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!

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.

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!

I recently came across an interesting-looking indie game, Idols of Ash. Basically, you have to use a simple grapple-and-swing mechanic to descend through an eldritch underground complex while being pursued by a dangerous "murderpede" monster.

I first played it on what I thought was the official site, idolsofash.fun. It's a pretty spiffy design, with a playable web version, extensive FAQs, strategy guides, and embedded images and video of the game. But I ran into some bugs while playing -- no sound effects, weird lighting. When I mentioned these flaws on the developer's Itch.io page, they responded that they had nothing to do with the site.

Turns out it has a disclaimer at the very bottom: "Unofficial fan site. Not affiliated with or endorsed by Leafy Games." Buying and installing the actual version solved my tech issues. And in playing the game more, I noticed that the various guides on the site were subtly wrong in a lot of ways. The About page claims it's maintained by a big fan of the game, but in hindsight the whole thing seems AI-written and full of hallucinations.

Thing is, I don't get the angle here. There's no advertising on the site. It prominently links directly to the game's official Steam and Itch pages, so they're not trying to deliver malware or intercept the developer's sales. I assume the glitches are from a poor decompilation and rehosting of the original Godot engine game, but there's nothing to be gained from that. The presence of images and video suggests some level of human involvement in the site design, meaning it's not some cheap fire-and-forget thing. The URL and content are far too specific to flip into something else after gaining SEO rank. It presents (and acts) exactly like a non-commercial labor-of-love fansite (albeit one that shares the paid game for free in a broken state).

Could this be a genuine, if misguided, attempt by an actual fan to share the game using AI tools? Or is there some kind of scam I'm not seeing? Is this sort of fake AI fansite with embedded versions of the game a widespread problem with indie titles now?

I used to really value the art of essay writing. There seemed to be such a richness in the different ways people would construct arguments, structure those arguments, then deliver those arguments stylistically, not just from the perspective of being persuaded as a reader but also from the perspective of seeing how a given writer thinks, relates to the living tradition of language, and understands the world conceptually. But it's basically lost most of its meaning to me in this age of LLMs. The reality is, LLMs are capable of writing texts that, if you gave them to a seasoned reader 5 years ago, they'd say it was well written and indicative of a truly thoughtful mind. Even if there currently exist certain tells with LLMs, those styles certainly existed in different ways in real human writing beforehand. Now, those perfectly reasonable set of styles are verboten and we have to dedicate half our deep focus to figuring out whether, or to what extent, an essay or article was written by AI. It's difficult to enjoy, let alone care, about essay writing and the writers behind them now.

I can still find value in books, though, because they were written in the past and I don't mind never reading any non-scientific book published after 2022 if it comes down to it.