavior, and incremental hardening over complex or opaque security tooling.
When Severance used “Baby It’s Cold Outside” in Season 2, Episode 7 (Chikhai Bardo), which aired February 28, 2025, the choice looked controversial only if you froze time in 2018.
If you remember there was a very brief controversy in 2018 framed around consent, radio pullbacks, and an outsized media backlash narrative.
At RewindOS we don’t know if the team over at Apple TV refined their data in this structured way or if indeed the 2018 controversy ever even came up in the writer’s room or the legal department.
So with that being said this project set out to answer a simple question:
By late February 2025, was this song actually risky to use?
My original thought was no. Not only was it not risky to use, but nobody had even mentioned it after the episode aired, and if you sort through the various subreddits run by eagle-eyed severance fans who discuss all things Severance in very detailed posts, you will get an idea of where I am going with this, how I got there, and how it can be recreated for just about any type of RewindOS Cultural Safety Framework that I will be developing to apply to other shows, quotes, actors, or songs.
How I built this:
This analysis draws on two independent signals to assess whether “Baby It’s Cold Outside” remained culturally risky by the time Severance aired in 2025.
1. Google Trends: Controversy Decay
Search interest was tracked for multiple controversy-framed queries related to the song from 2018 through 2025. using manual searches and then a larger python code. The data shows:
a clear but small peak during the 2018 radio pullback news cycle
rapid decay in the months that followed
no secondary spikes or resurfacing in subsequent years
By 2020, controversy-related search interest had returned to baseline and remained there through 2025.
2. Reddit: Social Engagement & Backlash Check
Reddit posts were collected using public JSON search endpoints across within a python code that produced the following outputs:
broad references to the song and Severance / Apple TV
subreddits where this discussion would be most likely to appear
Weekly aggregation shows effectively no sustained discussion following the episode’s release. Only one post referenced the song in connection with Severance, and it discussed narrative tension rather than offense.
The absence of engagement is itself the result. I was surprised to see the one post which featured the song as part of a long implied consent piece but this post did not show until March 6, 2025 a week after the episode aired and the post itself only generated three comments of discussion none about the song itself.
Again, Whether or not Apple TV explicitly modeled this risk, guessed, or just didn’t care. We don’t know, but the outcome reflects what the data already showed and how others can use it. This is what cultural data intelligence looks like when measured instead of guessed.
If you want to see or run the Python script used for this analysis, the full repository is here:
Christmas episodes are a long-standing sitcom tradition. Nearly every major network comedy of the past 30 years has produced multiple holiday-themed episodes, often using Christmas or other holidays as a narrative anchor for character conflict, sentimentality, or satire.
This project was built to answer a simple question:
How much Christmas music do sitcoms actually use?
Rather than assuming that Christmas episodes are saturated with holiday songs, this analysis catalogued theactual music used in Christmas episodes across a sample of well-known sitcoms, tracking whether songs were:
licensed (commercial recordings)
public domain (traditional carols)
or absent altogether
What the Data Actually Shows:
To build this dataset, I started by manually reviewing episode music listings on Tunefind.com, a site that catalogs what songs appear in television episodes.
I selected five well-known sitcoms spanning different eras and focused specifically on their Christmas episodes during periods when holiday-themed programming was most common, based on findings from my earlier analysis of Christmas episodes. For each episode, I recorded whether Christmas music appeared and whether those songs were licensed recordings or public-domain carols. I then wrote a small Python script to organize the data, calculate basic metrics (such as licensed versus public-domain usage), and generate visualizations to make patterns easier to compare across shows and time periods.
Across major sitcoms analyzed — spanning the 1990s through the 2010s — one pattern is consistent:
Most Christmas episodes use little to no Christmas music at all.
Key observations from the spreadsheet:
Licensed Christmas songs appear infrequently, even in peak-era sitcoms.
Many Christmas episodes contain zero licensed holiday tracks.
When music is present, it is often:
public-domain carols
brief background cues
or isolated moments rather than sustained themes
Entire Christmas episodes often rely purely on dialogue, setting, and performance, not music, to signal the holiday.
Importantly, this pattern holds across decades. The data does not show a sharp drop-off or collapse in music usage — it shows that Christmas music was never heavily used to begin with.
Why This Matters (and Why Cost Alone Isn’t the Answer)
The initial question — “Is Christmas music too expensive to license?” — is still valid, but the data suggests a more nuanced conclusion:
Sitcoms largely chose not to rely on Christmas music, even when budgets were larger and licensing was easier.
Several plausible explanations emerge:
1. Sitcoms Signal Christmas Visually, Not Musically
Christmas episodes communicate the holiday through:
Music is optional, not essential. The holiday is already legible without it.
2. Comedy Prioritizes Timing Over Atmosphere
Unlike dramas, sitcoms are built around:
rapid dialogue
punchlines
awkward silence
Continuous background music — especially familiar Christmas songs — can interfere with comedic timing or dilute jokes. Many sitcoms intentionally avoid music so the rhythm of the scene remains intact.
3. Christmas Songs Are Culturally “Loud”
Christmas music carries heavy emotional and cultural baggage. Using a well-known song can:
overwhelm a scene
inject sentiment the writers didn’t intend
pull attention away from character dynamics
Avoiding music gives writers tighter control over tone and avoids the episode from being dated.
4. Licensing Cost Reinforces an Existing Creative Preference
Licensing costs may reinforce avoidance, but the data suggests they are not the root cause.
Even when:
network budgets were higher
syndication economics were stronger
licensing environments were more permissive
sitcoms still used very little Christmas music.
In other words:
Cost may explain why music didn’t increase — not why it was absent.
How This Fits with the Broader RewindOS Christmas Analysis
In our companion analysis on Christmas episodes and television health, we show that:
Christmas episodes remain common
but their function has changed
This music analysis complements that finding:
Christmas episodes persist as narrative rituals
not as audio spectacles
music was never the core signal — the episode itself was
The holiday survives on structure and story, not soundtrack and it doesn’t need music.
Final, Data-Faithful Conclusion
Sitcoms did not abandon Christmas music — they largely never relied on it in the first place.
Across decades, networks, and formats, Christmas episodes consistently used minimal music, suggesting a long-standing creative norm rather than a modern cost-driven retreat.
Licensing expense may discourage experimentation, but the evidence indicates that sitcoms have always treated Christmas music as optional.
So the next logical question for us would be if certain dramas would choose a controversial christmas song to heighten the episode? Stay tuned to find out.
If you want to see or run the Python script used for this analysis, the full repository is here:
A data-driven look at how Christmas-themed TV episodes rise and fall with industry confidence.
Why I Built This
This is the second project for rewindOS. This project is part of a short Christmas programming sprint designed to explore how holiday-themed television functions as a cultural signal rather than a novelty. I started with a simple question: Are Christmas episodes still as prevalent as they were during the era of long-running television shows? In earlier decades, holiday episodes were almost guaranteed milestones for successful series, so I wanted to understand how that tradition has changed in the modern TV landscape.
The first step was to measure actual output specifically, and so I went to wikipedia to see if they had a list of Christmas Episodes for TV. They had an extensive list that was used for the dataset of this project. Then I used python coding to produce a graph for how many Christmas-themed television episodes were produced between 1947 and 2025, and looked at the downward spiral of shows from 2023-2025 and how that compares to earlier periods.
From there, the sprint expands into a few focused, related experiments rather than a single large dataset.
Next, I plan to track Christmas music usage within TV episodes, looking at which songs appear, how frequently music is used, and what types of Christmas music are favored. This portion will intentionally stay small, focusing on two to three well-known sitcoms, to keep the analysis tight and interpretable.
The sprint will conclude with a beta test of a controversy index, applied to a Christmas-themed case study, as a way to explore how sentiment, cultural backlash, or reinterpretation can affect holiday media over time.
All of these projects are designed to be completed within one to two weeks, serving as a focused seasonal experiment while also laying groundwork for broader RewindOS cultural analytics moving forward.
Figure 1: Number of U.S. Christmas-themed television episodes by year (1947–2025), excluding standalone specials.
When we chart U.S. Christmas-themed TV episodes over time (excluding standalone specials), we see that they begin appearing consistently in the 1950s, rise steadily, and then fluctuate in clear cycles. Major declines occur in 1998–2000, 2006–2008, and again starting in 2023. Notably, 2008 and 2025 mark the lowest levels of Christmas episodes in modern television, matching periods of industry instability. The most productive era was 2012–2023, driven by long-running, sitcoms with over 5 seasons.
Expanding on what the data shows (so far)
Using a filtered dataset of U.S. television Christmas-themed episodes (excluding standalone holiday specials and animation), several clear patterns emerge:
🎄 Long-term rise, then contraction
Christmas-themed TV episodes begin appearing consistently in the 1950s, aligning with the rise of broadcast television and stable seasonal scheduling.
Prior to that, occurrences are sparse, with 1973 marking the lowest point for Christmas episodes in the modern TV era.
Repeated contractions in Christmas episode production align with broader industry disruptions.
📉 Structural dips repeat over time
Three periods show notable declines in Christmas episode production:
1998–2000
2006–2008
2023–2025
These downturns closely resemble each other in magnitude and shape, suggesting recurring industry-wide contractions rather than random fluctuation.
❄️ Modern low points
2008 and 2025 stand out as the lowest years for Christmas episodes in modern television, placing today’s output on par with the post-network-era shock of the 2008 Writers’ Strike period.
The current decline beginning in 2023 suggests a sustained trend rather than a single anomalous year.
📈 Peak era: 2012–2023
The years 2012 through 2023 represent the highest sustained production of Christmas-themed episodes.
This era is largely fueled by:
Highly successful, long-running sitcoms
Large ensemble casts
Stable season orders (22 Episodes ftw!!!!)
Continued contribution from U.S. animation (even when animation is excluded from the core count, its ecosystem supports the broader trend)
This period reflects an industry confident in long arcs, recurring characters, and calendar-based “event episodes.”
Long-running ensemble sitcoms dominated the peak era of Christmas television episodes (2012–2023).
Christmas episodes function as a high-confidence production choice:
They assume a show will still be on the air in December
They reward audience loyalty
They often require additional budget, music licensing, and scheduling certainty
Their decline aligns with periods of:
Shorter seasons
Higher show churn
Platform fragmentation
Labor disruption or industry uncertainty
This suggests that Christmas episodes may act as a proxy indicator for scripted TV stability, not just seasonal nostalgia.
This data in no way shape or form indicates that there is an overall agenda driven message surrounding Christmas itself on TV. It just shows that there is a decline in Television confidence in general and so holiday themed episodes are either cut or don’t make sense when a show doesn’t have 22 episodes or won’t be around during the holidays.
My original thought going into this project was that the years 2020-2025 would show a major decrease but as you can see 2023 had a high output of Christmas themed episodes.
If you want to see or run the Python script used for this analysis, the full repository is here:
This is the first mini-project for RewindOS and it started with a simple question:
When I watched the latest season of Prehistoric Planet Season 3: Ice Age, I noticed that Tom Hiddleston who recently replaced the late great David Attenborough as the narrator, said some variation of “the biggest of all time,” quite a lot. So I set off to determine how many times without using my fingers to count it like the cavemen did in those prehistoric times.
It turns out with a little reverse engineering and hacking, this is quite easy and it highlights a metric that nobody in Hollywood seems interested in measuring— yet.
If you watch a lot of nature documentaries, you probably notice recurring superlatives—“the largest ever discovered,” “the biggest predator of its era,” “the strongest bite force in history.” These phrases shape how we interpret both animals and storytelling. They’re a mix of science communication and spectacle.
So I wanted to answer a playful but data-driven question:
How frequently does the show in season 3 use the phrase “of all time,” and in what contexts?
This became a perfect first test for RewindOS, because it touches everything I envision this project will be and ultimately do at scale:
extracting structured data from media
analyzing linguistic patterns
building repeatable pipelines
archiving, visualizing, and tracking cultural tendencies in media
How I Got the Subtitles
I already had the Season 3 video files. Thanks to my automated plex setup. So logically, I first went to my plex library and tried to extract the files that way.
However, I see in plex after some basic research and simple ls linux commands on my server that they were nowhere to be found readily available for extract in my system. Lo and behold further research proved my point:
Subtitles downloaded by Plex itself are stored inside of Plex’s blob files and aren’t able to be interacted with, nor can the location be changed.
Chatgpt gave me some helpful but rather tedious “legal” ways to obtain the subtitles by recording them on VLC/Plex as it plays. However after some more research I discovered VLC’s VLSub feature which can download the embedded files. Success!
This gave me five .srt files I needed — one for each episode.
Those files became the dataset for the project.
Not being a scratch programmer and now deeply entrenched into the era of AI, the next logical step was to build the python script.
The entire analysis pipeline was built using AI as a collaborator.
I described the problem (“Find all phrases ending in of all time in the SRT files”), and the model generated a clean Python script that:
Walks through my subtitle directory
Reads every .srt file
Uses a regex pattern to capture the phrase + its context
Prints them to the console
Exports everything into a structured CSV
The core of the extraction logic was this:
pattern = re.compile(
r"(\b\w+(?:\W+\w+){0,6}\W+of all time\b)",
re.IGNORECASE
)
This lets the script capture phrases like:
“largest predators of all time”
“one of the most powerful hunters of all time”
“the biggest terrestrial bird of all time”
The final output CSV includes:
which episode
the snippet of text
how many instances appear across the season
This is exactly the kind of small linguistic dataset that RewindOS will eventually let creators, journalists, and analysts explore effortlessly.
It’s easy to laugh at how often nature docs use hyperbole, but those phrases shape cultural impressions of extinct animals. They’re part of storytelling tradition. Being able to quantify them is the first step in understanding patterns across eras, genres, studios, and creators.
If you want to see or run the Python script used for this analysis, the full repository is here:
