• 15 Posts
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Joined 2 years ago
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Cake day: June 5th, 2023

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  • dfyxAto3DPrinting@lemmy.worldUpdates and Third-Party Integration with Bambu Connect
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    19 days ago

    Actually, isn’t this the optimal outcome? The new “security” features are now optional for those who want them. Everyone else can choose developer mode, has all the old features and is responsible for securing their network. We could argue if opt-in or opt-out is better but I see the argument for having “security” features enabled by default.






  • Here in Germany, we celebrate mostly on Christmas Eve. I have a couple of hours left to get ready before my stepdad picks me up. We’ll visit my grandma who sadly has to spend the holidays in hospital because she fell and hurt her knee. After that, Christmas dinner at my mom’s and stepdad’s house with most of the family, gifts and a relaxed evening. I’ll stay over night, have breakfast and then go home to finish packing everything I need for my winter vacation which starts on the 26th.

    It’s been an exhausting year and I can really use the downtime.


  • dfyxAtoich_iel@feddit.orgich🎁iel
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    2 months ago

    Ich persönlich lasse einfach für jeden (bzw. jedes Paar) einen Kalender mit Fotos, die ich im Lauf des Jahres geschossen hab, drucken. Manche kriegen noch was oben drauf, aber so hat jeder zumindest eine Kleinigkeit.



  • I have a wishlist that I share with my family and close friends. People follow that list unless they have an idea that they’re 100% sure about. I think the only times I got an unwanted gift was things I already had. Either because something went wrong coordinating between people (rare, everyone knows they can contact my partner to ask what’s still available) or because they accidentally bought the wrong thing (like the first book of a series instead the second one).

    The only exception ever was during a single https://givin.gifts/ secret santa exchange where someone at the same time a) completely ignored my profile, b) gifted something below the stated minimum value, c) didn’t wrap my gift, d) didn’t include a card and e) didn’t include any packing material. They just threw a random 5€ item from the supermarket into an unpadded box and called it a day.


  • German here, we have the same thing (du vs. sie). Our rules may be slightly different than dutch but probably similar enough.

    Police: definitely formal unless the officer is someone you know privately.

    Shop: usually formal though some hobby-related shops (think GameStop or board games) might prefer informal.

    Campsite: probably informal

    As a general rule of thumb: informal is used with first names, formal is used with last names. Think about which name you would use in English and go with that. If in doubt, use the formal version or ask.



  • The diagram is pretty good but your interpretation is not quite right, especially for NP-complete and NP-hard.

    NP-hard means “at least as hard as all problems in NP”, proven by the fact that any single NP-hard problem can be used to solve the entire class of all NP problems.

    NP-complete means “at least as hard as all problems in NP and itself also in NP”, so the intersection between NP and NP-hard.

    The thing about P = NP or P != NP is something different. We don’t know if P and NP are the same thing or not, we don’t have a proof in either direction. We only know that P is at least a subset of NP. If we could find a P solution for any NP-hard problem, we would know that P = NP. That would have massive consequences for cryptography and cyber-security because modern encryption relies on the assumption that encrypting something with a key (P) is easier than guessing the key (NP).

    On the other hand, at some point we might find a mathematical proof that we can never find a P solution to an NP-hard problem which would make P != NP. Proving that something doesn’t exist is usually extremely hard and there is the option that even though P != NP we will never be able to prove it and are left to wonder for all eternity.



  • Alright, part 2, let’s get to NP.

    Knowing that P means “in polynomial time”, you might be tempted to think that NP means “in non-polynomial time” and while that kind of goes in the right direction, it means “in non-deterministic polynomial time”. Explaining what non-deterministic calculations are would be a bit too complicated for an ELI5, so let’s simplify a bit. A regular computer must make all decisions (for example which way to turn when calculating a shortest route between two points) based on the problem input alone. A non-deterministic computer can randomly guess. For judging complexity, we look at the case where it just happens to always guess right. Even when guessing right, such a computer doesn’t solve a problem immediately because it needs to make a number of guesses that depends on the input (for example the number of road junctions between our points). NP is the class of problems that a non-deterministic computer can solve in polynomial Time (O(n^a) for any a).

    Obviously, we don’t really have computers that always guess right, though quantum computers can get us a bit closer. But there are three important properties that let us understand NP problems in terms of regular computers:

    1. a non-deterministic computer can do everything a regular computer can do (and more), so every problem that’s part of P is also part of NP.
    2. every problem that takes n guesses with x options for each guess can be simulated on a regular computer in O(x^n) steps by just trying all combinations of options and picking the best one. With some math, we can show that this is also true if we don’t have n but O(n^a) guesses. Our base x might be different, but we can always find something with n in the exponent.
    3. While finding a solution on a regular computer may need exponential time, we can always check if a solution is correct in polynomial time.

    One important example for a problem in NP is finding the prime factors of a number which is why that is an important basic operation in cryptography. It’s also an intuitive example for checking the result being easy. To check the result, we just need to multiply the factors together and see if we get our original number. Okay, technically we also need to check if each of the factors we get is really prime but as mentioned above, that’s also doable in polynomial time.

    Now for the important thing: we don’t know if there is some shortcut that lets us simulate NP problems on a regular computer in polynomial time (even with a very high exponent) which would make NP equal to P.

    What we do know is that there are some special problems (either from NP or even more complex) where every single problem from NP can be rephrased as a combination of that special problem (let’s call it L) plus some extra work that’s in P (for example converting our inputs and outputs to/from a format that fits L). Doing this rephrasing is absolutely mind-bending but there are clever computer scientists who have found a whole group of such problems. We call them NP-hard.

    Why does this help us? Because finding a polynomial-time solution for just a single NP-hard problem would mean that by definition we can solve every single problem from NP by solving this polynomial-time NP-hard problem plus some polynomial-time extra work, so polynomial-time work overall. This would instantly make NP equal to P.

    This leaves us with the definition of NP-complete. This is simply the class of problems that are both NP-hard and themselves in NP. This definition is useful for finding out if a problem is NP-hard but I think I’ve done enough damage to your 5-year-old brain.
















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