null000's comments

> it seems faster for its intended use...

But then you have to store the entire before & after locally? That's the entire point of using a hash for change detection.

Counter-anecdote: I was diagnosed sixish months ago, prescribed medication about 3 months ago. The meds don't have the same high of alcohol or weed, but there's definitely an occasional deep sense of calm and wellness - one that's hard not to want, being my chronically depressed self. Definitely enough to set off my "never abuse this, it will end badly" mental alarm

Otherwise though, it checks all the usual boxes - way easier to sit still and be quiet, way easier to do the thing I intended to do when I sat down to do it, way easier to get in and out of focus. Also improved my sleep schedule a surprising amount - between it and melatonin, I've moved from 6am-3pm to ~12am-8:30am, which is wildly outside any of the expectations I've seen set for pharmaceutical remedies, and puts me firmly in 'able to hold a normal adult job without killing yourself through sleep deprevation' territory.

Honestly almost annoying it has such a positive effect since the practical difference between that and addiction are pretty minimal on a short-term basis. I'm deeply uncomfortable with the idea of having to go without it or something filling the same brain-holes for a long period of time precisely because I'm basically worthless without it, in a way I didn't really come to appreciate until after starting medication.

I don't think your last point is a good thing. It sounds like the problem with Academia is the source and requirements of the funding, rather than the work itself.

I'd much rather academia had ample enough funding where people could work on what they wanted and what they felt was useful without the need to appeal to large businesses or metaphorically knife-fight for grants.

The academic/business split is weird. In business, you're much more likely to be unknowingly treading on known ground, but the visibility of lessons learned is, in most cases, incredibly narrow. I've worked on compilers blindly implementing features I know other competitors have worked on but can't cheat off of, and I've worked on systems programming issues that probably pushed at the state of the art, but whose lessons wouldn't go outside my team in any case.

In academia, though, there's a whole host of obstacles to doing anything useful and interesting that have nothing to do with "the problem at hand". So while you can be more confident in the relative novelty of what you're doing, as well as the broad applicability of said work (since the whole point is publishing) the scope of things you actually can work on is incredibly limited until late in your career.

I blame religion. Not all religion, not every religious person, and not exclusively, but looking back - there was a strong emphasis that "we're all awful people really at our core, and the only reason anyone is half decent to anyone is because they don't want to burn in hellfire" in my religious teachings (protestant Wesleyan, primarily).

I bought into that for a long time. Then I dropped religion, and realized I didn't really want to be an awful person, just as a rule of thumb. It wasn't too much of a jump to realize most people weren't awful most of the time, and it's only a handful that, for whatever reason, didn't get the memo. Even if many people manage to convince themselves otherwise.

This is an article written by an angry user whose usecase isn't well supported.

Like, it's a valid gripe I guess, assuming you think music storage and streaming should be perpetually free even if you didn't buy any of it on the platform, but it just doesn't apply to 90% of the userbase - who do license their music through a monthly subscription rather than buying it outright.

Don't get me wrong, I'm not exactly excited about the shutdown of Google Music - but it's hard to take this author seriously.

This doesn't need to happen, but this is the logical conclusion when we ensure most of the housing market is controlled by landlords rather than owned directly by the people living there. Also, when you turn housing into an investment vehicle. Also, remove any and all public housing alternatives. Also fail to build enough housing to create competition in the housing market. Also demolish housing stock serving poorer residents. Also fail to provide the transportation network to make transit from the suburbs cheap and easy. Also concentrate a bunch of upper-middle-class people in an area suffering from all of the above problems.

I played through the game and did the pre- and post- game study.

At the beginning, I tried pretty hard to genuinely gauge reliability, and was more willing to use some nuance (maybe I don't think the tweet is written fairly, but it touches on a genuine trend, so I'll give it a 3/10). There was also one that I just let slip by me (HBO tweet with some random characters that I realized probably weren't intentional markers to dodge trademarks after I instinctively hit 10/10 reliability, and also you can't go back and correct yourself).

After the game I was just so worn out I didn't feel like giving any credence to anything. Looks like you're kinda emotional? 1/10. Talking about an opinion instead of a fact? 1/10. From a celebrity? 1/10. It also looked like they weren't throwing in many or any tweets that were supposed to be "credible" or "reliable" in the feed.

I'd be interested in seeing the results 2 or 3 weeks out, and with a more even mix of "credible" and "non-credible" tweets. I have the suspicion the results won't stick, or at least won't stick well.

The whole point is that the algorithm doesn't know about obstacles or success as a concept baked into the algorithm. Likewise, this is pretty initial research, meant to inform and promote

In other words, this isn't meant to be super useful by itself. It seems tailor made (as many of these things do) to play super-simple 80's video games and literally nothing else, but it's an interesting proof of concept. I'd also be interested in different iterations on this general pattern - for instance, something that didn't translate directly from screen + button -> prediction, and instead had some interstitial systems - translating from screen -> entities, then predicting entity state of entities given button presses. It'd also be interesting to see how this performs with ML algorithms designed to learn on the fly instead of through training from a static set of data (at least, this looked like it learned through back propagation - I skimmed).

But I can see broader practical applications for this in, for instance, recommender systems trying to break users out of the closed feedback loop that people tend to end up in when going down certain rabbit holes (e.g. watch one Flat Earther conspiracy video and suddenly that's all you see for a week because the recommender system knows that people who look at one will look at more). The point being: the real test comes when this strategy is exposed to more diverse problem spaces, it's just that those are harder to model and we need to weed out the pointless stuff first.

Tbf, the problem comes in when developers (or someone - developers are probably just the most visible) make a pile of cash for coming in, pushing renters out of a small numbed of relatively affordable units, ploping down a building with a large number of completely in affordable units, and causing a huge headache for everyone living in the area in the process.

I'm generally for development, but the way it's carried out in many cases tends to screw over existing residents while benefitting mostly just people-who-dont-live-there.

I think that's a side effect of the modern expectation that everyone move cross country on a dime if it makes sense economically. Kinda hard to figure out ecological best practices when families can barely stick around for a generation and people constantly move into areas they're just not familiar or comfortable with.

It takes a long time for best practices to become accepted and normal, and there just isn't the geneological inertia to develop that anymore

Keep in mind that they might only get a room, but it's probably a pretty damn big room. And otherwise nice.

Not sure I'd take that tradeoff at my current age, but considering I lived in a shoebox with a shared bathroom during College for maybe 3/4 the price post-inflation, I'd have taken it in a heartbeat.

The nice thing is that they're at least moving those people out of the lower tiers of housing.

If someone can afford $5000 a month for rent, I'd prefer they compete for mansions that would otherwise remain vacant than townhomes which might otherwise go to people who can only afford $2000 a month.

That's kinda like saying "Age is a protected class. But being on a fixed income isn't"...

One of the things I think is under-discussed (although the article does touch on this) is that this is another move to take customers from buyers to renters. It used to be that customers bought things, and then they owned them - games included.

Now, we've removed legal "ownership" basically entirely, as well as physical "ownership" when dealing with online stores when it comes to video games. This would take that a step further - now you don't even own the hardware producing your content - instead you'll likely need to pay an ongoing fee for access, as well as probably a fee for content.

> a fertility crisis or environmental collapse

Not sure it really makes sense to fix a lack of people with something that's extremely labor intensive (at least up front). Not to mention, getting robots to be cheap requires a really highly advanced society, which requires a lot of people and a lot of time.

That said, it'd be interesting to see at what point or under what conditions a robot's total labor output exceeds it's total labor input (including watching it, debugging, programming, etc)

> the technology in which they are an expert is obsolete

A lot of the principles, idioms, patterns, and instincts experts pick up transcend technology. Sure, they won't be able to optimize a framework they've never interacted with, and some bugs will probably require a trip to stack overflow, but they know how different pieces fit together, where to go for help, what it looks like when something isn't working, best practices for avoiding a huge catastrophe, and - probably most importantly - the wetware people skills required to get things done and done right.

Being a master software engineer, in other words, has almost nothing to do with the particulars of the software.

I'm reminded of the three panel meme comic.

"Yeah, we have passenger train service"

"good passenger train service?"

"Hey, lets not get ahead of ourselves"

Amtrak is heavy rail - that's light rail, which is kinda different.

I've been listening to the Revolutions podcast recently, and I'm currently in the post-french-revolution series of European revolutions. Kinda funny - France tried setting up some guaranteed-work promises and implemented them through a series of National Workshops, where you could theoretically show up, do some labor for the country, and go home after a full day with subsistence wages.

Of course, the people proposing/pushing for the idea and the people implementing the idea did not share a lot of membership, so the implementers put a person deeply opposed to the project in charge. Of course, no projects of use got approved, most of the projects that did get approved were obviously pointless and tedious, and they were constantly thousands of jobs short or what was needed to employ everyone who wanted work anyway.

So yeah, that's why we have the "digging and filling holes" analogy for useless make-work. Ideas are fine and all, but it doesn't matter for shit if you have an antagonist implementing them.