Is your post any better? You attack GP's character without addressing any of his arguments. You could have provided evidence for why you think his speculation is wrong, or tried to point out how his logic falls apart.
Photocraft has been working alright for me. I really struggled to learn GIMP, and I haven't found any easy lightweight image editing tool that replaces Paint.NET for me before trying Photocraft. It's still a little confusing since I'm not used to photoshop-like bindings and layout, but it hasn't been too hard to adjust. (I also only ever use it for small simple edits, drawing, cropping, color gradients, background removal, etc.)
I tried it out, and it's a pretty good clone. It serves my muscle memory well, with a few hiccups like drag-and-drop image onto the window doesn't work exactly the same, the save menu is a bit different. But there are also some pretty good improvements, like the addition of text layers (text got rasterized immediately after loosing focus in paint.NET which made it HORRIBLE to work with. This change is super welcome). There are also a bunch of new "plus" menu items that it seemingly gets from PhotoCraft? Not sure if that's because I have PhotoCraft installed, or if it comes with those features bundled. I'm not sure how good this is, because Paint.NET menus were already quite bloated and confusing to navigate, so this makes them even more cluttered with for example "Hue / Saturation" next to "Hue / Saturation +". The UI windows can't be dragged out of the paint.rs window like they could in Paint.NET. I think this is also faster than Paint.NET was.
I like this, it's quite good. I would like a more modern UI though, ideally using native GTK elements. Though that raises and interesting question of how much should a re-implementation veer away from the original. I think I'll keep this as a backup if I get fed up with PhotoCraft.
Knowledge was already extremely accessible in the age of the Internet, but it required time to read and learn. AI is making that same knowledge applicable without the prerequisite of learning.
Most people are not interesting in learning for learning's sake, they are motivated to learn something they can apply (see: kids complaining about how learning Math in school doesn't transfer to useful skills). Why learn about architecture and technical debt, if you can tell the machine to make it faster?
I don't think you're wrong, but fundamentally we're in an age of information overload, and this may be the new normal. In other words, the environment where you had to know things deeply and become an expert, like in the past, may be different from an environment where you know many things broadly and combine them.
I basically don't think you're wrong. However, books and reading have their information fixed at the point of publication, whereas depending on where you live or your community, the information you get buried in differs. Because I was in an Asian country, it was actually very hard for me to synchronize my programming skills with the latest technology in the US. The frameworks commonly used in my country are stuck at roughly 15 years ago. The time lag between US technology and my mainstream technology is about 5 years. In my area, it's about 15 years.
Anyway, thinking about your opinion, I think there are both valid points and dark points. I think learning for its own sake is important.
I agree that people should have an attitude of learning for its own sake, but I don't think it's mandatory. I think it should vary depending on the environment. Still... I do think having that attitude is a good thing. I like your idealism, senior.
However, that holds for people who are middle class or above and can survive. Because rich kids can picture the future and are given the right to love learning. But if you're poor, that's very hard. And when you grow up in such a poor family, there's an emphasis on it having to lead to practical skills.
And even if someone loves learning for its own sake, I think quite a lot of people love the honor that comes from learning. But that too can be called loving learning itself. It's purely a matter of individual values.
I don't think you're wrong, senior, but I also wonder whether the old ways are right in today's era of excessive information. It's just that because our lives and the environments surrounding us are different, we came to different conclusions. I upvoted because I agree with your thoughts. But there are points where I differ. Have a good day.
Watch what they do, not what they say. My right-click menu still takes 2000ms to open, the Weather app idles at over 700MB ram, teams randomly heats up my CPU and causes my laptop to sound like a jet engine until i close it, opening start menu still causes the PC to lag. Do not give them any credit for posting Copilot-generated press posts, before you see the changes yourself.
Try an OS that's designed for people rather than for tracking, and you'll be amazed at how fast an OS can be.
Oh I'm not taking this for granted, but they are just getting started of course. And don't forget, the business market is a huge influence on Microsoft. They are also looking to save significantly on hardware costs and are pushing for real improvements here too. They are spending more on AI services that MS is also trying to sell, and hardware costs are going up a lot.
And yes I use FOSS privately. I just have one big gaming PC with windows because I love VR and VR on Linux is not ready for primetime.
It's not really, and I don't really run SteamVR that much anyway. I mainly use meta's Airlink or virtual desktop for streaming. The Steam Frame itself is also still really hit and miss, many accepted features on Windows are considered "not important" by Valve and they may get to it at some point. There's a lot of catching up going on.
For me it's not worth the trouble. My game PC is for 2 things only: Gaming and 3D design and for those I am ok with just using windows.
I'm not a big fan of Steam anyway, I'm more of a GOG user. I will just buy on Steam if I can't find something on GOG.
It's 85% gross margin only if you divide by "Compute and infrastructure", which does not include personnel, legal settlements, sales, marketing and partnerships, or other operating and administrative. Their total operating expenses (34Bn as of 30th September this year) is slightly higher than their revenue (33Bn as of 30th September this year) which means they have an operating loss of ~0.6Bn so far this year. The majority of the negative numbers lie in debt to Amazon and Google (72Bn + 8Bn other), which is actually mostly convertible notes that to my understanding are never going to be paid back as cash, but as stocks in the company. A valuation of 1-2 trillion would give them plenty of room to pay back that debt with stocks, so imo. that part is a little overblown. IPOing would directly help reduce these numbers.
The spending commitments the article mentions are a little more concerning, but I haven't looked into that so I'm currently taking that with a huge grain of salt (especially since the article felt very biased and politically motivated).
Does it make sense that you should use a keyboard you need to study and practice for? Because I'm willing to bet you didn't instantly learn touch typing the first time you laid eyes on a keyboard. I would also bet that you didn't know about ctrl c, ctrl v, ctrl x, multiple cursors, tab, and so many other things that you now take for granted in code editors and elsewhere. Vim is just like that - a little foreign in the start, but after using it for a while it becomes second nature. You can learn just the basics and be productive in a couple of days, then you have the rest of your life to learn the more advanced features like terminal mode or macros and so on. Though it seems most people (me included) get really excited about learning vim once we get into it, and start sprinting through a bunch of features. That can make it seem a bit overwhelming.
I've been experimenting with vibe-coding some games. One of the games started performing very badly in a particular menu, and I began suspecting it's because of a shader that may be redundantly computed way too much on too many large surfaces. I began writing my suspicion to Claude, then stopped and decided to just describe the problem and see if it would come to the same conclusion as me. A few minutes later, it had profiled the game, found what contributed most to the poor performance, fixed the rendering and made the game performant again. I didn't have to inspect the code to do any of that. The current models (paired with coding harnesses) love to test and validate their results by running the games, taking screenshots, singling out individual parts, including logs to reproduce errors or other issues, and they love to create tests. The code is good enough in the same sense that the code I used to write was good enough if it did what we wanted. My non-technical bosses didn't have to inspect my code for that.
In my experience, it's usually good for finding out the bugs, but I take the fixes it provides with a grain of salt. There were numerous times where the fix could have lead to some more serious bugs because of some business logic special case scenarios that it didn't understand.
Yeah, I'm seeing this at work too. It's capable of adjusting behavior to meet some narrow success criteria, but it can easily introduce bigger problems in the process in large coupled code bases, which it doesn't catch on its own. Automated tests help a bit, but I find I still have to steer it very strongly at work.
It sounds like you understood what was happening here and used a tool effectively, nice.
That's very different from what a lot of vibe-coders do, like wholesale cloning and relicensing projects. This is happening in the GIS space, and these vibe-clone projects slightly hilariously inherit all the technical debt they didn't understand.
Nothing wrong with using LLMs, but I agree with Andrej Karpathy: "you can outsource your thinking, but not your understanding".
I think that quote is very apt. I experimented with making realistic video game grass (inspired by Ghost of Tsushima), and let it take the reins. But its solution became very contrived and hard to steer in the direction I wanted without me learning the specifics of how grass rendering should work. Still, it seems that I can limit my understanding to the higher levels of abstractions in most cases now. For example, I made it clump grass together according to a voronoi diagram, yet I have no idea how the voronoi algorithm works.
It turns out that most people are fine with most of their things being mass-produced cheap stuff from IKEA, Amazon, Temu, etc. - if that means that the cost is significantly lower. The functional benefit of having a door is huge, while the aesthetic benefit of having a beautiful, hand-crafted door is much less important to most people.
Code is not like craftsmanship anyway. The end-user doesn't see any difference whether I use microservices or hexagonal design or react or htmx or how I structure the code or which language I use. All they see is the design and the stability. Since the average developer was terrible at design anyway, the average design output from LLMs is actually an improvement in most cases, even if it always has that generic AI generated look. Stability... was not good before AI either. We have become accustomed to SAP taking 10 seconds to load each component on the page, or Atlassian spewing error messages in your face when you click submit on a ticket. It seems that the aspects that are most valued by our end users are QA and UX, not the craftsmanship of our code.
>current deep learning is much less malleable and neuroplastic as it should be
Why do you think this is? I am very new to this research, but to me, the biggest challenge appears to be how information seems to be stored in superposition (making it difficult to change one thing without affecting others). But biological brains appear to exhibit the same kind of superposition storage as well, and yet they are very plastic.
It argues that our understanding of neurons storing information in superposition was just a limitation of our tools and us thinking about it wrong. Instead most neurons initially specialize, and then branch out into related concepts. In addition, understanding one part of the network helps better understand other parts. And with agents we can scale this to understand non-trivial networks.
While, if true, this would more directly help with interpretability, a better understanding could also help with improving NNs in general.
Not who you asked, but biological brains come about very differently than LLM neural networks. Most notably, they come about largely through plasticity, and with evolutionary pressure towards exhibiting plasticity.
My guess would be that the structure to allow for plasticity to work is a strong limit to how well you can tune weights to fit training data. My (doubtless massively oversimplified and way behind the state-of-the art) understanding of neuro-plasticity is something like "often used pathways get stronger connections". Which, even with significant tweaks, is quite different from the gradient descent used to tune neural networks.
If strengthening of frequently used neural pathways is all our brains do, I don't see how it's seemingly capable of reordering knowledge physically from one part of the brain to another (which I believe is the case). Infrequently used pathways would simply rot away and die away over time, rather than being moved somewhere else for long-term storage to keep the high-frequency pathways more available.
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