I've been thinking about the demographics surveys recently and how they usually draw a good reception, but are difficult to compile in a privacy-minded way and finally visualize the data. I think the 2024 survey never ended up getting its results published?

So I thought maybe instead of one big yearly survey we could organize smaller weekly or monthly surveys that ask one or a couple questions at a time. And then while the current survey is running the question+answers for the next survey could be plannedy out. And the questions could be about anything and not just demographics related things.

In terms of technical setup I've been using a self-hosted n8n instance for a while and with tinkering around with it I've come up with a decent proof of concept to make these surveys happen via n8n's Form nodes. This makes it so all the data is in our own control and it's just a matter of building out any visualizations we may want.

And at the end of a survey the data could be anonymized and published so any further analysis can be done by whoever wants to.

One other thing I've come up with is a way to "authenticate" responses so we can know when a response was made by a real Tildes user. In the survey flow the server can generate a UUID and give it to the user to put in their profile bio. Then we scrape the user's bio and check if that UUID is present and now we know that yes, this is a real user. And then the UUID can be removed again from the user's bio (so it can't be linked back to them). This would also enable us to not have to save usernames at all (yay privacy). Edit: this would be entirely optional if it were to be added. There would be no requirements to fill in the survey unless we start seeing a bunch of spam or bogus responses.

What do you Tildoes think? That's what we settled on right? Is this something you'd be interested in?

Sorry for the late posting (life got in the way).

Welcome all to our weekly (ish) gardening group discussion!

Feel free to discuss anything related to gardening, beginner or advanced, challenge or success.

‘Seed’ questions:

1. Would you like to garden in a different climate, if so where?
2. Who shares in the your gardening outcomes? Friends/family, or is it more personal?
3. What if your motivation to garden? Is it the reward at the end, the journey or something else?

Have you watched any movies recently you want to discuss? Any films you want to recommend or are hyped about? Feel free to discuss anything here.

Please just try to provide fair warning of spoilers if you can.

What have you been watching and reading this week? You don't need to give us a whole essay if you don't want to, but please write something! Feel free to talk about something you saw that was cool, something that was bad, ask for recommendations, or anything else you can think of.

If you want to, feel free to find the thing you're talking about and link to its pages on Anilist, MAL, or any other database you use!

Predicting the NBA MVP with Machine Learning

Thesis

Every season, basketball fans debate who deserves the MVP award. We built 3 machine learning models that attempt to answer that question using box score statistics. At the end of each season, this award is determined by a panel of voters.

Methodology

Each model is trained on every NBA season from 1974 to 2017. For each player season, it looks at nine statistics:

• Points, assists, blocks, defensive rebounds, and field goals per game the core production numbers
• Win Shares (WS): an estimate of how many wins a player contributed to their team
• Value Over Replacement Player (VORP): how much better a player is than a league average replacement
• Box Plus/Minus (BPM): a player's net impact per 100 possessions
• Usage Rate (USG%): what share of team plays run through that player

From those nine numbers, the model learns what a typical MVP season looks like versus a non MVP season, then applies that knowledge to current players. Each model outputs an independent probability that a given player wins MVP, not a share of a single pool, so the values do not sum to 1. Think of it as each player's individual odds.

Three Models, One Question

Rather than relying on a single approach, the system runs three different models and lets you compare:

Logistic Regression

The simplest of the three. It draws a straight line through the data, each statistic gets a weight, and a player's score is the weighted sum of their stats. It's easy to interpret (a higher coefficient means that stat matters more).

Win Shares (WS) is by far the most influential feature, with an absolute coefficient of ~1.85, nearly double the next most important feature. Box Plus/Minus (BPM) ranks second at ~1.0, followed by Defensive Rebounds per Game (DRBPG, ~0.85) and Assists per Game (ASTPG, ~0.70). VORP and Field Goals per Game (FGPG) contribute moderately at ~0.50. Blocks per Game (BLKPG), Points per Game (PTSPG), and Usage Rate (USG%) have minimal weight, all under 0.15.

Random Forest

Builds hundreds of decision trees, each one asking a series of "is this stat above or below X?" questions and averages their answers. It handles complex relationships between stats well and is less sensitive to any one unusual data point. Think of it as a large committee of simple rules voting together.

WS again dominates at ~0.31, accounting for roughly twice the importance of the next feature. VORP (~0.15) and BPM (~0.125) rank second and third. DRBPG (~0.10), PTSPG (~0.08), BLKPG (~0.07), FGPG (~0.065), and ASTPG (~0.06) contribute in a fairly tight mid-range band. USG% is the least important at ~0.05. Compared to logistic regression, the Random Forest spreads importance more evenly across features.

Gradient Boosting

Also uses decision trees, but builds them sequentially: each new tree focuses on correcting the mistakes the previous ones made.

This model is heavily concentrated on just two features: BPM (~0.47) and WS (~0.41) together account for roughly 88% of total feature importance. All remaining features, PTSPG, VORP, ASTPG, DRBPG, contribute ~0.02–0.03 each, and BLKPG, USG%, and FGPG are effectively unused (near zero). This suggests the gradient boosting model learned that BPM and WS alone are nearly sufficient to separate MVP candidates.

Historical Results

The models were trained on data through 2017, so every season from 2018 onward is a genuine out of sample test, the models have never seen these players or seasons before.

Top-1 accuracy: LR 5/8 · RF 5/8 · GB 6/8

Top-3 accuracy: LR 8/8 · RF 7/8 · GB 7/8

Top-3 accuracy: LR 8/8 · RF 7/8 · GB 7/8

For five straight seasons (2019–2022 + 2024), all three models agreed on the same #1 pick, and were right every time.

In 2023, every model ranked Nikola Jokić #1, and by the numbers, he arguably had the better season. Joel Embiid won the award anyway, the kind of outcome that may reflect voter narrative/fatigue and team performance rather than pure statistics. In 2025, Gradient Boosting ranked Shai Gilgeous-Alexander outside the top 500, while Logistic Regression and Random Forest had him at #3 and #2 respectively. I have no idea why GB did this. Likely a bug.

Future Direction

No model is perfect, and these have known blind spots. Team record is not included, MVP voters have historically punished players on losing teams regardless of individual stats. Injuries and narrative don't appear in a box score. And the training data skews toward an older era; the three point revolution and the rise of players like SGA have introduced statistical profiles the 1970s–1990s data doesn't fully capture.

Current Season Predictions (2025–26)

Two of the three models have Nikola Jokić as the frontrunner. Random Forest is the dissenter, putting Shai Gilgeous-Alexander ahead. Victor Wembanyama appears in all three top 3s in just his second season, which is notable. Before running the models, I expected him to be #1 for all of them considering the way the models use advanced stats.

Conclusion

Thank you for reading. I hope you found this interesting. Basketball reference also has their own model if you would like to see a different result. Please do not gamble on my models!

So much of what the algorithms surface is negative. For all of the reasons that mostly everyone's aware of at this point.

It's easy to get the general impression that times are dark without realizing it. I think sometimes it's good to intentionally offset algorithmic (and general human) negativity bias.

Lets do a positive news thread, I'll start:

Hungary votes out Orbán after 16 years

Perovskite solar cells hit 34.85%

Portugal hits 80.7% renewable electricity

Hidden drainage system found in human brain

First lab-grown oesophagus using hosts own cells (fully incorporated with muscles, nerves, arteries within 6 months)

And of course Artemis II! Why is space exploration somehow more positive than the sum of its parts?

Please post anything, it doesn't have to be "news". The full range of the humanities works too

I've been working on a playlist for a while of rock / pop songs with sax solos. The rules are:

• Must be from this millennium
• sax can't be a primary instrument
• pop or rock genres preferred

Here's what I've got so far: https://link.deezer.com/s/323YPvabsQgEuS8BOTCXj

I have been gifted a tiny bit of VPN quota from a relative to allow me to upload my beatsaber creations. Its been the most important thing for me to put out, had found a very kind community, was known-ish in it. I spent multiple weeks working on one of the projects i had planned, it was the only thing do to daily. Since it was finished life's been nothing after nothing after nothing. Fear, stress, depression, power infrastructure might get hit, planning for loss of power, loss of water, loss of food.
Constant stream of people asking for internet in news website comments. On the verge of crying 80% of the time.
I'm not gonna buy atrociously prices VPNs, even in this state people are scamming eachother with fake VPNs.
I miss life, life's been on pause for more than a month. the best describer of life is that it's on pause, that's all.
I was never at risk for depression, my friends who were now have it and struggle with it.
I have lost weight and gained a few more white hairs tho.
I love everyone and everything, i wish to get back to life.

Will be seeing you hopefully in a better state.
-Pooya (slabs37)

edit 2026/4/14:
got connected via another paid vpn, don't think this one will last long either. i put the wrong url, because ofcourse i did...

This topic is part of a series. It is meant to be a place for users to discuss creative projects they have been working on.

Projects can be personal, professional, physical, digital, or even just ideas.

If you have any creative projects that you have been working on or want to eventually work on, this is a place for discussing those.

For a long time programmers have had two types of program verification tools, static analysis (like a compiler's checks) and dynamic analysis (running a test suite). I find myself using LLMs to analyze newly written code more and more. Even when they spit out a lot of false positives, I still find them to be a massive help. My workflow is something like this:

1. Commit my changes
2. Ask Claude Opus "Find problems with my latest commit"
3. Look though its list and skip over false positives.
4. Fix the true positives.
5. git add -A && git commit --amend --no-edit
6. Clear Claude's context
7. Back to step 2.

I repeat this loop until all of the issues Claude raises are dismissable. I know there are a lot of startups building a SaaS for things like this (CodeRabbit is one I've seen before, I didn't like it too much) but I feel just doing the above procedure is plenty good enough and catches a lot of issues that could take more time to uncover if raised by manual testing.

It's also been productive to ask for any problems in an entire repo. It will of course never be able to perform a completely thorough review of even a modestly sized application, but highlighting any problem at all is still useful.

Someone recently mentioned to me that they use vision-capable LLMs to perform "aesthetic tests" in their CI. The model takes screenshots of each page before and after a code change and throws an error if it thinks something is wrong.