The "nobody" in the title doesn't have to be literal -- it can be a battle that very few people know about. The important thing is that it's mostly hidden.

What is the struggle?
Is it hidden by choice?
Do you want more people to know about it? Why or why not?

I've been thinking on this for a while, and was inspired to ask about it while reading through the blackpill thread. I don't intend to actually look for a relationship for a while; it's been six months since the breakup, and my ex and I didn't agree to no-contact until two days ago, so I still have a long healing process to get through. But I have a lot of... dread? around not having a life partner forever, with the key factor being not having a close friend like my ex was pre-relationship. If I could emotionally and financially handle all life matters on my own that would be beautiful, but even just thinking about getting to the place I want to be financially while still maintaining a certain lifestyle is anxiety-inducing on its own. So again, even as I do not actively prepare to download any app and put myself out there, I'd like to take some notes as someone who has never used an app and whose previous relationships were by chance (classmates while in school, ex was from MMO).

For starters: I'm a cis woman, early 30s, and identify as lesbian, demisexual, demiromantic. I don't know where I am on the scale of conventional attractiveness. I'm extremely short and skinny. I've never really gone through the initial "dating" process (I knew my exes before getting in a relationship with them so we kind of jumped into being exclusive/"official").

The demi- bits mean a lot to me. I feel it makes sense to just seek spaces for activities that I enjoy and go on from there, but I feel like it's a difficult numbers game because statistically most people will be straight, right? And I don't think I exude any non-straight energy either, if that's even a thing. So this brings me to why I feel I inevitably will need to use dating apps - I fear the environment, I have never applied makeup on myself and couldn't tell you the difference between mascara and eyeliner without Googling, and the blackpill thread is filled with commentary on how these apps really cultivate a landscape with a focus on appearance. But simply being not-straight makes me feel I have to use an app for the basic filter of gender preference.

I don't see myself going to a gay bar (prefer not to drink). I can see some queer-friendly dating-focused events in my area that sound okay but I fear my issues with social performance will keep me away (I can perform for one person but the few events I see right now are speed-dating or casual mixers). Also some of them are hosted at wineries/pubs and I get that alcohol is normal, but I really don't like the vibe of bars themselves (too loud).

I also don't know if there are... things to "know" when trying to date as a lesbian? Like when folks talk about being masc/femme, those things don't really mean anything to me - I have male-dominated hobbies and don't wear feminine clothing, but to say that any bit of me says "masculine" in any way just doesn't seem right. I also honest to god do not know what expectations are regarding trans women. I can't write them off as I've never dated or been romantically interested in a trans woman, but I do fear that the... equipment, for lack of better phrase... might matter to me, and I don't want to offend too late? Is it transphobic to say I'd prefer to date cis women?

Apologies as I realize that this is definitely becoming more of a ramble on "how date, I've never dated strangers" and less on advice for use of dating apps specifically. But at the end of the day, yes, I feel that I will need to use dating apps but fear the experiences that I read about from using them.

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?

What have you been doing lately for your own fitness? Try out any new programs or exercises? Have any questions for others about your training? Want to vent about poor behavior in the gym? Started a new diet or have a new recipe you want to share? Anything else health and wellness related?

Have you watched any TV shows recently you want to discuss? Any shows 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.

I noticed bluesky is down.

I haven't seen news about this yet so I wonder if it is just a temporary tech problem or a DOS.

I'm getting this error or an error about rate limiting:

Hmm, some kind of issue occurred when contacting the feed server. Please let the feed owner know about this issue.
Message from server: Upstream server responded with a 503 error

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