A couple of days ago (give or take) my social media feed showed a post
that goes like this: Here is a real task from my job. I have a 100GB binary
file. Produced daily. I can't grep it. But I can decode it.
However, I can't store the decoded version either. It's too big.
How do I efficiently query it?
Decoding piped to grep takes 2 minutes. I want 2 seconds.
When I saw this post, my initial reaction was to question the approach. Unless the binary file is highly encoded, for example, encrypted, it seems inefficient to decode it to plain text and then search with grep; especially within a company that reported a revenue in the billions last year. While this method may be understandable for those with limited resources, one would expect a much more optimized solution in a large enterprise setting.
The engagement generated by the post led the author to disclose more details, for example, that this file was generated by a microservice. And that it had a record-type structure with various record types. (1) We all know what microservices use to talk to each other, right? (2) 100GB files seem to defeat the micro purpose, but that is relative. Also, someone suggested to grep the binary file, but the author seemed to not be aware of such a tool, and the reply was something along the lines of wanting to avoid translating the grep queries into binary grep ones. This seems reasonable if the queries are complex, which is kind of expected.
My suspicions were confirmed when the author disclosed that the decoded file is a JSON file worth at least 1 TB. Then I thought "Who greps such a big file when there are a lot of efficient tools that parse JSON?, again, you must be kidding, right? I mean, you are aware that there are a bunch of smart folks who create software specialized in storing and querying data, right?" But again, probably you don't want to add an extra layer of complexity, so you prefer to stick to big file transactions, or perhaps (and more likely) a high number of small transactions.
The cherry of the cake was when the author posted a follow-up post: You just gotta love how many people jump with solutions without clarifying requirement. This is a common mistake I see while interviewing people at *****. People propose solutions without trying to clarify constraints, or input data patterns, or edge cases.
So, this might as well have been an interview question and an interview behavior. I think it is troublesome. If you are given a problem to solve as part of an interview for a job about solving problems, the first natural reaction is to attempt to solve the problem with the information you are given. Moreover, if the person doing the interview is your prospective immediate supervisor, then it is reasonable to assume you are given all the information necessary or at least available at the time to solve the task. I think most people try to be polite and won't tell the interviewer, "Who the f..k does that? Are you incompetent or just plain lazy?" even if they think about it.
If you are given a half-baked problem by the person who is going to supervise your work, then you, as a prospective employee, can easily make the conclusion "this guy is plain incompetent, he wants me to boost his production by more than 10x without having a complete idea of the problem he wants to solve." If you want to assess the candidate's ability to correctly frame a problem, there are many more meaningful ways to do so.
Interviews go both ways, just as the interviewer concludes the candidate makes a mistake by not asking more questions about the problem, the candidate (even if they ask questions) can easily conclude that the interviewer is lazy, incompetent, or withholds information on purpose to other colleagues.
When I saw this post, my initial reaction was to question the approach. Unless the binary file is highly encoded, for example, encrypted, it seems inefficient to decode it to plain text and then search with grep; especially within a company that reported a revenue in the billions last year. While this method may be understandable for those with limited resources, one would expect a much more optimized solution in a large enterprise setting.
The engagement generated by the post led the author to disclose more details, for example, that this file was generated by a microservice. And that it had a record-type structure with various record types. (1) We all know what microservices use to talk to each other, right? (2) 100GB files seem to defeat the micro purpose, but that is relative. Also, someone suggested to grep the binary file, but the author seemed to not be aware of such a tool, and the reply was something along the lines of wanting to avoid translating the grep queries into binary grep ones. This seems reasonable if the queries are complex, which is kind of expected.
My suspicions were confirmed when the author disclosed that the decoded file is a JSON file worth at least 1 TB. Then I thought "Who greps such a big file when there are a lot of efficient tools that parse JSON?, again, you must be kidding, right? I mean, you are aware that there are a bunch of smart folks who create software specialized in storing and querying data, right?" But again, probably you don't want to add an extra layer of complexity, so you prefer to stick to big file transactions, or perhaps (and more likely) a high number of small transactions.
The cherry of the cake was when the author posted a follow-up post: You just gotta love how many people jump with solutions without clarifying requirement. This is a common mistake I see while interviewing people at *****. People propose solutions without trying to clarify constraints, or input data patterns, or edge cases.
So, this might as well have been an interview question and an interview behavior. I think it is troublesome. If you are given a problem to solve as part of an interview for a job about solving problems, the first natural reaction is to attempt to solve the problem with the information you are given. Moreover, if the person doing the interview is your prospective immediate supervisor, then it is reasonable to assume you are given all the information necessary or at least available at the time to solve the task. I think most people try to be polite and won't tell the interviewer, "Who the f..k does that? Are you incompetent or just plain lazy?" even if they think about it.
If you are given a half-baked problem by the person who is going to supervise your work, then you, as a prospective employee, can easily make the conclusion "this guy is plain incompetent, he wants me to boost his production by more than 10x without having a complete idea of the problem he wants to solve." If you want to assess the candidate's ability to correctly frame a problem, there are many more meaningful ways to do so.
Interviews go both ways, just as the interviewer concludes the candidate makes a mistake by not asking more questions about the problem, the candidate (even if they ask questions) can easily conclude that the interviewer is lazy, incompetent, or withholds information on purpose to other colleagues.
UPDATE:
After writing this I reached out to Marcus Pilgaard to thank him for posting the scores at musescore and showed him the result. He didn't just spot the mistakes and pointed them out, he was so kind enough to offer the solution! That's super cool.
Marcus handed me a revised score for the melody adjusted to the kind of sounds I was generating and he also provided me with a couple of MIDI files for a keyboard. I was able to revise the whole melody. It was nice to hear what I generated along with a trusted baseline.
So here are the results:
Søren Banjomus Revised
I mixed Marcus MIDI melody and what I generated and here is the result
I also tweaked the sound shaper to produce quieter sounds. The revised version is here
Marcus: Tusind tak, du er den bedste!
Original:
A while ago I wrote a white noise generator to avoid going to an app. I wanted to be able to hear it and tune some elements on the fly. Since the generator is written in Python and use Simpleaudio to construct the wav file, I consulted the documentation to figure out how to play raw data.
There I found an example using numpy. The numpy example had some appealing syntax so I decided to create a small program to generate some notes. My "wife" had the idea to play the Imperial March from Star Wars on it and from there I decided to make it play one of my favorites Christmas songs: Søren Banjomus.
Luckily I found a score written by Marcus Pilgaard HERE which was wonderful for my attempts :)
These are the .wav results: Imperial March, Søren Banjomus
And finally this is the code: Player the configuration settings (i.e. filename and playback) are the last two lines
I think it is not bad for a couple of days work considering I had to recall how to read scores, interpret them, and so on :D
After writing this I reached out to Marcus Pilgaard to thank him for posting the scores at musescore and showed him the result. He didn't just spot the mistakes and pointed them out, he was so kind enough to offer the solution! That's super cool.
Marcus handed me a revised score for the melody adjusted to the kind of sounds I was generating and he also provided me with a couple of MIDI files for a keyboard. I was able to revise the whole melody. It was nice to hear what I generated along with a trusted baseline.
So here are the results:
Søren Banjomus Revised
I mixed Marcus MIDI melody and what I generated and here is the result
I also tweaked the sound shaper to produce quieter sounds. The revised version is here
Marcus: Tusind tak, du er den bedste!
Original:
A while ago I wrote a white noise generator to avoid going to an app. I wanted to be able to hear it and tune some elements on the fly. Since the generator is written in Python and use Simpleaudio to construct the wav file, I consulted the documentation to figure out how to play raw data.
There I found an example using numpy. The numpy example had some appealing syntax so I decided to create a small program to generate some notes. My "wife" had the idea to play the Imperial March from Star Wars on it and from there I decided to make it play one of my favorites Christmas songs: Søren Banjomus.
Luckily I found a score written by Marcus Pilgaard HERE which was wonderful for my attempts :)
These are the .wav results: Imperial March, Søren Banjomus
And finally this is the code: Player the configuration settings (i.e. filename and playback) are the last two lines
I think it is not bad for a couple of days work considering I had to recall how to read scores, interpret them, and so on :D
In her CACM 2006 landmark paper "Computational Thinking" Jeannette
M. Wing coined the term Computational Thinking.
Wing concludes: Professors of computer science should
teach a course called "Ways to Think Like a Computer Scientist" to
college freshmen, making it available to non-majors, not just to
computer science majors.
If we recognize that all professions are equally important, then the
first years (not semester nor semesters) of college would be a
plethora of courses "Ways to Think Like a ...".
Which, actually, seem to be more suitable to be taught at
High-School level and should give a more informed landscape of
professional development possibilities.
But why is necessary to have the concept Computational Thinking? According to Wing, "Computational thinking is a grand vision to guide computer science educators, researchers and practitioners as we act to change society's image of the field". As this paper resonated within the Computing Education community, research projects and papers found a conductive thread to spread the "Computational Thinking" concept and how to promote it in pre-college learners, although it remains challenging to define exactly what you should observe to conclude that students have reached "Computational Thinking".
Long before Wing's paper, Leone Minna Gold Burton in 1984 published "Mathematical Thinking: The Struggle for Meaning" in JRME (Journal for Research in Mathematics Education) in which she argues that "mathematical thinking is not thinking about the subject matter of mathematics but a style of thinking that is a function of particular operations, processes, and dynamics recognizably mathematical". In essence this is similar to Wing's definition: "Computational thinking involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science". While Wing acknowledges Computational Thinking is related to Mathematical Thinking, she states this relation is due to "like all sciences, its formal foundations rest on mathematics". Indeed Computer Science is a branch of Mathematics, we study problems solvable by algorithms, that is, well-defined (hopefully unambiguous) processes. There should be little doubt that computational problems are more restricted than Mathematical problems, and hence, Computational Thinking should be a restricted, specialized version of Mathematical Thinking.
Wing claims Computational Thinking is a "Fundamental skill", "something every human being must know to function in modern society". As far as I know, Wing does not justify this requirement, other authors state this is because computers are "everywhere". Wing does mention: "Ubiquitous computing was yesterday's dream that became today's reality" but she doesn't relate this reality with the "Fundamental skill" characteristic of Computational Thinking.
Arguably most people own a watch: wristwatch, wall-clock, alarm clock, etc. It is almost impossible to imagine going on your daily modern routine without a tool that marks the time down to the second. Clocks are a marvel of human ingenuity and nowadays 'smart-watches' even check your oxygen saturation. And yet, how to think like a clock designer/builder/tinker is not a course nor a "Fundamental skill". Shall we teach "Car engineering Thinking" just because in your country cars are everywhere? Shall we teach "Railroad design" to European students because you can reach many small towns by train? Or shall we teach "How to think like a librarian" just because a vast majority of persons in the "modern society" own a book? The difference is clear: a computer does much more than mark the time, it doesn't take you from A to B but it can show you how to get from A to B the quickest, it is not a book, but it can contain millions of books in just a tiny fraction of the space.
Is this enough to justify Computational Thinking?
It is futile to cement Computational Thinking on the "Computer" object because problems solvable by well-defined processes transcend computers and had been, in fact, a constant presence throughout the history of mankind: you can't cook an animal you haven't hunted yet. Computers, at the present moment, are instrumental just because they are our current predictable model in which we can test our theories.
But why is necessary to have the concept Computational Thinking? According to Wing, "Computational thinking is a grand vision to guide computer science educators, researchers and practitioners as we act to change society's image of the field". As this paper resonated within the Computing Education community, research projects and papers found a conductive thread to spread the "Computational Thinking" concept and how to promote it in pre-college learners, although it remains challenging to define exactly what you should observe to conclude that students have reached "Computational Thinking".
Long before Wing's paper, Leone Minna Gold Burton in 1984 published "Mathematical Thinking: The Struggle for Meaning" in JRME (Journal for Research in Mathematics Education) in which she argues that "mathematical thinking is not thinking about the subject matter of mathematics but a style of thinking that is a function of particular operations, processes, and dynamics recognizably mathematical". In essence this is similar to Wing's definition: "Computational thinking involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science". While Wing acknowledges Computational Thinking is related to Mathematical Thinking, she states this relation is due to "like all sciences, its formal foundations rest on mathematics". Indeed Computer Science is a branch of Mathematics, we study problems solvable by algorithms, that is, well-defined (hopefully unambiguous) processes. There should be little doubt that computational problems are more restricted than Mathematical problems, and hence, Computational Thinking should be a restricted, specialized version of Mathematical Thinking.
Wing claims Computational Thinking is a "Fundamental skill", "something every human being must know to function in modern society". As far as I know, Wing does not justify this requirement, other authors state this is because computers are "everywhere". Wing does mention: "Ubiquitous computing was yesterday's dream that became today's reality" but she doesn't relate this reality with the "Fundamental skill" characteristic of Computational Thinking.
Arguably most people own a watch: wristwatch, wall-clock, alarm clock, etc. It is almost impossible to imagine going on your daily modern routine without a tool that marks the time down to the second. Clocks are a marvel of human ingenuity and nowadays 'smart-watches' even check your oxygen saturation. And yet, how to think like a clock designer/builder/tinker is not a course nor a "Fundamental skill". Shall we teach "Car engineering Thinking" just because in your country cars are everywhere? Shall we teach "Railroad design" to European students because you can reach many small towns by train? Or shall we teach "How to think like a librarian" just because a vast majority of persons in the "modern society" own a book? The difference is clear: a computer does much more than mark the time, it doesn't take you from A to B but it can show you how to get from A to B the quickest, it is not a book, but it can contain millions of books in just a tiny fraction of the space.
Is this enough to justify Computational Thinking?
It is futile to cement Computational Thinking on the "Computer" object because problems solvable by well-defined processes transcend computers and had been, in fact, a constant presence throughout the history of mankind: you can't cook an animal you haven't hunted yet. Computers, at the present moment, are instrumental just because they are our current predictable model in which we can test our theories.
I have heard and read the statement "Programming isn't Math" too
many times, but this one is new, it doesn't just tell you what
programming "isn't" but what "is".
Today a tweet with a video claiming this popped up and I
watched it.
The video dwells on different kinds of "ambiguities" that can happen on C++ programs with examples of "English counterparts". For some reason omitted the fact that Mathematics has ambiguities too, called paradoxes, one of the most famous is "Russell's paradox".
Like all the times I have seen "Programming isn't Math", the video failed to explain this point at any serious level, it follows the "conter-example fallacy"; showing counter examples that curiously fall in the claimed category and conclude the subject at hand must belong to the claimed category. I feel the expression "Programming isn't Math" is meant to point out that having a formal mathematical education is not a pre-requisite to write programs. It is obvious that you can create programs without any formal Mathematical background just as you can build a brick house without any civil engineering knowledge. Imagine you can get a building manual with recipes to build any possible two-story house that explains how you should arrange all the elements. Then, clearly you don't need any civil engineering knowledge because every possible case is solved in the manual, how that manual came to be is beyond this discussion, the point is the manual exists. The existence of the manual can be considered as the culmination of civil, structural and material engineering research into two-story houses. The interesting, open question is: if we accumulate all the programs ever written, can we write any possible program by taking only parts of the existent programs?
I usually do not check "the credentials" of authors because I feel those bias us, but since some of the people I follow, follow that account, then I was curious. I was "surprised" to find the profile indicates the author to be a researcher at the company that created the most successful search engine.
I can not help to be left with this taste of marketing paving: if to create programs you don't need "math" just to be able to have an idea of how to "write proper English" then you are all set now the last step is to use this super intuitive AI tool to help you...
The video dwells on different kinds of "ambiguities" that can happen on C++ programs with examples of "English counterparts". For some reason omitted the fact that Mathematics has ambiguities too, called paradoxes, one of the most famous is "Russell's paradox".
Like all the times I have seen "Programming isn't Math", the video failed to explain this point at any serious level, it follows the "conter-example fallacy"; showing counter examples that curiously fall in the claimed category and conclude the subject at hand must belong to the claimed category. I feel the expression "Programming isn't Math" is meant to point out that having a formal mathematical education is not a pre-requisite to write programs. It is obvious that you can create programs without any formal Mathematical background just as you can build a brick house without any civil engineering knowledge. Imagine you can get a building manual with recipes to build any possible two-story house that explains how you should arrange all the elements. Then, clearly you don't need any civil engineering knowledge because every possible case is solved in the manual, how that manual came to be is beyond this discussion, the point is the manual exists. The existence of the manual can be considered as the culmination of civil, structural and material engineering research into two-story houses. The interesting, open question is: if we accumulate all the programs ever written, can we write any possible program by taking only parts of the existent programs?
I usually do not check "the credentials" of authors because I feel those bias us, but since some of the people I follow, follow that account, then I was curious. I was "surprised" to find the profile indicates the author to be a researcher at the company that created the most successful search engine.
I can not help to be left with this taste of marketing paving: if to create programs you don't need "math" just to be able to have an idea of how to "write proper English" then you are all set now the last step is to use this super intuitive AI tool to help you...
The first time I heard about Roskilde University (Roskilde Universitet) was when I was a bacherlor student: the PhD advisor of one of my bachelor advisors worked there on program analysis of logic programs, the topic I was working on at that time. Little I knew what a special place Denmark and Roskilde would have in my wife's heart and mine. How our breath would be taken away standing in the shadow of Roskilde Domkirke, our weekly trips and special trips to the Vikingeskibsmuseet to see the ships pulled out of the water for the winter while discussing how nice it would be to live in Roskilde and be part of the maintenance crew. Little I knew how we would wait for summer to buy Carolina Reaper chilly peppers at the weekend market and how patient the sweet couple would be with my less-than-acceptable danish or how nice was the 'Honey man' from whom we would buy our Roskilde Ring honey, how much we would cherish our wood crafts we bought at the local market or seeing all the cherished belongings people would sell on weekends or our 'tours' to find and photograph all Kilder. I wouldn't even imagine telling to my office mate at CMU over lunch (during a research stay) about how much I would have loved to have an academic position at RUC while staying at one of the most prestigious computer science universities. I can not help, but feel disappointed and somewhat heartbroken about the proposal to close RUC.
What I write here is not based on exhaustive research, as I said, my Duolingo fueled danish is not what it should be, so there is a lot that I do not understand; it is based on perspective and feeling. It seems some doubtful decisions were made regarding the acceptance of 300 students from Bangladesh in 2024 into a masters program in business that had financial consequences. The consequence for RUC is that it seems it 'profited' millions of danish krone. Perhaps if it were just that, then it would be kind of ok. However, politicians seem to suspect the students and accompanying spouses true purpose is to seek jobs; a discourse often heard worldwide nowadays. Suspicion alone is not a fact; suspicion draws attention and requires investigation, research and debate. If wrong doing with intent is found, then corrective actions must be taken, if necessary fire the university's entire ruling body, but closing a regional university has no sense at all!; specially when a higher-education decentralization strategy is in the works. It seems to me the goal of RUC shutdown is to send a message not an actual step in the right direction to fix the root causes. A message that will have far more negative consequences than 'letting' other universities known whatever there is for them to know.
I feel for the RCU and Roskilde communities but I also feel for the foreigner students that may be prevented from experiencing the warmness and kindness my wife and I had the opportunity to be part of.
I got news regarding a position I applied to, it was rejected. What is particularly frustrating about this situation, is that you had to write a proposal and attach it to the application. It took me several days of work to write the application, reading books, papers, and so on to frame the proposal within the scope of the job advertisement. In the end, I got a template email basically saying "thank you for your interest". From my perspective, that kind of response does not help with your self-grow as a prospective employee. There is no feedback, nothing, just a boolean answer. Of course, I reached out in the hopes of getting a more elaborated feedback, after all we are humans and not AI models using backpropagation. I got an email wrote by a human but still template, however, what was interesting is that only short-listed applicants will get feedback on their application. This seems reasonable if you get a significant number of applicants. That being said, if complete feedback is only given to short-listed applicants, then why not require the proposal only to short-listed applicants? It seems to be a proportional reasonable request. It would be the equivalent of programming tests some companies perform as part of their hiring process.
I still have another issue tho. This is the 3rd position of this type that I have applied and the second that uses a hiring platform. The first position was sent by email, this was a long time ago. In that case I got an interview and then the employer reached out saying they picked the other candidate because he had more experience with Y which I totally get; there was a well-stated reason behind my rejection, of the n boxes we had to tick I checked only n-1. In the second case I got accepted. But in this case there was nothing. I find unsettling that in an era where there are platforms that help to bring talent in, it is still a black hole: information gets sucked in and nothing gets out. There is no theoretical/technical limitation that prevents employers from providing feedback even if you only send your CV; someone, somewhere had to say 'no because ...' and if you don't wish to type there are speech synthesizers or check boxes. I think even LinkedIn provides you with some metrics, if you pay them of course.
I feel there is a culture of opacity which obviously seem reasonable to some employers, perhaps feedback would give a glimpse into the thinking process behind selection which might be exploitable or legal-abiding? Bottom-line if we accept employers have the right to reject candidates for any reason, for example, if you have your head tilted to the right slightly on your profile picture, then nowadays, it is a piece of cake to write a program that desk-reject such applications and write a template email: "We thank you for your interest unfortunately your head was slightly tilted...". Then it is up to you as a job seeker to take it or leave it; unlike AI model training, we do have a choice to incorporate feedback or not.
I have many reusable paper sheets. Sometimes I print on one side and the other is clean, others I buy extra paper sheets that end up not being what I needed and sometimes friends gift me some extra clean or reusable paper sheets. I love handwriting, specially when I use fountain pens, there is something I like about them. My handwriting is terrible; I often tell my students that by the end of the semester they would master two skills, the academic goals of the assignment and decoding hieroglyphs :). I often craft my own notebooks and blocks. Don't get me wrong, I also have my fair share of Moleskine, Fabriano and 365 notebooks. Lately I'm using I block I made a long time ago, perhaps 7 or so years ago with some block sheets that were too thin for me at the time. The sheets are even older, perhaps 12 years? I don't even remember. But I bought a couple of new fountain pens, a Platinum Preppy Extra-Fine and a Pilot kaküno also Extra-Fine and both are great!. If the pen doesn't come with a converter, it is not a problem, I have a syringe, ink bottles and an affinity to mix inks :)
I have been spending a lot of time preparing Programming Without Problems there were many ups and downs and corrections and so on. At some point it was no fun to see so much HTML code together. So I wrote a small Python (go figure :) ) script to help me strip all HTML code and just focus on my writing. It seems to fit the bill so far so, I'm happy. It also helps me to manage my references automatically, so that is nice; if BibTeX is an interstellar ship, my code is the Wright's Brothers sketch :D.
When I was in high-school at UNAM's CCH-Sur I used to spend quite sometime at the library. It is a beautiful place, not only because the building is gorgeous but also because its mathematics collection is amazing. I spent hours there reading and browsing books and of course, napping.
I got the same feelings on the early days of Google. After clicking on the 3rd page or so :)
With AI (ChatGPT, etc.) it is like entering a library in which all books are blacked out and there is this annoying librarian asking what you want and giving you a paragraph of a black book.
When you know exactly what you are looking for, this annoying librarian could be useful. I have spent half a day looking for a reference of some quote I have buried deep in my brain. However, when you look for guidance, the annoying librarian is the least help you should seek.
I understand the need for empirical evidence on a claim. What I can't grasp is the reaction of surprise when the evidence supports common sense, like today on my social media feed regarding a paper showing accumulation of "Cognitive Debt" when using AI for creative writing.
AI will accelerate the success of those that 1) Know exactly what thing they are looking for, 2) it is not easy to find or tedious to achieve and 3) it is common enough in the training set.
AI will accelerate the creative process destruction of those that are novice to a subject and are seeking guidance.
Of course, I would not be surprise that a model happens to be advertised as fine-tuned to provide guidance.
Sometime ago I found that a "white noise" broadcast on YouTube helped me to concentrate while listing to it on my headphones. However, I feel it is too much of a waste to just listen to "random noise". So, I wrote a small Python program to generate some "white noise". We all know it is not trully random, but it is close enough :) And, since I had control over what I created, I decided to mix in some effects.
The first Passing Rain is white noise with the same sinusoidal volume control on both ears.
The second Passing Rain Adjusted is white noise with the same sinusoidal volume control on both ears, but before adjusting the volume, a linear interpolation with 35 points is performed between pairs of "sufficiently off" points. Effectively removing some randomness (actually a lot :D).
The third Alternating Rain is white noise with a sinusoidal volume control starting at for the left channel and another starting at for the right channel.
The fourth and fifth are more if you are into regular beats.
The fourth Slow Beat is a sine wave each second using only the upper part of the intensity spectrum. I'm using 16 bits for each number but only using the [0,32767] range.
The fifth Slow Motor are 15 sine waves per second, again using only the upper part of the intensity specturm.
Watch for your volume if you are using headphones!
I asked Gemini to generate a code to solve the 24 arithmetical puzzle in Ada. I haven't made progress checking the correctness of the generated code. I was planning to cross check it with implementations on other languages in RosettaCode, but it has been down for the best part of the week. What got my eye was the "tests" Gemini generated, here is an example:
It is difficult to believe this kind of code generation is taken seriously. Of course I chose this language and this problem because I don't expect to exist a lot of Ada implementations of 24. At RosettaCode there was none. And if you are using AI to generate code that has been done and redone for 1000 times, then, isn't really a cheaper way?
I watched a GitHub Copilot tutorial video about basics. At some point Copilot is asked what error handling functionality should be added to some code. The first one to be suggested was an ArgumentNullException and I immediately remembered back when I was an undergrad. The Java compiler used to refuse to compile your code if you failed to catch a NullPointerException that some method you called may throw. I had this professor that used to say: "Just compile the damn thing, I KNOW what I'm DOING".
I came across this post on the once
great, then not so great before they sold it and now it is just a
zombie. If I recall correctly it was one of the main aims of
C++: to not impose a certain mindset. But for some reason they lobby
a lot for modern C++. I think one of the reasons is that you need
certain standard guidelines in order to be able to work quickly
on different teams without the (re)learning curve. Some changes are
justified to help the careless developer. I think it is one of
the worst reasons to change a language, it is like saying: We are
going to introduce these changes into (insert your favorite natural
language here) because some people fail to speak it "properly".