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.

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!

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.

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.

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

Title.

This is my machine:

• OS: Windows 10
• Video editing software: Adobe Premiere
• Display (ED320QR S): 1920x1080 @ 144 Hz in 28" [External]
• CPU: AMD Ryzen 5 2400G (8) @ 3.60 GHz
• GPU: NVIDIA GeForce RTX 2060 12GB [Discrete]
• Memory: 64 GiB (7%)
• 1TB NVME SSD (I can't get the info right now on Linux, but it's a very good SSD)

I am looking into getting back into video editing for my personal projects.

My program of choice was and is Adobe Premiere.

So, how much has changed since then, and what is the best way for me to get up to speed? Do my knowledge and assumptions from 2014 more or less translate to current versions of Adobe Premiere? Should I use some other program instead? Are there any courses, summaries, or cheat sheets you would recommend?

I should probably clarify that going back to editing is a source of distress for me, since it was something I was too emotionally invested in back then, leading to a significant burnout. So I would like to overcome some of that emotional fragility by mapping the terrain a little bit before going back to it.

Back in the day, I used to love the courses on lynda.com. Something along those lines might help alleviate some of that anxiety.

Thanks!

This thread is posted weekly - please try to post all relevant US political content in here, such as news, updates, opinion articles, etc. Extremely significant events may warrant a separate topic, but almost all should be posted in here.

This is an inherently political thread; please try to avoid antagonistic arguments and bickering matches. Comment threads that devolve into unproductive arguments may be removed so that the overall topic is able to continue.

I mean the blue light filter that's built-in in most phones and computers. on Windows, it's called "Night Light" and on most android phones, it's called "Reading mode".

so my question is, do I need not that at all and if the glasses accomplish the same thing? or if they're completely different things. I don't even know if the Anti-Reflective coating provides any protection from blue light.

also I read that blue light can disrupt sleep but I don't really have any problems sleeping, even though I don't use the night light/reading mode

Hi,

I’m hoping to tap into the collective knowledge base. I have officially ‘run out’ of notes on my task/notes app Tick Tick. I had no idea this was a thing.

I prefer to avoid paying subscription fees so I’m looking for a pay once alternative that fits my needs.

Tick tick is primarily a task / to do list app but I use it more extensively for the notes feature. I value having different spaces for notes and being able to quickly see my notes tags when looking at lists of notes. I also use the countdown feature to know which events are coming up first and how many days away they are.

I use the notes to organise bookings I have for upcoming events. I use the tags system to quickly see if deposits have been paid and what category of booking the event is. I also value being able to import a template first to keep notes structured in the same way.

Another thing that works for me is that tick tick is available on Mac, iOS and Android. All platforms that I use.

If they had a pay once option I’d pay it but I work hard not to rely on too many subscriptions, perhaps naively in this economy.

If anyone has any suggestions for similar apps then I’d love to know your recommendations.

Thank you so much