> 1. Existing guidelines already handle low-value content. If an AI reply is shallow or off-topic, it gets downvoted or flagged.
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> 2. Transparency is good. Explicitly citing an AI is better than users passing off its output as their own, which a ban might encourage.
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> 3. The community can self-regulate. We don't need a new rule for every type of low-effort content.
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> The issue is low effort, not the tool used. Let downvotes handle it.
My best guess would be that plugins are limited into what they can do within VSCode, and rewriting a whole IDE/Text editor just for AI Agent seems a lot of work.
For example a while back vscode-pets[1] plugin became popular and tried it and noticed that the pet can only live within a window, whether its the explorer section or in its own panel, I thought it'd be more of a desktop pet that could be anywhere within VSCode but apparently there are limitations (https://github.com/tonybaloney/vscode-pets/issues/4).
So my guess is that forking VSCode and customizing it that way is much easier to do things that you can't with a plugin while also not having to maintain an IDE/Text editor.
From my understanding this is the "bottleneck" in quality for older systems (at least in gaming consoles), converting Analogue to Digital. Which is why "RetroTink" sells different converters from ~$100 up to $750 (RetroTINK-4K Pro). I've seen a few videos comparing cheap generic USB converters with more expensive upscalers and there is a noticeable difference in image quality
I played it, and it was fun. Some feedback (I'm on mobile) is that it needs UX improvement.
At first I wasn't sure what I had to do, it simply throws you into the game, and it's easy to get confused with the numbers around it, because usually if there are numbers, there is going to be somewhere (usually at the bottom) the numbers and the clue for each of them.
It took me a bit to find how to get the clues from the vertical words (double tapping).
The onscreen keyboard isn't very responsive in the sense that I had to tap several times the backspace to delete a letter.
I gave it a try and look for the locations, specially the 3rd one that does indeed look like it could be in Chile.
For the 2nd picture I found an island in the French Polynesia that has very similar colors and characteristics, might be its around that area, 8°56'10.8"S 139°34'41.2"W (-8.936304553977038, -139.57811272908305)
For the 3rd picture I found many locations that look like your picture but really couldn't find one. The first one is around Mexico, though it probably isn't 27°32'39.0"N 114°45'00.5"W (27.544166, -114.750130). And the second one are islands close to Morocco 28°01'54.4"N 17°16'22.9"W (28.03198233652239, -17.27306308433365) though the angle is not the same... As a bonus for the 3rd picture, I did find in the Andes mountain something that looks like your picture: 33°38'11.7"S 70°07'01.4"W (-33.636446, -70.116968). So maybe you should also look around mountains.
At least from what I've seen in Chile the coast is usually very rocky and the water is usually lot of waves, and in the picture it looks really smooth. (Though I don't know how zoomed out the picture is)
Wow, that looks fun and probably get to learn a lot about algorithms.
I don't have any feedback, but rather a question, as I've seen many repositories with people sharing their algorithms, at least on GitHub for many different languages (e.g. https://github.com/TheAlgorithms), what did you find that was missing from those repositories that you wanted to write a book and implement hundreds of algorithms, what did you find that was lacking?
Those algorithms implement so random to me, with lack of explanation, no test cases, no formal proof, and often inconsistent naming or structure across languages. Many repositories like TheAlgorithms are great collections, but they feel more like code dumps than true learning resources. You can find an implementation of Dijkstra or QuickSort, but often there is no context: why it works, how to prove it correct, what the complexity is, or how to test it against edge cases. For someone who wants to learn algorithms deeply, that missing layer of reasoning and validation is critical.
No organization for learners either. It jumps straight into implementations without a logical flow from fundamentals. I want to build something more structured: start from the very foundation (like data structures, recursion, and complexity analysis), then move to classical algorithms (search, sort, graph, dynamic programming), and eventually extend to database internals, optimization, and even machine learning or AI algorithms. Basically, a single consistent roadmap from beginner to researcher level, where every algorithm connects to the next and builds intuition step by step.
Another very good resource for beginners is https://www.hello-algo.com. At first, i actually wanted to contribute there, since it explains algorithms visually and in simple language. But it mostly covers the basics and stops before more advanced or applied topics. I want to go deeper and treat algorithms as both code and theory, with mathematical rigor and formal proofs where possible. That is something I really liked about Introduction to Algorithms (CLRS) and of course The Art of Computer Programming (TAOCP) by Knuth. They combine reasoning, math, and practice. My goal is to make something in that spirit, but more practical and modern, bridging the gap between academic books and messy open source repos.
Another reason for writing the book is that many developers see "algorithms" as something only needed for FAANG interviews, not for real work. For beginners and even seniors, learning algorithms often just means doing LeetCode problems, which most people dislike but feel forced to do.
I want to change that view and show that algorithms are beautiful and useful beyond interviews. They appear everywhere, from compilers to databases to the Linux kernel, where I found many interesting data structures worth exploring. (i will share more about this topic later)
I hope to share more of these insights and connect with others who enjoy discussing real world algorithm design, which is what I love most about the Hacker News community (except for the occasional trolls that show up from time to time).
For more context, I actually used The Algorithms as a reference when working on my own programming language, Mochi, which includes around 150–300 algorithms (I don't remember exactly) implemented directly in Mochi. These are then transpiled to over 25 programming languages such as C, Haskell, Java, Go, Scala, and more:
https://github.com/mochilang/mochi/tree/main/tests/algorithm...
The VM and transpiler were originally implemented by hand, and later I used Codex to help polish the code. The generated output works, though it is a bit messy in places. Hopefully, after finishing a few books, I can return to the project with more experience and add better use cases for it.
* Nvidia invests 5 billion in Intel
* Nvidia and OpenAI announce partnership to deploy 10 gigawatts of NVIDIA systems (Investment of upto 100 billion)
* This indirectly benefits TSMC (which implies they'll be investing more in the US)
YouTube has been pushing for longer videos for a while now. I believe it has to do with getting more money for ads. I remember YouTube updated their guidelines suggesting creators to create longer videos (10+ minutes for better monetization)
More or less there was a shift in how Youtube allocated ad revenue, where instead of paying per view on any video over 10 minutes, they pay based on watch time alone (which makes sense with how they added mid-roll ads instead of just pre-roll ads). This incentivized longer videos, or at least didn't penalize them, but then more recently there was another shift in their recommendation algorithm to push these videos harder (I suspect because they found it increased watch time and thus ad impressions overall), and that really pushed the long-form videos into the mainstream.
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