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I started writing this early last week but Real Life Stuff happened and now you're getting the first-draft late this week. Warning, unedited thoughts ahead! New Logic for Programmers release! v0.9 is out! This is a big release, with a new cover design, several rewritten chapters, online code samples and much more. See the full release notes at the changelog page, and get the book here! Write the cleverest code you possibly can There are millions of articles online about how programmers should not write "clever" code, and instead write simple, maintainable code that everybody understands. Sometimes the example of "clever" code looks like this (src): # Python p=n=1 exec("p*=n*n;n+=1;"*~-int(input())) print(p%n) This is code-golfing, the sport of writing the most concise code possible. Obviously you shouldn't run this in production for the same reason you shouldn't eat dinner off a Rembrandt. Other times the example looks like this: def is_prime(x): if x == 1: return...
20 hours ago

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More from Computer Things

Requirements change until they don't

Recently I got a question on formal methods1: how does it help to mathematically model systems when the system requirements are constantly changing? It doesn't make sense to spend a lot of time proving a design works, and then deliver the product and find out it's not at all what the client needs. As the saying goes, the hard part is "building the right thing", not "building the thing right". One possible response: "why write tests"? You shouldn't write tests, especially lots of unit tests ahead of time, if you might just throw them all away when the requirements change. This is a bad response because we all know the difference between writing tests and formal methods: testing is easy and FM is hard. Testing requires low cost for moderate correctness, FM requires high(ish) cost for high correctness. And when requirements are constantly changing, "high(ish) cost" isn't affordable and "high correctness" isn't worthwhile, because a kinda-okay solution that solves a customer's problem is infinitely better than a solid solution that doesn't. But eventually you get something that solves the problem, and what then? Most of us don't work for Google, we can't axe features and products on a whim. If the client is happy with your solution, you are expected to support it. It should work when your customers run into new edge cases, or migrate all their computers to the next OS version, or expand into a market with shoddy internet. It should work when 10x as many customers are using 10x as many features. It should work when you add new features that come into conflict. And just as importantly, it should never stop solving their problem. Canonical example: your feature involves processing requested tasks synchronously. At scale, this doesn't work, so to improve latency you make it asynchronous. Now it's eventually consistent, but your customers were depending on it being always consistent. Now it no longer does what they need, and has stopped solving their problems. Every successful requirement met spawns a new requirement: "keep this working". That requirement is permanent, or close enough to decide our long-term strategy. It takes active investment to keep a feature behaving the same as the world around it changes. (Is this all a pretentious of way of saying "software maintenance is hard?" Maybe!) Phase changes In physics there's a concept of a phase transition. To raise the temperature of a gram of liquid water by 1° C, you have to add 4.184 joules of energy.2 This continues until you raise it to 100°C, then it stops. After you've added two thousand joules to that gram, it suddenly turns into steam. The energy of the system changes continuously but the form, or phase, changes discretely. Software isn't physics but the idea works as a metaphor. A certain architecture handles a certain level of load, and past that you need a new architecture. Or a bunch of similar features are independently hardcoded until the system becomes too messy to understand, you remodel the internals into something unified and extendable. etc etc etc. It's doesn't have to be totally discrete phase transition, but there's definitely a "before" and "after" in the system form. Phase changes tend to lead to more intricacy/complexity in the system, meaning it's likely that a phase change will introduce new bugs into existing behaviors. Take the synchronous vs asynchronous case. A very simple toy model of synchronous updates would be Set(key, val), which updates data[key] to val.3 A model of asynchronous updates would be AsyncSet(key, val, priority) adds a (key, val, priority, server_time()) tuple to a tasks set, and then another process asynchronously pulls a tuple (ordered by highest priority, then earliest time) and calls Set(key, val). Here are some properties the client may need preserved as a requirement: If AsyncSet(key, val, _, _) is called, then eventually db[key] = val (possibly violated if higher-priority tasks keep coming in) If someone calls AsyncSet(key1, val1, low) and then AsyncSet(key2, val2, low), they should see the first update and then the second (linearizability, possibly violated if the requests go to different servers with different clock times) If someone calls AsyncSet(key, val, _) and immediately reads db[key] they should get val (obviously violated, though the client may accept a slightly weaker property) If the new system doesn't satisfy an existing customer requirement, it's prudent to fix the bug before releasing the new system. The customer doesn't notice or care that your system underwent a phase change. They'll just see that one day your product solves their problems, and the next day it suddenly doesn't. This is one of the most common applications of formal methods. Both of those systems, and every one of those properties, is formally specifiable in a specification language. We can then automatically check that the new system satisfies the existing properties, and from there do things like automatically generate test suites. This does take a lot of work, so if your requirements are constantly changing, FM may not be worth the investment. But eventually requirements stop changing, and then you're stuck with them forever. That's where models shine. As always, I'm using formal methods to mean the subdiscipline of formal specification of designs, leaving out the formal verification of code. Mostly because "formal specification" is really awkward to say. ↩ Also called a "calorie". The US "dietary Calorie" is actually a kilocalorie. ↩ This is all directly translatable to a TLA+ specification, I'm just describing it in English to avoid paying the syntax tax ↩

2 weeks ago 20 votes
The Halting Problem is a terrible example of NP-Harder

Short one this time because I have a lot going on this week. In computation complexity, NP is the class of all decision problems (yes/no) where a potential proof (or "witness") for "yes" can be verified in polynomial time. For example, "does this set of numbers have a subset that sums to zero" is in NP. If the answer is "yes", you can prove it by presenting a set of numbers. We would then verify the witness by 1) checking that all the numbers are present in the set (~linear time) and 2) adding up all the numbers (also linear). NP-complete is the class of "hardest possible" NP problems. Subset sum is NP-complete. NP-hard is the set all problems at least as hard as NP-complete. Notably, NP-hard is not a subset of NP, as it contains problems that are harder than NP-complete. A natural question to ask is "like what?" And the canonical example of "NP-harder" is the halting problem (HALT): does program P halt on input C? As the argument goes, it's undecidable, so obviously not in NP. I think this is a bad example for two reasons: All NP requires is that witnesses for "yes" can be verified in polynomial time. It does not require anything for the "no" case! And even though HP is undecidable, there is a decidable way to verify a "yes": let the witness be "it halts in N steps", then run the program for that many steps and see if it halted by then. To prove HALT is not in NP, you have to show that this verification process grows faster than polynomially. It does (as busy beaver is uncomputable), but this all makes the example needlessly confusing.1 "What's bigger than a dog? THE MOON" Really (2) bothers me a lot more than (1) because it's just so inelegant. It suggests that NP-complete is the upper bound of "solvable" problems, and after that you're in full-on undecidability. I'd rather show intuitive problems that are harder than NP but not that much harder. But in looking for a "slightly harder" problem, I ran into an, ah, problem. It seems like the next-hardest class would be EXPTIME, except we don't know for sure that NP != EXPTIME. We know for sure that NP != NEXPTIME, but NEXPTIME doesn't have any intuitive, easily explainable problems. Most "definitely harder than NP" problems require a nontrivial background in theoretical computer science or mathematics to understand. There is one problem, though, that I find easily explainable. Place a token at the bottom left corner of a grid that extends infinitely up and right, call that point (0, 0). You're given list of valid displacement moves for the token, like (+1, +0), (-20, +13), (-5, -6), etc, and a target point like (700, 1). You may make any sequence of moves in any order, as long as no move ever puts the token off the grid. Does any sequence of moves bring you to the target? This is PSPACE-complete, I think, which still isn't proven to be harder than NP-complete (though it's widely believed). But what if you increase the number of dimensions of the grid? Past a certain number of dimensions the problem jumps to being EXPSPACE-complete, and then TOWER-complete (grows tetrationally), and then it keeps going. Some point might recognize this as looking a lot like the Ackermann function, and in fact this problem is ACKERMANN-complete on the number of available dimensions. A friend wrote a Quanta article about the whole mess, you should read it. This problem is ludicrously bigger than NP ("Chicago" instead of "The Moon"), but at least it's clearly decidable, easily explainable, and definitely not in NP. It's less confusing if you're taught the alternate (and original!) definition of NP, "the class of problems solvable in polynomial time by a nondeterministic Turing machine". Then HALT can't be in NP because otherwise runtime would be bounded by an exponential function. ↩

3 weeks ago 15 votes
Solving a "Layton Puzzle" with Prolog

I have a lot in the works for the this month's Logic for Programmers release. Among other things, I'm completely rewriting the chapter on Logic Programming Languages. I originally showcased the paradigm with puzzle solvers, like eight queens or four-coloring. Lots of other demos do this too! It takes creativity and insight for humans to solve them, so a program doing it feels magical. But I'm trying to write a book about practical techniques and I want everything I talk about to be useful. So in v0.9 I'll be replacing these examples with a couple of new programs that might get people thinking that Prolog could help them in their day-to-day work. On the other hand, for a newsletter, showcasing a puzzle solver is pretty cool. And recently I stumbled into this post by my friend Pablo Meier, where he solves a videogame puzzle with Prolog:1 Summary for the text-only readers: We have a test with 10 true/false questions (denoted a/b) and four student attempts. Given the scores of the first three students, we have to figure out the fourth student's score. bbababbabb = 7 baaababaaa = 5 baaabbbaba = 3 bbaaabbaaa = ??? You can see Pablo's solution here, and try it in SWI-prolog here. Pretty cool! But after way too long studying Prolog just to write this dang book chapter, I wanted to see if I could do it more elegantly than him. Code and puzzle spoilers to follow. (Normally here's where I'd link to a gentler introduction I wrote but I think this is my first time writing about Prolog online? Uh here's a Picat intro instead) The Program You can try this all online at SWISH or just jump to my final version here. :- use_module(library(dif)). % Sound inequality :- use_module(library(clpfd)). % Finite domain constraints First some imports. dif lets us write dif(A, B), which is true if A and B are not equal. clpfd lets us write A #= B + 1 to say "A is 1 more than B".2 We'll say both the student submission and the key will be lists, where each value is a or b. In Prolog, lowercase identifiers are atoms (like symbols in other languages) and identifiers that start with a capital are variables. Prolog finds values for variables that match equations (unification). The pattern matching is real real good. % ?- means query ?- L = [a,B,c], [Y|X] = [1,2|L], B + 1 #= 7. B = 6, L = [a, 6, c], X = [2, a, 6, c], Y = 1 Next, we define score/33 recursively. % The student's test score % score(student answers, answer key, score) score([], [], 0). score([A|As], [A|Ks], N) :- N #= M + 1, score(As, Ks, M). score([A|As], [K|Ks], N) :- dif(A, K), score(As, Ks, N). First key is the student's answers, second is the answer key, third is the final score. The base case is the empty test, which has score 0. Otherwise, we take the head values of each list and compare them. If they're the same, we add one to the score, otherwise we keep the same score. Notice we couldn't write if x then y else z, we instead used pattern matching to effectively express (x && y) || (!x && z). Prolog does have a conditional operator, but it prevents backtracking so what's the point??? A quick break about bidirectionality One of the coolest things about Prolog: all purely logical predicates are bidirectional. We can use score to check if our expected score is correct: ?- score([a, b, b], [b, b, b], 2). true But we can also give it answers and a key and ask it for the score: ?- score([a, b, b], [b, b, b], X). X = 2 Or we could give it a key and a score and ask "what test answers would have this score?" ?- score(X, [b, b, b], 2). X = [b, b, _A], dif(_A,b) X = [b, _A, b], dif(_A,b) X = [_A, b, b], dif(_A,b) The different value is written _A because we never told Prolog that the array can only contain a and b. We'll fix this later. Okay back to the program Now that we have a way of computing scores, we want to find a possible answer key that matches all of our observations, ie gives everybody the correct scores. key(Key) :- % Figure it out score([b, b, a, b, a, b, b, a, b, b], Key, 7), score([b, a, a, a, b, a, b, a, a, a], Key, 5), score([b, a, a, a, b, b, b, a, b, a], Key, 3). So far we haven't explicitly said that the Key length matches the student answer lengths. This is implicitly verified by score (both lists need to be empty at the same time) but it's a good idea to explicitly add length(Key, 10) as a clause of key/1. We should also explicitly say that every element of Key is either a or b.4 Now we could write a second predicate saying Key had the right 'type': keytype([]). keytype([K|Ks]) :- member(K, [a, b]), keytype(Ks). But "generating lists that match a constraint" is a thing that comes up often enough that we don't want to write a separate predicate for each constraint! So after some digging, I found a more elegant solution: maplist. Let L=[l1, l2]. Then maplist(p, L) is equivalent to the clause p(l1), p(l2). It also accepts partial predicates: maplist(p(x), L) is equivalent to p(x, l1), p(x, l2). So we could write5 contains(L, X) :- member(X, L). key(Key) :- length(Key, 10), maplist(contains([a,b]), L), % the score stuff Now, let's query for the Key: ?- key(Key) Key = [a, b, a, b, a, a, b, a, a, b] Key = [b, b, a, b, a, a, a, a, a, b] Key = [b, b, a, b, a, a, b, b, a, b] Key = [b, b, b, b, a, a, b, a, a, b] So there are actually four different keys that all explain our data. Does this mean the puzzle is broken and has multiple different answers? Nope The puzzle wasn't to find out what the answer key was, the point was to find the fourth student's score. And if we query for it, we see all four solutions give him the same score: ?- key(Key), score([b, b, a, a, a, b, b, a, a, a], Key, X). X = 6 X = 6 X = 6 X = 6 Huh! I really like it when puzzles look like they're broken, but every "alternate" solution still gives the same puzzle answer. Total program length: 15 lines of code, compared to the original's 80 lines. Suck it, Pablo. (Incidentally, you can get all of the answer at once by writing findall(X, (key(Key), score($answer-array, Key, X)), L).) I still don't like puzzles for teaching The actual examples I'm using in the book are "analyzing a version control commit graph" and "planning a sequence of infrastructure changes", which are somewhat more likely to occur at work than needing to solve a puzzle. You'll see them in the next release! I found it because he wrote Gamer Games for Lite Gamers as a response to my Gamer Games for Non-Gamers. ↩ These are better versions of the core Prolog expressions \+ (A = B) and A is B + 1, because they can defer unification. ↩ Prolog-descendants have a convention of writing the arity of the function after its name, so score/3 means "score has three parameters". I think they do this because you can overload predicates with multiple different arities. Also Joe Armstrong used Prolog for prototyping, so Erlang and Elixir follow the same convention. ↩ It still gets the right answers without this type restriction, but I had no idea it did until I checked for myself. Probably better not to rely on this! ↩ We could make this even more compact by using a lambda function. First import module yall, then write maplist([X]>>member(X, [a,b]), Key). But (1) it's not a shorter program because you replace the extra definition with an extra module import, and (2) yall is SWI-Prolog specific and not an ISO-standard prolog module. Using contains is more portable. ↩

a month ago 72 votes
[April Cools] Gaming Games for Non-Gamers

My April Cools is out! Gaming Games for Non-Gamers is a 3,000 word essay on video games worth playing if you've never enjoyed a video game before. Patreon notes here. (April Cools is a project where we write genuine content on non-normal topics. You can see all the other April Cools posted so far here. There's still time to submit your own!) April Cools' Club

a month ago 29 votes

More in programming

I switched from GMail and nobody died

Whether we like it or not, email is widely used to identify a person. Code sent to email is used as authentication and sometimes as authorisation for certain actions. I’m not comfortable with Google having such power over me, especially given the fact that they practically don’t have any support you can appeal to. If your Google account is blocked, that’s it. Maybe you know someone from Google and they can help you, but for most of us mortals that’s not an option.

yesterday 2 votes
A Little Bit Now, A Lotta Bit Later

In mid-March we released a big bug fix update—elementary OS 8.0.1—and since then we’ve been hard at work on even more bug fixes and some new exciting features that I’m excited to share with you today! Read ahead to find out what we’ve released recently and what you can help us test in Early Access. Quick Settings Quick Settings has a new “Prevent Sleep” toggle Leo added a new “Prevent Sleep” toggle. This is useful when you’re giving a presentation or have a long-running background task where you want to temporarily avoid letting the computer go to sleep on its normal schedule. We also fixed a bug where the “Dark Mode” toggle would cancel the dark mode schedule when used. We now have proper schedule snoozing, so when you manually toggle Dark Mode on or off while using a timed or sunset-to-sunrise schedule, your schedule will resume on the next schedule change instead of being canceled completely. Vishal also fixed an issue that caused some apps to report being improperly closed on system shutdown or restart and on the lock screen we now show the “Suspend” button rather than the “Lock” button. System Settings Locale settings has a fresh layout thanks to Alain with its options aligned more cleanly and improved links to additional settings. Locale Settings has a more responsive design We’ve also added the phrase “about this device” as a search term for the System page and improved interface copy when a restart is required to finish installing updates based on your feedback. Plus, Stanisław improved stylus detection in Wacom settings preventing a crash when no stylus is found. AppCenter We now show a small label next to the download button for apps which contain in-app purchases. This is especially useful for easily identifying free-to-play games or alt stores like Steam or Heroic Games Launcher. AppCenter now shows when apps have in-app purchases Plus, we now reload app icons on-the-fly as their data is processed, thanks to Italo. That means you’ll no longer get occasionally stuck with an AppCenter which shows missing images for app’s who have taken a bit longer than usual to load. Get These Updates As always, pop open System Settings → System on elementary OS 8 and hit “Update All” to get these updates plus your regular security, bug fix, and translation updates. Or set up automatic updates and get a notification when updates are ready to install! Early Access Our development focus recently has been on some of the bigger features that will likely land for either elementary OS 8.1 or 9. We’ve got a new app, big changes to the design of our desktop itself, a whole lot of under-the-hood cleanup, and the return of some key system services thanks to a new open source project. Monitor We’re now shipping a System Monitor app by default By popular demand—and thanks to the hard work of Stanisław—we have a new system monitor app called “Monitor” shipping in Early Access. Monitor provides usage information for your processor, GPU, memory, storage, network, and currently running processes. You can optionally see system information in the panel with Monitor You can also optionally get a ton of glanceable information shown in the panel. There’s currently a lot of work happening to port Monitor to GTK4 and improve its functionality under the Secure Session, so make sure to report any issues you find! Multitasking The Dock is getting a workspace switcher Probably the biggest change to the Pantheon shell since its early inception, the Dock is getting a new workspace switcher! The workspace switcher works in a familiar way to the one you may have seen in the Multitasking View: Your currently open workspaces are represented as tiles with the icons of apps running on them; You can select a workspace to switch to it; You can drag-and-drop workspaces to rearrange them; And you can use the “+” button to create a new blank workspace. One new trick however is that selecting the workspace you’re already on will launch Multitasking View. The new workspace switcher makes it so much more accessible to multitask with just the mouse and get an overview of your workflows without having to first enter the Multitasking View. We’re really excited to hear what people think about it! You can close apps from Multitasking View by swiping up Another very satisfying feature for folks using touch input, you can now swipe up windows in the Multitasking View to close them. This is a really familiar gesture for those of us with Android and iOS devices and feels really natural for managing a big stack of windows without having to aim for a small “x” button. GTK4 Porting We’ve recently landed the port of Tasks to GTK4. So far that comes with a few fixes to tighten up its design, with much more possible in the future. Please make sure to help us test it thoroughly for any regressions! Tasks has a slightly tightened up design We’re also making great progress on porting the panel to GTK4. So far we have branches in review for Nightlight, Bluetooth, Datetime, and Network indicators. Power, Keyboard, and Quick Settings indicators all have in-progress branches. That leaves just Applications, Sound, and Notifications. So far these ports don’t come with major feature changes, but they do involve lots of cleaning up and modernizing of these code bases and in some cases fixing bugs! When the port is finished, we should see immediate performance gains and we’ll have a much better foundation for future releases. You can follow along with our progress porting everything to GTK4 in this GitHub Project. And More When you take a screenshot using keyboard shortcuts or by secondary-clicking an app’s window handle, we now send a notification letting you know that it was succesful and where to find the resulting image. Plus there’s a handy button that opens Files with your screenshot pre-selected. We’re also testing beaconDB as a replacement for Mozilla Location Services (MLS). If you’re not aware, we relied on MLS in previous versions of elementary OS to provide location information for devices that don’t have a GPS radio. Unfortunately Mozilla discontinued the service last June and we’ve been left without a replacement until now. Without these services, not only did maps and weather apps cease to function, but system features like automatic timezone detection and features that rely on sunset and sunrise times no longer work properly. beaconDB offers a drop-in replacement for MLS that uses Wireless networks, bluetooth devices, and cell towers to provide location data when requested. All of its data is crowd-sourced and opt-in and several distributions are now defaulting to using it as their location services data provider. I’ve set up a small sponsorship from elementary on Liberapay to support the project. If you can help support beaconDB either by sponsoring or providing stumbler data, I’d highly encourage you to do so! Sponsors At the moment we’re at 23% of our monthly funding goal and 336 Sponsors on GitHub! Shoutouts to everyone helping us reach our goals here. Your monthly sponsorship funds development and makes sure we have the resources we need to give you the best version of elementary OS we can! Monthly release candidate builds and daily Early Access builds are available to GitHub Sponsors from any tier! Beware that Early Access builds are not considered stable and you will encounter fresh issues when you run them. We’d really appreciate reporting any problems you encounter with the Feedback app or directly on GitHub.

3 days ago 1 votes
The System-Level Foundation of Assembly

Tracing how the CPU, OS, and ELF format shape the structure of your assembly code

4 days ago 1 votes
Taking a break

I've been publishing at least one blog post every week on this blog for about 2.5 years. I kept it up even when I was very sick last year with Lyme disease. It's time for me to take a break and reset. This is the right time, because the world is very difficult for me to move through right now and I'm just burnt out. I need to focus my energy on things that give me energy and right now, that's not writing and that's not tech. I'll come back to this, and it might look a little different. This is my last post for at least a month. It might be longer, if I still need more time, but I won't return before the end of May. I know I need at least that long to heal, and I also need that time to focus on music. I plan to play a set at West Philly Porchfest, so this whole month I'll be prepping that set. If you want to follow along with my music, you can find it on my bandcamp (only one track, but I'll post demos of the others that I prepare for Porchfest as they come together). And if you want to reach out, my inbox is open. Be kind to yourself. Stay well, drink some water. See you in a while.

4 days ago 1 votes