So, according to my memory, I asked this question on Tildes "not that long ago."

Then I checked.

Chat, it was a LONG time ago. Excuse me while I crumble into dust.

Anyway, given that the Linux landscape looks very different than it did not that long ago in 2018, I figure we're due for another topic like this:

• Which Linux distro do you use, and, most importantly, why do you prefer it?

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?

I've found myself replying to different people about the early 2025 METR study kind of often. So I thought I'd try posting a top level thread, consider it an unsolicitied public service announcement.

You might be familiar with the study because it has been showing up alongside discussions about AI and coding for about a year. It found that LLMs actually decreased developer productivity and so people love to use it to suggest that the whole AI coding thing is really a big lie and the people who think it makes them more productive are hallucinating.

Here's the thing about that study... No one seems to have even glanced at it!

First, it's from early 2025, they used Claude Sonnet 3.5 or 3.7. Those models are no way comparable to current gen coding agents. The commonly cited inflection point didn't happen until later in 2025 with, depending on who you ask, Sonnet 4.5 or Opus 4.5

The study was comprised of 16 people! If those 16 were even vaguely representative of the developer population at the time most of them wouldn't have had significant experience with LLMs for coding.

These are not tools that just work out of the box, especially back then. It takes time and experimentation, or instruction, to use them well.

It was cool that they did the study, trying to understand LLMs was a good idea. But it's not what anyone would consider a representative, or even well thought out, study. 16 people!

But wait! They did a follow up study later in 2025.

This time with about 60 people and newer models and tools. In that study they found the opposite effect, AI tools sped developers up (which is a shock to no one who has used these tools long enough to get a feel for them). They also mentioned:

However the true speedup could be much higher among the developers and tasks which are selected out of the experiment.

In addition they had some, kind of entertaining, issues:

Due to the severity of these selection effects, we are working on changes to the design of our study.

Back to the drawing board, because:

Recruitment and retention of developers has become more difficult. An increased share of developers say they would not want to do 50% of their work without AI, even though our study pays them $50/hour to work on tasks of their own choosing. Our study is thus systematically missing developers who have the most optimistic expectations about AI’s value.

And...

Developers have become more selective in which tasks they submit. When surveyed, 30% to 50% of developers told us that they were choosing not to submit some tasks because they did not want to do them without AI. This implies we are systematically missing tasks which have high expected uplift from AI.

And so...

Together, these effects make it likely that our estimate reported above is a lower-bound on the true productivity effects of AI on these developers.

[...]

Some developers were less likely to complete tasks that they submitted if they were assigned to the AI-disallowed condition. One developer did not complete any of the tasks that were assigned to the AI-disallowed condition.

[...]

Altogether, these issues make it challenging to interpret our central estimate, and we believe it is likely a bad proxy for the real productivity impact of AI tools on these developers.

So to summarize, the new study showed a productivity increase and they estimate it's larger than the ~20% increase the study found. Cheers to them for being honest about the issues they encountered. For my part I know for sure that the increase is significantly more than 20%. The caveat, though, is that is only true after you've had some experience with the tools.

The truth is that we don't need a study for this, any experienced engineer can readily see it for themselves and you can find them talking about it pretty much everywhere. It would be interesting, though, to see a well designed study that attempted to quantify how big the average productivity increase actually is.

For that the participants using AI would need to be experienced with it and allowed to use their existing setups.

I want to add that this is not an attempt to evangelize for AI. I find the tools useful but I'm not selling anything. I'm interested in them and I stay up to date on the conversations surrounding them and the underlying technology. I use them frequently both for my own projects and to help less technical people improve their business productivity.

Whether AI agents are a good thing or not, from a larger perspective, is a very different, and complicated, conversation. The important thing is that utility and impact are two different conversations. There isn't a debate anymore about utility.

I know this probably won't stop people from continuing to derail conversations with the claim that developers are wrong about utility, but I had to try. It's just hard to let it pass by when someone claims the sky is green.

I understand that AI makes people angry and I think they have good reason to be angry. There are a lot of aspects of the AI revolution that I'm not thrilled about. The hype foremost, the FOMO as part of the hype, the potential for increased wealth consolidation really sucks, though I lay that at the feet of systems that existed before LLMs came along.

It's messy, but let's consider giving the benefit of the doubt to professionals who say a tool works instead of claiming they're wrong. Let them enjoy it. We can still be angry at AI at the same time.

A few months ago someone posted an article on the male loneliness epidemic. I had shared my thoughts in the comments on that post. But I think that article and a lot of comments are under the impression that "redpill" content/ideology is still in vogue or relevant in today's world. It still has its followers and influencers for sure, but it's not at the forefront of cultural discussions anymore. To think otherwise is outdated, the redpill era died around 2022/2023 and was replaced by a more incel-derived "blackpill" era.

Thanks to TikTok, what was once relegated to niche internet forums became mainstream. The biggest influencer from this internet phenomenon is Clavicular, who is currently getting articles written about him in press outlets such as The Hollywood Reporter and People.

I am no stranger to talking about looks (side note: I would have taken more time to write that out and discuss broader topics, such as "types," if I had known it would have gotten as much attention as it did). And I have been around looksmaxxxing spaces on the internet since about 2022. I'll try to make this as brief and simple as I can.

What is the blackpill?

The blackpill is a deterministic outlook on life. It states that your genetics determines the quality of your life, and if you were not born with advantageous bone structure and height, then "it was over before it even started." You won't be successful in life, you won't find love, and you will end up a lonely, pathetic person wishing you'd been born looking better.

How does this differ from the redpill?

The redpill has some overlap with the blackpill. Both believe that men are the true victims of society. That feminism has been detrimental, this and that, and the other. The redpill, however, insinuates that you can self-improve. There's almost zero focus on improving looks, and it's almost entirely focused on making money and increasing your status.

A core belief of the redpill is that all women are gold diggers, and in order to get laid, you need to make a ton of money. The blackpill does not entirely dispute this, but it does say that if a woman chooses you for money, she will never actually love you. And that you are paying a lot of money for affection and attention that an attractive man gets for free.

I think that explanation in and of itself should show you the difference between the two.

What's looksmaxxxing? Are looksmaxxxing and the blackpill interchangeable terms?

They are not.

Looksmaxxxing is what guys do to look better, to increase their rankings on the looks scale. So that they can start getting laid (primarily) or start to "mog" (i.e., outshine everyone in a room).

In certain blackpill spaces looksmaxxxing is seen as cope, since, again, your life was determined by genetics and there's nothing you can do to fix this.

You might think looksmaxxxing consists of losing body fat, getting a skin care routine, dressing nicely, hygiene, and cologne. And that is part of it, all of that stuff is considered "softmaxxxing" but there's also "hardmaxxxing" as in surgeries and other more serious treatments such as steroids and peptides (which technically occupy a grey area between soft and hard maxxxing). An example of a popular surgery is double jaw surgery, here's the subreddit for it so you can see examples. If your jaw was not properly developed and you have a recessed chin (or a pushed-in chin), then a double jaw surgery is something you can do that would greatly increase your attractiveness. Although it does carry quite a bit of risk. There are other surgeries that people do on their eyes, their noses, ear pinning, there's a lot.

It is essentially a belief that your best investment is going to be in how you look. It's a bit of a running joke that instead of going to college, you should invest in plastic surgery, and that will do more to make your life better than a degree.

How do they view women? How do they view themselves?

The belief is that women are hypergamous. That they will only want to date up, and it's significantly easier for women to date and get laid, even if they are below average looking. And that even an above average looking man will have trouble since they aren't the holy grail of attractiveness.

Here's a brief explanation of their rating system.

• Sub-5

5 is considered average; sub-5 means below average. Not even that you don't get attention but that you get negative attention from the people around you.

• Low-Tier Normie / Low-Tier Becky
• Mid-Tier Normie / Mid-Tier Becky
• High-Tier Normie / High-Tier Becky

The "normie" categories are all average categories. Ranging from on the low side of average (LTN) to above average (HTN). The High Tier categories are where a lot of attractive actors sit, think romcom leads, the boy/girl next door types.

• Chad/Stacy

Essentially unobtainable beauty. Taylor Hill or Henry Cavill.

Depending on who you're talking to, someone would say that "life starts at HTN" or that life doesn't exist unless you're "Chad." And that if you're anything below that, you might as well not even exist.

How did it get popular?

The first instance most people probably heard of it was likely in 2014 when Elliot Rodger committed a mass shooting at a University. He was a member of a looksmaxxxing forum (the original looksmaxxxing forum, I believe), which led to the site being shut down and thus delaying any chance of its popularity. If you go back and watch and read what Elliot Rodger believed, it makes more sense in today's context now that this thought process has been more normalized.

In 2023, TikTok started promoting this content. Primarily from "edits" here's an example and coinciding with that were also the rise of a few influencers. All leading up to Clavicular, and how dominant he is on social media (thanks in part to funding from Peter Thiel). He was a kid posting on looksmaxxxing forums, was a micro celebrity in the niche, became a slightly bigger internet celebrity on TikTok before streamers started bringing him on leading to his insane fame.

Conclusion

Going back to the initial tildes post that I linked to. That whole thing was essentially saying, if you're just a good person, then someone will want to date you or fall in love with you or want to have sex with you or whatever. And I think part of the reason why looksmaxxxing stuff has taken off is that it feels more honest. It's not coddling you, and if you do improve your looks, you're going to see better results in dating than if you read feminist literature or something. So the takeaway ends up being that one of these places was telling me the truth.

Like, on a broader scale, it's a response to the body positivity stuff from the 2010s. When everyone was being told that it's okay if you're obese, it's healthy, it's beautiful. And there was just kind of a sense of performance to all of it.
The effort to change what people are attracted to, or to shame people for not being attracted to a certain thing. Has it gone too far? Probably, but I think that's why it took off initially and why it grew so quickly.

I obviously have my own personal experience about this, and so I very obviously know that it's not just what's inside that counts. Normal everyday people will make assumptions about you based on the way that you look. And I don't think it's a morally wrong thing to acknowledge that it happens, nor do I think it's a morally righteous thing to pretend like it doesn't.

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.

So with the Paramount acquisition, all the new HP content, and the general state of both TV and Movie ownership are people returning to the high seas?

I was an eager participant of the first and second wave of piracy in the early and late 00s, and considering the re-consolidation of the entertainment industry and the seemingly nefarious acquisitions of late, I am considering hoisting the black flag once again. I guess this post has two objectives: 1. how are other people navigating our changing media landscape, and 2. for those who have stayed immersed in piracy or have returned to it how have things changed in the last decade or so. Obviously Megavideo and Putlocker are no more, so are there directions to point folks who are just getting back to it. This can be streaming, torrenting, anything really.

Caveat: Let's not even give the horrible human that is JK airtime. I mentioned HP because folks might want to indulge without supporting but if we can keep the discussion to piracy that would be awesome!

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?

This is a recurring post to discuss programming or other technical projects that we've been working on. Tell us about one of your recent projects, either at work or personal projects. What's interesting about it? Are you having trouble with anything?

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.

Increasingly I'm looking at my backup solution and I'm not totally happy. My "threat model" I guess is if the house burns down and we only make it out with the shirts on our backs. Alternatively if I get hit by a bus I'd like a backup of passwords and maybe some instructions for my wife.

TL;DR What are decent medium term cold storage options for < 1gb that I can be really sure will be good for several years (maybe 10 or 20 years at the extreme end) and is fairly cheap. I was thinking optical media but I'm kind of lost as to what specifically to get and how to not get conned by buying fake media (m discs). I (somewhat randomly) have an m disc drive in my computer but I don't know if thats overkill or not? My important stuff may even fit on a CD actually...

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.