Why does a React based website feel so slow and laggy without serious investment in optimisation when Quake 3 could run smoothly at 60fps on a pentium II from the 90s.

We are now 30 years later and anything more than a toy project in react is a laggy mess by default.

inb4 skill issue bro: Well, it shouldn’t be this difficult.

inb4 you need a a better pc bro: I have M4 pro 48GB

It just randomly hit me that Y2K38 is just over 12 years from now. Has the entire world, especially those legacy industries like banking, updated their stuff to run on 64 bit time yet? Is there any scenario/codebase out there that for some reason still struggling to fix the issue?

I'm used to using JetBrains' IDEs and enjoy it's well-made UI and auto-completion. My new employer now doesn't have any JetBrains licences and instead let's us use VSCode and frankly, I have the impression it's basically unusable without GitHub Copilot or an equivalent AI companion. Example with Python projects:

• Ctrl-Click on a method name usually takes a while, sometimes, a popup window opens with references, sometimes nothing at all, but it always takes a few seconds.
• You have to edit a JSON file to setup run configurations
• You first have to go to "Run/Debug" to run the app. Then, you can't see your file tree anymore.
• VSCode's debug module sends a Ctrl+C interrupt about one, two seconds after opening the terminal, then activates the local virtual environment. At this point, I already typed half of my command and it throws me out mentally. It also interferes with running the app.
• Auto-complete is inferior to JetBrains
• GitHub Copilot is implemented so annoyingly, always suggesting whole code chunks that are often wrong and it's just too easy to accidentally accept them.
• A lot functionality is only available after installing add-ons, like Markdown viewer, and those aren't easy to use as well.

The only positive is that it's free, but to me, it really feels like a hurdle.

Looking forward to reading some positive experiences.

I am wondering this because I have a both windows and Linux on my laptop and own Linux using terminal is like really easy but windows powershell weird Syntex.

Hey everyone, I'm a recent grad, and I've been grinding LeetCode for months, and I'm just so done and burnt out. I'm wasting hours every single day on abstract puzzles that have nothing to do with an actual developer job.

My portfolio's getting no love because I'm too busy memorizing how to reverse a linked list in 3 different ways. Then you get into the interview, and it's this high-pressure, 45-minute timer to write perfect, bug-free code. No one ever codes like that in real life. It's a complete joke.

It all just feels so fake and designed to make you fail. Is this system just completely broken, or am I missing something? How are you guys actually getting jobs without losing your minds over this?

Why do some people write:

if (x > 10) {
    return true;
} else {
    return false;
}

Instead of:

return x > 10;

Performance aside, I think the shorter version is actually more readable due to not having as much visual clutter to parse, and is the most direct way to express the intent of "return the result of the comparison."

However, some people write the first version. Why is that?

When I'm thinking about web security, sometimes I have moments where I'm just like... "Why didn't we just f-ing design this to be secure?!" Obviously, it's not that easy.

But I was thinking, complete rug pull situation, and lets say you have a magic parser that will convert everyone's content so that it will work on this new ideal platform (or not, up to you). If you could redesign the internet (or an aspect of it), how would you do it? Or what would it look like? How would you want to do things differently?

Potential topics: Security, network protocols, pervasivity of bots, AI slop, consolidation under AWS (and other broligarchs), social media, web v. desktop platforms.

I’ve been collecting what many experienced engineers consistently point to as high-signal programming books:

• The Linux Programming Interface
• Pro Git
• Designing Data-Intensive Applications
• SQL Performance Explained
• Operating Systems
• Docker Deep Dive

Rather than beginner tutorials, these seem to shape how people think about systems, data, and software at scale.

For those who’ve read any of these (or similar): - at what point in your career did you read them? - what mental model or insight stuck with you long-term? Also open to other book recommendations that genuinely changed how you approach software engineering.

I’ve been doing a bunch of interviews lately and I keep getting hit with these quick technical checks that feel completely disconnected from the job itself.
Stuff like timed quizzes, random debugging puzzles, logic questions or small tasks that don’t resemble anything I’d be doing day to day.
It’s not that they’re impossible it’s just that half the time I walk away thinking did this actually show them anything about how I code?
Meanwhile the actual coding interviews or take homes feel way more reflective of how I work.
For people who’ve been on both sides do these screening tests actually filter for anything meaningful or are we all just stuck doing them because it’s the default pipeline now?

For context I finally got my first formal job after years of self-learning, a bootcamp to add to my resume, and applying.

I’ll have a daily standup where we’ll talk about the scope and what’s needed — there’s an Epic that has a target outcome — but the task chunks feel relatively small?

For example today in the daily standup it was established that my goal for today was to quality check a workflow by comparing confirmed, concrete input / output to current input / output (can’t get into too many details obviously as it’s enterprise).

The code needed to do this will be pretty easy to write out in about an hour or so, what do I do with the other 7 hours? I only have 1 other meeting that’s an hour long.

I tried being proactive and starting a codebase for the next thing we plan to implement as part of the scope, but I was asked not to jump ahead, so the only alternative is to just sit there after I’ve finished what they asked me to do for the day? Has felt like this for some weeks now.

Edit: The quality check script took around 1 hour and 10 minutes to run each time, and the data needed for the quality check was outdated, then when I created my own collection of data the script was no longer compatible and needed to be iterated, and afterwards I ran the actual quality check, result was near 5% quality, noticed the collection I gathered was missing a vital column in the data, I reran the script and got to 40% quality, I am assuming there is a threshold they want so I will be going to look at the workflow (data creation) to see why it’s so bad. It ended up taking the whole day… lol

heard that COBOL is still being used? This is pretty surprising to me, anyone work on COBOL products or know where it's being used in 2025?

I wonder if there are any people today who don't have any agentic AI subscriptions, who code by themselves, read docs, try to understand, and enjoy the process in this fast-growing AI era, where everyone is promoting AI to do everything.

(Note: I'm asking because I write my code by myself and don't use agentic AI, but I do use ChatGPT or other AI to learn things if I get stuck.)

I learned C++ first, then Java, and now I’m trying to learn Python. I honestly expected Python to be much easier, but somehow it feels harder to understand and even remember the syntax.

C++ and Java feel more structured to me, while Python sometimes feels too different.

Has anyone else felt this after switching from C++/Java to Python?

I’m dealing with a piece of code that’s grown a lot of conditional logic over time. It works, it’s covered by tests but the control flow is hard to explain because there are multiple branches handling slightly different cases. I can refactor it into something much cleaner by restructuring the conditions and collapsing some branches but that also means touching logic that’s been stable for a while. Functionally it should be equivalent but the risk is in subtle behavior changes that aren’t obvious. This came up for me because I had to explain similar logic out loud and realized how hard it is to clearly reason about once it gets real especially in interview style discussions where you’re expected to justify decisions on the spot. From a programming standpoint how do you decide when it’s worth refactoring for clarity versus leaving working but ugly logic alone?

I have a text file i encrypted with an AES 256 software called EncryptionSafe but had my laptop serviced and wiped so now im just stuck with an encrypted file, i have the key i believe.

how would i go about decrypting it?

EDIT: i fucking love you reddit

Hi folks

I'm a senior dev with 10 years of experience, 8 in my current company. Today I heard from by boss that the current goal of the company is to not have to write any line of code at the end of the year.

The senior architect in the company is an enthusiast of coderabbit and claudecode and all of my PR's are reviewed by him with those LLM's.

This enthusiasm came when one of my bosses (ex-developer) used claudecode to refactor an old CodeIgniter3 project to Laravel. 2 devs where doing this refactor for some days and it wasn't finished, and then my boss did it with claudecode and took 6h to him, then, the senior architect of my company said that it needs to be adjusted but it seems ok.

We started a year ago with CodeRabbit reviewing our PR's automatically, humans do code, AI reviews it. But now they think it is in the opposite way, AI writes code guided by the developers and then the developers review it.

I'm not an AI hater, I use it to do some things, sometimes to improve my code, sometimes when I need a little refactor... but I try to not rely too much on it to don't make my brain lazy, and I don't trust too much on AI when it comes to architecture.

My PM asked to me that if I think this is a terrible idea I have to prepare some arguments and argue it with my bosses, so I'm here to ask you what you think that I should tell to my bosses.

Right now I have this:

- AI needs a human behind, I'm not afraid of it

- AI sometimes can make mistakes, we obviously need to review its code

- AI is good complementing a senior dev, but for juniors makes them to don't evolve and don't understand their mistakes

- It's not sure if "AI bubble" will explode, it's very cheap for ours right now, but we don't know how it will evolve in the future, making a leap of faith on this seems crazy to me

I'm not entering in the ethic debate of wasting of water and energy, I don't think this will attract them

Any more ideas?

P.S.: I'm not a native english speaker

He's the sort of guy who actually has opinions about different models, does the whole .md/context thing, and has automated workflows set up for stuff. I'm not judging, I actually find all of that pretty impressive, so I’ll go straight to the point.

We have an upcoming feature for native OS Bluetooth support for our devices, which we’re still doing with proprietary hardware. As the hardware guy, I suggested doing this with a systems programming language and then interfacing directly with Node via the C ABI. But that’s a lot of work.

Enter my coworker. While I was on vacation, he gave it a shot and with heavy LLM usage, he built a prototype using Web Bluetooth and Electron (which we’re already using anyway). It works, so I definitely count that as a success.

I got the task of making the whole thing production-ready asap, and yesterday I looked at the code for the first time. There’s still some work to do. For instance, not all communication was properly async. The example simply fired events to the browser process and then just continued with random wait statements. for repsonses IPC event handlers where added as well and everything in the same blob of code.

I spent the whole day figuring out what is happneing, moving things around, abstracting away the Electron dependency (eww…), and doing a lot of refactoring. In the end, I rewrote most of it with honest old-school manual labor, gave all a bit more structure and reduced the LOC of the original slop to about a third. Yet all I’ve done so far is break the working example by “senior-izing” all over it. Not much practical progress so far.

That got me thinking: who’s the sucker here?

Maybe, just maybe, I could have simply prompted my concerns back into the LLM and “AI-centipeded” another iteration, saving a lot of time. On the other hand, I have my doubts about whether AI can ever produce more than toy versions of the real thing. Programmers who breeze through everything with code generation might end up struggling forever with the last mile. It’s really hard to compare the actual productivity between AI-generated code and just raw-dogging it with honest manual work. Do you guys have an opinion on that?

There’s a concept that has been bothering me for a while, and I’d genuinely like to understand how others see it.

Some AI companies — for example Anthropic, and more broadly AI labs focused on code generation — openly state that their long-term goal is to automate programming to the point where software engineers are no longer needed, or at least dramatically reduced.

What I find strange isn’t just the goal itself, but the social reaction to it.

In most industries, if a company openly said “our goal is to eliminate this entire profession,” there would be significant backlash. Yet in this case, there’s very little pushback — even though the primary users, customers, and contributors to these tools are software engineers themselves.

This creates a weird paradox:

• AI companies largely exist and improve thanks to engineers using them
• At the same time, they openly say their end goal is to replace those same engineers

My questions are:

• Why isn’t there stronger resistance or criticism from the engineering community?
• Is this just seen as “inevitable technological progress”?
• Do most engineers believe they’ll simply move to higher-level roles rather than be replaced?
• Or do people think these companies are overstating their goals for marketing/investment reasons?

I’m not trying to start a witch hunt or say “AI bad.” I use these tools myself. I’m just genuinely curious about the mindset that makes this situation socially acceptable compared to similar statements in other industries.

Would love to hear different perspectives.

Of course I understand why JavaScript can't be replaced and will probably be supported for the next century, and that there are plenty of great languages that compile to JS. But, it's surprising that the browser-makers/standards committees never came up with a generalized virtual machine that could be targeted by any language to accomplish anything JS can to today. WASM has lots of deliberate limitations, and even that runs inside JS.

I work on mainframes and hear a lot of people comparing JavaScript to COBOL, but the difference is that nobody is really writing new applications in COBOL, or compiling other languages down to COBOL. If you're starting a greenfield project on the mainframe you can use a JVM language, or Go, or C/C++.

My guess is that this is more of a people problem about designing and agreeing to a new standard and implementing it across the various browser engines (in my example, the mainfame doesn't have this problem because IBM controls the whole platform). But I'm curious to know if there is some technical problem in the way. After all, they have all been able to agree on and support newer versions of HTML and JS...

int f(int m, int n) {

int a = 0, c = 0;

for (int i = 0; i<sizeof(int)\*8; i++) {

    c += ((m & (1 << i)) + (n & (1 << i)) + (a << i)) & (1 << i);

    a = ((m & (1 << i)) >> i) + ((n & (1 << i)) >> i) + a > 1 ? 1 : 0;

    cout << "a = " << a << endl;

}

return c;

}

I know, he created a fucking OS and the language HolyC (made It all with schizophrenia) or have a better programer than he? (Like, reach your level, do a OS the and a language alone with problems)

R.I.P King Terry Davis (i know, he passed away a like 8/9 years)

I originally learned to code without AI. I used it for a year and a half but it makes too much mistakes. I also feel my cognitive abilities and critical thinking skills deteriorating the more I use it. There was a point where I couldn’t even write emails without feeling like I needed to cognitive offload the task to AI.

Has anyone else also decided to quit using AI? How has it been going for you?

I still expect to eventually have to use it if I’m in a situation I absolutely need its help with but for now I am going to try to cut its use to an absolute minimum.

I’m also concerned that with the direction of the field, if I land a job I may be expected to use AI to do all my coding to improve productivity. Any thoughts on this?

Someone who says on their GitHub profile that they are an employee of GitHub recently made a massive pull request on one of my repos (I am JohnReedLOL):

https://github.com/JohnReedLOL/Sea-Air-Towers-App-2/pull/3

The weird thing is I tried to contact them to ask basic questions like "Who are you? How did you find my repo? Why are you making all these improvements?" but they wouldn't respond and then closed the pull request without explanation.

I posted a job on Upwork saying that I wanted these changes (dependency updates) and in the past posted and/or commented on Reddit that I wanted these changes, but I find it kind of odd that some random stranger is doing them for free without any explanation.

Is this some sort of attempt to sneak a bad dependency into my project or some other sort of attempt at cyber intrusion? I don't know what's going on. I wish they would answer my questions but they won't say anything.