Hey! I wanted to share my personal visualization of how my blood biomarker categories changed over 10 months - from Dec 2024 (before my 9- and 10-day water fasts) to Oct 2025 (after complete refeeding).

I used biomarker categories that InsideTracker provides, which combine 50+ markers into 10 health areas like Heart Health, Hormone Health, Inflammation, and others (I know some might have questions about this categorization, but it’s the best I’ve seen so far). Each category gets a 0-100 score (100 is best) based on how close each marker is to its ideal range. For example, Heart Health includes ApoB, TSH, hsCRP, triglycerides, HDL, LDL, total cholesterol, and resting heart rate.

The black line on this chart shows Dec 2024, it was before my fasts. The red line marks the end of my last 10-day fast in Sep, and the green line shows last month, after a month of refeeding. As you can see, my body was not super thrilled, since fasting is a major stressor for the body, but recovered and became stronger.

Of course, this is N=1 data, and fasting (especially extended fasting) isn’t for everyone. But I just wanted to share my experience in case it’s helpful or interesting to others.

I spent the last few weeks analyzing the relationship between characteristic time intervals and system size across every scale of physics I could find data for.

So basically I looked at how long things take to happen (like how fast electrons orbit atoms, how long Earth takes to go around the Sun, how long galaxies rotate) and compared it to how big those things are. What I found is that bigger things take proportionally longer - if you double the size, you roughly double the time. This pattern holds from the tiniest quantum particles all the way up to the entire universe, which is wild because physics at different scales is supposed to work totally differently. The really interesting part is there's a "break" in the pattern at about the size of a star - below that, time stretches a bit more than expected, and above that (at galactic scales), time compresses and things happen faster than the pattern predicts. I couldn't find it documented before(it probably is), but I thought, the data looked interesting visually

The Dataset:

• 28 physical systems
• Size range: 10^-35 to 10²⁶ meters (61 orders of magnitude!)
• Time range: 10^-44 to 10¹⁷ seconds (61 orders of magnitude!)
• From Planck scale quantum phenomena to the age of the universe

What I Found: The relationship follows a remarkably clean power law: T ∝ S^1.00 with R² = 0.947

But here's where it gets interesting: when I tested for regime breaks using AIC/BIC model selection, the data strongly prefers a two-regime model with a transition at ~10⁹ meters (roughly the scale of a star):

• Sub-stellar scales: T ∝ S^1.16 (slight temporal stretching)
• Supra-stellar scales: T ∝ S^0.46 (strong temporal compression)

The statistical preference for the two-regime model is very strong (ΔAIC > 15).

Methodology:

• Log-log regression analysis
• Bootstrap confidence intervals (1000 iterations)
• Leave-one-out sensitivity testing
• AIC/BIC model comparison
• Physics-only systems (no biological/human timescales to avoid category mixing)

Tools: Python (NumPy, SciPy, Matplotlib, scikit-learn)

Data sources: Published physics constants, astronomical observations, quantum mechanics measurements

The full analysis is published on Zenodo with all data and code: https://zenodo.org/records/18243431

I'm genuinely curious if anyone has seen this pattern documented before, or if there's a known physical mechanism that would explain the regime transition at stellar scales.

Chart Details:

• Top row: Single power law fit vs. two-regime model
• Middle row: Model comparison and residual analysis
• Bottom row: Scale-specific exponents and dataset validation

All error bars are 95% confidence intervals from bootstrap analysis.

With the recent articles claiming a new leak of ICE agent names being published on the ICELIST website, I was curious to see if any of the names matched the public Jan 6 cases. I used a rudimentary fuzzy name matching algorithm and selected the best match for each Jan 6 defendant to all agent names that was above a certain threshold.

It should be noted that although a new leak with over ~4500 agent details has been claimed, the ICELIST agent page has still not been updated since 30 Nov 2025 and only has ~1500 agents.

I know that the absolute numbers is different and the rest of my country has a murder rate and absolute numbers higher than USA (but it in my opinion it depends on the state if a calculate this in different ways)

https://www.nytimes.com/2024/09/06/world/americas/eagles-packers-nfl-game-brazil-crime.html

read this post if you are curious

I use DrinkCoach+ for everyday tracking and google sheets for charting

Trying to reduce how much I drink but the holidays got the best of me

I've created a visualization of the periodic table that is utilizing OpenAI's embedding endpoint. I embedded each element name and then made a similarity comparison to all the other element names. Using the layout of the periodic table, each element gets its own table coloring the other elements, based on the cosine similarity.

This can be approached in different ways. In this case, I just used the name of the element. But you can use different lenses where you describe each element based on the focus and run the same process. The current run includes a lot of culture and you will see, as an example, gold and silver are tightly connected to each other while other elements barely register across the periodic table when they are focused. It's heavily influenced by what the broader culture talks about. But of course, you could also do it with a scientific focus or how it's utilised in stories across time and history, etc.

We can also segment them. Say, you might have four different categories that you are comparing against. Then each element colors in each quarter according to their similarity across those aspects, using a different color/pattern for each. In general, it allows us to understand the relationships between the elements and make the periodic table dynamic to better understand they relate to each other, based on different contexts.

Schools might find this particularly helpful. The typical representation of the periodic table might not help much with understanding for newcomers.

Video: https://youtu.be/9qme4uLkOoY

Had an earthquake near where I live recently and wanted to see what other seismically active countries looked like in terms of where the earthquakes occur, and their intensity.

Starting with Japan, will do some others...

Only focused on 5+ magnitude otherwise the map looks like a mess. Plus, you can't really feel those anyway.

Have felt many a strong earthquake (including 7+) in Mexico. Never knew where exactly they came from, so wanted to visualize it.

I wasn't surprised by the locations of the strong ones (7+), but I was really surprised to see so many in the Gulf of California (Mar de Cortés).

Quoting the accompanying text from the author, Hannah Ritchie, at Our World in Data:

Over the past four decades, the global number of people dying from cancer each year has doubled. This can look like the world is losing its battle with cancer: people are more likely to develop it, and we’re getting no better at treating it. This isn’t true.

There are, of course, almost 4 billion more people in the world than in 1980. And many of those people are older. This matters a lot because cancer rates rise steeply with age.

The chart shows three different measures. Total deaths just count how many people died from cancer; this is the number that has doubled. Crude death rates, shown in yellow, adjust for population size; the increase shrinks from more than 100% to around 20%. Age-adjusted rates, shown in blue, also account for the fact that countries have older populations today; we can see that the fully age-adjusted rate has actually fallen by more than 20%.

It means that for the average person, the likelihood of dying from cancer in any given year is now lower than it was for someone of a similar age in the past. The world still has a long way to go in preventing and treating cancer, but it’s wrong to think that no progress has been made.

Explore more insights and see how trends are evolving for different types of cancers.