npunt

Short story: not worth it because LG has terrible quality control. I bought two copies and experienced bad light uniformity / banding with both, color repro wasn't great, and the matte finish was a bit fuzzy. Many others have had this issue.

Here are photos of what I saw:

https://www.reddit.com/media?url=https%3A%2F%2Fpreview.redd....

I wound up buying the Asus ProArt 32" and out of the box it had good light uniformity, a better matte finish, better color accuracy (using the M-Model P3 profile), and was much cheaper.

I recently got this after getting three copies of the LG 32U990A, which had serious light banding and uniformity issues. Loving the Asus.

Yeah, after a period of general stability where power was more even distributed among different groups of people (pols, media, finance, labor, edu, etc), we've found ourselves at the mercy of this dangerous new concoction of naive software engineers and business sociopaths that has escaped the lab and run amok over the world. Sociopaths always find a way to harness the ignorant but powerful, and this time its the software engineers.

Part of the role of college education is to expose students to the broader world and help them become informed members of society, raising unanswered/unanswerable questions, getting young people to think and grow and find their place to contribute in the great experiment of civilization. Cramming for exams is def part of the college experience but so is/should be these listen to the wisdom of your elders kinds of talks, even if some are kooky or you don't agree w aspects of them.

Discourse around college education has shifted a lot in the last 20 years toward a kind of optimization for job readiness, which itself is both a reflection of economic conditions and a misunderstanding of what elements are necessary for civilization to persist and thrive. College is supposed to be full of messy ideas among a menu of disciplines to challenge us and help us find our passions, and it's supposed to prepare us to become members of a society where all of these ideas and disciplines co-exist. In other words, college is under-optimized for the individual because its purpose is to optimize for society as a whole.

The kind of bigger picture discussion that this lecture is doing is especially important in engineering disciplines since they don't focus much on humanities and the stuff they get isn't tailored to their approach and mindset. We might live in a different world if a little more 'why' had been introduced into the 'what' and 'how' of eng education.

wait til you find out who supplies iPhone screens.

Haha yes hopefully Oracle works its anti-magic on TikTok, that'd be lovely to see.

Agree social & network effects are essential to achieving the mission. We (Margins) are building that part out now after spending the last year and a half perfecting the single player experience, it's very early but so far it's going great and we went viral again this holiday season. Social also needs something different than the 'let's make an early 2010s social product' approaches I keep seeing people trying, that stuff just cannot work in the age of AI bots poisoning the commons.

I don't think any of the existing players are close to the quality levels of TikTok/Insta, certainly not GR or SG, and there's always new copy/paste projects that come and go, its kinda become a genre of solo dev project like weather apps. I also think all these Goodreads-like apps are following the wrong formula to win the mainstream, they tend to get stuck appealing to niche user interests. Of all of them Fable made the best attempt at it but wasn't ever original enough in formula or high enough in quality, and they burned through $27m largely failing to find PMF and sold for peanuts.

Local bookstores are necessary/essential parts of winning back attention from social media and offer the unique value of physical presence and community, but will never be sufficient to get all the people addicted to these digital drugs away from their apps. Apps really are powerful things that offer unique experiences that people have come to expect, so I think a realistic theory of change involves an app being the medium to route attention to healthier ends. How exactly to do that is indeed the challenge!

In the Aqua image the big bright blue scrollbars stand out far, far more than the content. That sucks, honestly. So does the percentage of the screen dedicated to their presence.

Also, horizontal scrollbars suck. One thing later versions of Finder did well was adjust columns to minimize the presence of them.

We just don't need UI that big anymore. These days our cursors are much more accurate, from the magical Mac trackpad to high DPI optical mice, and we're 40+ years into GUIs so the limited number of people who opt-in to a full computing experience can already be expected to know the basics.

Yes Tahoe sucks, but going back to Aqua or classic MacOS would also suck, just in a different direction. If you actually spend time using classic MacOS and Aqua these days, man is it frustrating to get basic things done. Everything is so slow and you're constantly resizing windows to see whats in them. I own several Macs from the 80s-00s and they are really in need of many quality of life updates that later MacOS revs added. On a modern Mac, enabling 'show scrollbars' gets you to a pretty optimal Finder experience, minus all the stupid Mac bugs and Tahoe nonsense like this article points out.

safest place is put it opposite of drivers side, because if you're out of gas on the side of the road and filling it up, you won't be standing right next to freeway traffic. Saab started this.

"Your scientists were so preoccupied with whether they could [run macOS on a phone], they didn't stop to think if they should." - Ian Malcolm, Jurassic Park

they make $1b in revenue and $300mm a day in profit

Institutional knowledge is scoped to members of an organization and covers things related specifically to the institution's operations. What I'm talking about is the knowledge of the general population, often as it relates to an institution's products.

For instance, tons of people know how to use Adobe products like Photoshop, by way of deliberate inaction on the part of Adobe around product piracy outside of workplaces. With this large knowledge pool entering into the workforce, users were able to convince workplaces to adopt Adobe products that they were already familiar with.

That wouldn't be institutional knowledge, but a pool of knowledge that institutions could take actions (or inaction, as the case above) to influence.

I've never found a term I liked for this particular concept at the intersection of education & business so I made one up a while back:

A Knowledge Pool is the reservoir of shared knowledge that a group of people have about a particular subject, tool, method, etc. In product strategy, knowledge pools represent another kind of moat, and a form of leverage that can be used to grow or maintain market share.

Usage: Resources are better spent on other things besides draining the knowledge pool with yet another new interface to learn and spending time and money filling it up again with retraining.

I don't think the medium of short form video is redeemable. Its occasional positives are the delivery vehicle for the many negatives, the sugar coating around the poison. It's rotten by just about any measure: properties of the medium, what types of business it attracts, messages that thrive, how well it reflects reality, aggregate effects on people across a variety of outcomes, the aftertaste of using it, etc.

I think the best question to assess it is does this make us better people or not, and to what degree? From what I have seen, the answer is it seems to be pretty significantly de-skilling us in attention, agency, nuance, psychological wellbeing, etc. It makes us more vulnerable to influence and manipulation. The businesses that benefit the most from deploying it are advertising-based, which naturally leads to surveillance and algorithms and pace of consumption that maximizes addiction. The messages that perform best are emotional and attention seeking. There is no information quality control. The consumption pattern it suggests leaves no room for thinking, processing emotion, or nuance.

The personal crusade I'm on is to build a competing product at the quality level of TikTok/Insta that diverts interest & attention toward books, which as a medium is both a lot more of a known quantity and whose consumption naturally results in longer attention spans, greater literacy, and all the nth order consequences of written culture. It's great that things like BookTok exist but ultimately that energy & activity needs to find its way over to a healthier home.

Normally the 1 sentence per para LinkedIn post for dummies writing style bugs me to no end, but for a technical article that's continually hopping between questions, results, code, and explanations, it fits really well and was a very easy article to skim and understand.

And your response here is a perfect example of confidently jumping to conclusions on what someone's intent is... which is exactly what you're saying the LLM did to you.

I scoped my comment specifically around what a reasonable human answer would be if one were asked the particular question it was asked with the available information it had. That's all.

Btw I agree with your comment that it hallucinated/assumed your intent! Sorry I did not specify that. This was a bit of a 'play stupid games win stupid prizes' prompt by the OP. If one asks an imprecise question one should not expect a precise answer. The negative externality here is reader's takeaways are based on false precision. So is it the fault of the question asker, the readers, the tool, or some mix? The tool is the easiest to change, so probably deserves the most blame.

I think we'd both agree LLMs are notoriously overly-helpful and provide low confidence responses to things they should just not comment on. That to me is the underlying issue - at the very least they should respond like humans do not only in content but in confidence. It should have said it wasn't confident about its response to your post, and OP should have thus thrown its response out.

Rarely do we have perfect info, in regular communications we're always making assumptions which affect our confidence in our answers. The question is what's the confidence threshold we should use? This is the question to ask before the question of 'is it actually right?', which is also an important question to ask, but one I think they're a lot better at than the former.

Fwiw you can tell most LLMs to update its memory to always give you a confidence score 0.0-1.0. This helps tremendously, it's pretty darn accurate, it's something you can program thresholds around, and I think it should be built in to every LLM response.

The way I see it, LLMs have lots and lots of negative externalities that we shouldn't bring into this world (I'm particularly sensitive to the effects on creative industries), and I detest how they're being used so haphazardly, but they do have some uses we also shouldn't discount and figure out how to improve on. The question is where are we today in that process?

The framework I use to think about how LLMs are evolving is that of transitioning mediums. Like movies started as a copy/paste of stage plays before they settled into the medium and understand how to work along the grain of its strengths & weaknesses to create new conventions. Speech & text are now transitioning into LLMs. What is the grain we need to go along?

My best answer is the convention LLMs need to settle into is explicit confidence, and each question asked of them should first be a question of what the acceptable confidence threshold is for such a question. I think every question and domain will have different answers for that, and we should debate and discuss that alongside any particular answer.

I'm not so sure; that may not have been what you meant, but that doesn't mean it's not what others read into it. The broader context is HN is a startup forum and one of the most common discussion patterns is 'I don't like it' that is often a stand-in for 'I don't think it's viable as-is'. Startups are default dead, after all.

With that context, if someone were to read your comment and be asked 'does this person think the product's model is viable in the long run' I think a lot of people would respond 'no'.

Filed under: trying to solve another domain's problem with only the tools in your domain (this is almost always a bad idea)

One of the few use cases for LLMs that I have high hopes for and feel is still under appreciated is grading qualitative things. LLMs are the first tech (afaik) that can do top-down analysis of phenomena in a manner similar to humans, which means a lot of important human use cases that are judgement-oriented can become more standardized, faster, and more readily available.

For instance, one of the unfortunate aspects of social media that has become so unsustainable and destructive to modern society is how it exposes us to so many more people and hot takes than we have ability to adequately judge. We're overwhelmed. This has led to conversation being dominated by really shitty takes and really shitty people, who rarely if ever suffer reputational consequence.

If we build our mediums of discourse with more reputational awareness using approaches like this, we can better explore the frontier of sustainable positive-sum conversation at scale.

Implementation-wise, the key question is how do we grade the grader and ensure it is predictable and accurate?

I grew up a few blocks from his funky Santa Monica house [1], passed by it all the time. When you’re a kid you typically see wild new things like that as just normal because you have no context for how unusual they are. His house defied that perspective; even as a kid you understand that being wrapped in oddly angled chain link fences and corrugated metal is just... different. It's an unanswered question, a loose thread, a thing you can't unknow.

I don't particularly like the house - it's meant to be challenging not beautiful - but with perspective I see now there aren't many creations out there that achieve existence in eternal confusion like it does for me. I see his other works like Bilbao [2] and Disney Hall as refinements on the concept with the added dimension of beauty. They're not quite as memorable, but I think do a great job exploring the frontier of beauty and befuddlement.

[1] https://en.wikipedia.org/wiki/Gehry_Residence

[2] especially the aerial perspective https://en.wikipedia.org/wiki/Guggenheim_Museum_Bilbao#/medi...

Perhaps you missed the product positioning: “Simple setup and clean design”

Not everyone wants to fix old hardware and configure linux on their weekends

The irony is that AI writing style is pretty off-putting, and the story itself was about people being put off by the author's AI project.

I think all of these are true:

1. Apple is big enough that it needs to take care of edge cases like offline & limited cell reception, which affect millions in any given moment.

2. Launching a major UI feature (Siri) that people will come to rely on requires offline operation for common operations like basic device operations and dictation. Major UI features shouldn't cease to function when they enter bad reception zones.

3. Apple builds devices with great CPUs, which allows them to pursue a strategy of using edge compute to reduce spend.

4. A consequence of building products with good offline support is they are more private.

5. Apple didn't even build a full set of intents for most of their apps, hence 'remind me at this location' doesn't even work. App developers haven't either, because ...

6. Siri (both the local version and remote service) isn't very good, and regularly misunderstands or fails at basic comprehension tasks that do not even require user data to be understood or relayed back to devices to execute.

I don't buy that privacy is somehow an impediment to #5 or #6. It's only an issue when user data is involved, and Apple has been investing in techs like differential privacy to get around these limitations to some extent. But that is further downstream from #5 and #6 though.

Discoverability is quite literally the textbook problem with CLIs, in that many textbooks on UI & human factors research over the last 50 years discuss the problem.

It's a great feature! I was demoing it to my parents over Thanksgiving and forgot about the lack of Siri support, and of course it failed. Parents were excited when I mentioned it but now won't be using it. Ah well.

With 1B+ users Apple isn't in the position to do the typical startup fast & loose order of operations. Apple has (rightly) given themselves the responsibility to protect people's privacy, and a lot of people rely on that. It'd be a really bad look if it turned out they made Siri really really useful but then hostile govt's all got access to the data and cracked down on a bunch of vulnerable people.

Apple Reminders has a feature to remind you when you are leaving or arriving at a location. It's super useful! But it's not super low friction to add to a Reminder via UI (it's buried at the bottom of the edit screen), so it's a feature ideally suited for a voice-based reminder. Nevertheless, nobody implemented it.

Yeah, it's a classic CLI v GUI blunder. If you don't know exactly what the commands are, the interface is not going to be particularly usable.

I've found I appreciate having Siri for a few things, but it's not good enough to make it something I reach for frequently. Once burned, twice shy.

I'm amazed more AI tools don't have reality checks as part of the command flow. If you take a UX-first perspective on AI - which Apple very much should - there's going to be x% failures to interpret correctly, causing some unintended and undesirable action. A reasonable way to handle these failure cases is to have a post-interpretation reality check.

This could be personalized, 'does this user do this kind of thing?' which checks history of user actions for anything similar. Or it could be generic, 'is this the type of thing a typical user does?'

In both cases, if it's unfamiliar you have a few options: try to interpret it again (maybe with a better model), raise a prompt with the user ('do you want to do x?'), or if it's highly unfamiliar, auto cancel the command and say sorry.

Was the failure really driven by privacy policy? Long term a privacy play is the right move. But right now, Siri's capabilities even underwhelm vis-a-vis a model with no understanding of user context that is just interpreting commands.

I'm amazed 'set a reminder for x when I leave this location' still doesn't get the 'when I leave this location'. It's clear user expectation created internally (by siri marketing) and externally (by ai tools) has far outpaced capability.