A few weeks ago the USB-C power supply of my work notebook died. As a replacement I ordered a UGreen one which can power multiple USB-C devices -- resulting in less power plugs on my desk at home.

After this I looked into not killing the battery of this Thinkpad by configuring the charging. Addionally the tool I installed could manage the temperature by not powering the notebook to the limit.

The Thinkpad I got from my employer is a Lenovo T14 Gen3 Ryzen7. Because I work at home most of the time, the notebook is pluged in nearly all the time. It is not healthy for the battery to always charge a tiny bit until it is full again.

To fix this I install TLP. The default Battery charging thresholds are perfect:

# Battery charge level below which charging will begin.
START_CHARGE_THRESH_BAT0=75
# Battery charge level above which charging will stop.
STOP_CHARGE_THRESH_BAT0=80

These values are the defaults, but I still removed the comments for me to be aware that I want them like that.

Additionally I unplugged the notebook a few times and recharged it, to give the battery a bit of "normal" charging. I am aware that mistreated batteries need to be observed if they inflate. Mine seems to be okay.

So as I already said, the Thinkpad is most of the time on AC. And when using a lot of CPU the notebook gets warm and loud. I was not aware that there are multiple power profiles to manage this. Then plugged into AC the performance profile is active, which results in more heat and fan spinning when all cores are busy. But I actually don't need the performance profile of the notebook running on maximum when connected to AC. So I changed the platform profile for AC from performance to balanced with this line in the config:

PLATFORM_PROFILE_ON_AC=balanced

I still could change it to performace again when needed. Or even to low-power when I don't want to have the fan spinning.

This resolved the two ThinkPad issues I wasn't aware I needed to fix. 🎉

Some years ago I aggregated my travel costs in a post to show the savings I had because of the 9€-Ticket. Since then I tracked all my Deutschlandticket saving using a hledger virtual entries.

2025-12-30 VVS Ticket (Stuttgart -> Schorndorf)
    Expenses:ÖPNV:VVS                          €6.80
    Assets:Girokonto
    (worthIt:Deutschlandticket)                €6.80

2025-10-10 Regional train (Göppingen -> Stuttgart)
    (worthIt:Deutschlandticket)                €-8.3

2025-10-01 Deutschlandticket
    Expenses:ÖPNV:Deutschlandticket              €58
    Assets:Girokonto
    (worthIt:Deutschlandticket)                  €58

hledger register ^worthIt:Deutschlandticket -O csv

"txnidx","date","code","description","account","amount","total"
"4928","2023-05-01","","Deutschlandticket bei der SSB","(worthIt:Deutschlandticket)","€49.00","€49.00"
"4970","2023-05-04","","Bahn: Stgt -> Reutlingen","(worthIt:Deutschlandticket)","€-11.00","€38.00"
"4970","2023-05-04","","Bahn: Tübingen -> Herrenberg","(worthIt:Deutschlandticket)","€-6.00","€32.00"
...
"5011","2023-05-30","","VVS: Office day","(worthIt:Deutschlandticket)","€-2.75","€-52.55"

def get_ticket_price_for_month(year_month):
    if year_month >= "2026-01":
        return Decimal("63")
    elif year_month >= "2025-01":
        return Decimal("58")
    return Decimal("49")

In the previous post I changed the reading challenge personal folder to a submodule, so that I can use the personal repository for another project: This one. A Python script is processing the IMDB Rating export and generates calendar heatmaps similar to the GitHub contribution activity plots.

The first version used the dayplot Python library, but this doesn't allow per-cell categorical colors. So I asked Claude to change the code to pure matplotlib.

In the IMDB Ratings every entry has a Type, I chose to color them differently. But first I merged the Types like this:

TV Movie -> Movie
TV Mini Series -> TV Series
TV Short -> Short
TV Special -> TV Series

And I removed "Video" and "Video Game". Both are rare in my data. And especially for Video Game the date of rating has nothing to do with the date I actually played the game.

As a result we get these Types, and they get colors when mixed have visible differences:

"Movie": "#16a34a",       # green
"TV Episode": "#4f46e5",  # indigo
"TV Series": "#ea580c",   # orange
"Short": "#0d9488",       # teal

The code would be much shorter without any Type colors, like the green in the GitHub contribution calendar heatmaps.

The full source code is mirrored on GitHub.

A few times in the past, I rated a large number of TV episodes in a single day. The highest count was 140. Obviously, I didn’t actually watch that many episodes on that day. So the data is not perfect for this kind of plot. Still the resulting images are a good indicator how much I watched Movies or TV Episodes.

Next are some example years from my data. In the first years I used IMDB (2004 till 2010) I only rated Movies. I don't know anymore why I started to rate TV Episodes in 2010. But since then I use IMDB ratings to track what I have already watched.

My ratings plot for 2010, the year I started to rate TV Episodes:

I can see in the plots the years where a lot happened and I had no time to watch Movies or Series, for example 2015:

And there is the obvious year where we all spent time at home and watched Movies and Series, 2020:

Overall a good way to visualize my personal Movie and TV Series consumption.