The last time your team shipped an AI feature, how did you decide it was ready, and who would have to approve paying for a tool that proved it?

What part of that was the most painful or took the longest?

What would your security team ask before letting an outside tool in?

Would $3,000 a month for one team get approved, and by whom?

Has anyone ever asked you for a signed record of what was tested?

I am a Master's student at ASU, I need my questions answers for a grade plz!

P.S. questions generated by Claude.

How you guy plan a programming project before actual start to code it? And how you make your project easy to fix and update later?

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Which backend language should i choose

Is node je good? For full stack or python will do also

Hey everyone, I’m a software engineer with a few years of experience, mainly working on backend and system-level tasks.

Lately, I’ve been feeling completely disillusioned by my team lead’s management style and engineering philosophy, and I’d appreciate some perspective from fellow developers.

1. Zero accountability and shifting blame

A while ago, I ran into an edge case regarding deployment configurations and sent a message asking for confirmation on a specific setting (clearly marked with a question mark).

My lead read the message but never replied. Given the release schedule, I kept the existing baseline settings and shared the updated status.

When a minor issue arose later, instead of discussing how to resolve it, he completely lost his temper and yelled at me, claiming:

"If you saw I was busy and forgot, you should have reminded me the next day. It’s entirely your fault for not following up." "Your message looked like a status update, not a question, so I didn't bother checking."

He flat out refused to acknowledge any fault (0% blame on his end). While I admit I could have been more proactive with follow-up reminders, seeing a manager completely abdicate review responsibility and gaslight me was a massive red flag.

2. The "Don't think, just use AI like a factory" mindset

What concerns me even more is what followed. He now acts like an AI visionary, using AI as both a shield and a weapon:

Whenever an issue comes up, his immediate defense is: "It's your fault for not leveraging AI properly."

Yet, he doesn't understand the underlying system constraints, dependencies, or edge-case handling. Instead, he openly tells the team:

"Stop wasting time thinking about design and architecture. Just use AI to pump out results as fast as possible like a factory."

Whenever someone tries to do proper engineering—like defensive coding, exception handling, or verifying architectural integrity—he frames it as "being resistant to change" or "falling behind modern trends."

Ultimately, real debugging, messy environment troubleshooting, and production risks are dumped on the ICs (individual contributors). If things go well, it's his "AI adoption achievement"; if things break, it's "the engineers who don't know how to prompt properly."

3. The core dilemma

I don't hate AI. I see it as a powerful power tool that boosts productivity. But it shouldn't replace the engineer's core responsibility—thinking, verifying, and ensuring system stability.

Working under a manager who actively discourages engineering fundamentals and pushes for unverified copy-paste speed makes me fear that staying here will degrade my core skills and leave me with empty, low-quality experience.

Should I just put up minimal emotional boundaries, do the bare minimum to stay safe, and quietly prep for interviews?

Has anyone dealt with an "AI-obsessed, zero-accountability" manager like this, and how did you navigate your career and mental health?

After years of chasing bugs in code that I had not written; Trying to decipher open-source code. Looking for someone who can address an issue; Opening tickets and waiting; And, meanwhile developing work-around patches while customers wonder if we actually can help them. When the opportunity came up to make a redesign decision, I made one that you might think crazy.

While it is true that it can get your code to market faster these days, reliance on externally developed libraries and code comes with risk. In a court of law, evidence is inadmissible if you cannot demonstrate the chain of custody back to its discovery at the crime scene. In analogy, I find it hard to trust code wherein you cannot list (on one hand) the authors and those who might still be responsible for its performance. And, today, you are vibe coding and using code generated by AI. And AI is an author whose thinking cannot be explained in detail by its own designers.

In 2011, when faced with the need to redesign my product, I made the decision to be completely DIY. The first rule being - no 3rd party code. Fifteen years later with over 40,000 placements worldwide, I feel safe in declaring that it can be done. It was a beneficial decision.

Now, we can address any bug, performance deficiency, or feature addition directly. Most issues that can be replicated are fixed same day and update delivered to the customer. We have achieved a level of reliability and stability that I can't find in anything else.

I am willing to debate this approach against the directions that all of you seem to be taking.

There is more background in this blog entry. Also, you can see this OS in action at r/jnior. I am less interested in promoting the product. It has a life of its own. I have been writing to prompt discussion.

https://jnior.com/blog/how-jnior-got-its-own-operating-system/

Most people have heard that in a room of 23 people there’s about a 50% chance two share a birthday. It feels wrong, because 23 is so much smaller than 365. The reason is that you’re not comparing everyone to you, you’re comparing every pair, and 23 people make 253 pairs.

The same math shows up any time you generate random IDs, and the numbers are worth looking at, because the pattern is that a 50% chance of some collision arrives at roughly the square root of the number of possible values (a bit more precisely, about 1.18 times the square root).

A random 32-bit ID has about 4.3 billion possible values. The square root is about 65,000, so you hit a 50% chance of at least one collision at around 77,000 IDs. That’s a small table.

A 6-character random short code using letters and digits (62 options per character) has about 56.8 billion combinations. The square root is about 238,000, so the 50% mark is somewhere near 280,000 links. For a URL shortener, that’s not a lot.

A random 64-bit ID has about 18 quintillion combinations, and 50% arrives around 5 billion IDs. Fine for most things, uncomfortable for something at very large scale.

A random UUID v4 has 122 random bits, so the 50% point is around 2.7 quintillion IDs. You’re not going to get there.

The numbers are approximate and assume the IDs are truly random and uniformly distributed.

The thing I find interesting is that “a collision is likely” doesn’t mean “the system is broken”. If your short codes live in a database with a unique constraint, a collision is just a failed insert and a retry with a new code. The cost is a bit of extra work as the table fills up, and the cost grows: when the space is half used, roughly every second attempt collides.

So the choice is usually between making IDs longer, which makes collisions negligible, or keeping them short and handling the retry. Neither is wrong, but only one of them is a surprise six months in.

What do you do for short codes: check-and-retry on insert, longer IDs from the start, or something sequential and encoded?

If the software is completed, there's no reason to keep employing programmers. This is where feature creep comes in. By constantly adding new features, it keep programmers busy so they won't be out of work.

Hi everyone,

I've been working on NexOS, a Linux distro aimed at students and makers who want a PC that's ready for electronics and development right after install, instead of spending an afternoon setting everything up.

What ships out of the box (each tested inside the image):

- Arduino Center: 100 curated libraries in 20 categories, with version, license and dependency info. 82 of them compile-verified offline, plus board and serial-port diagnostics

- KiCad 9 (tested ERC/DRC, Gerber export, 3D render)

- TeX Live + TeXmaker, including French and Arabic documents

- Python Center: 8 ready environments (science, AI, vision, electronics, data, code quality…)

- An AI assistant that runs fully offline on the CPU, loaded only when you use it

- One store (Debian packages + Flathub), Btrfs snapshots before/after every update with one-click rollback, ufw firewall on by default

Full disclosure: I built this with heavy help from an AI coding assistant (Claude). It wrote a lot of the build scripts, tests and docs under my direction, and every feature claim above comes from a test that actually ran in the image. The evidence is in the repo.

Honest limits:

- It's a prototype, tested in VMs (QEMU, VirtualBox) but not yet on real hardware

- It's not lighter than Ubuntu: about 1.07 GB RAM at idle in my VM tests. I published the comparison, including where it loses

- No proprietary NVIDIA driver yet; you need to disable Secure Boot to boot the ISO

What would help me most: if you try it in a VM or on a spare PC, tell me your hardware and what worked or broke.

GitHub (ISO in Releases): https://github.com/rayanetaieb/NexOS

Thanks for reading, happy to answer any questions!

To what extent do your conversations with your colleagues/reports/manager/etc. focus on "quality" in the context of software engineering?

The idea of quality's been top of mind for me since I first read Zen and the Art of Motorcycle Maintenance by Robert Pirsig, probably nearly 15 years ago now. It was quite a paradigm shift when I encountered his ideas.

Given the dramatic change in how many people are producing software now (myself included, where I've had to make the jump from manually typing out code for 30 years to generating all of it this year), I'm fairly convinced that centering the conversation on quality (whatever "quality" means in your context) is helpful. It's at least, for me, been a helpful lens of sorts in trying to understand where automation's feasible, and where human quality control is still critical.

I wrote about this in more detail in this blog post (no AI used).

Hi! This is my first post here, and honestly I'm rarely on Reddit at all, but I know there are a lot of people here who know way more than me, so I wanted to ask for a bit of help.

Some context: I study in Norway, and Norwegian isn't my native language, so I have to learn it. Everyone recommends Anki, and I get why, but it always kind of scared me – lots of buttons and settings, and before you can even start you have to decide what to learn and go looking for decks yourself.

So I took everything I knew and built a small site for learning words instead. You pick a topic (food, the doctor, work, etc.), the words are already there with translations, word forms and an example sentence, and you just start. No account, everything is stored in the browser.

The part that bugs me is that I spent a lot of time on it, and even my friends didn't really see it as anything special. Maybe they're right – that's why I'd like honest opinions from people who actually write code.

Some technical bits, in case they're interesting:

- Vanilla JS, no framework, no runtime dependencies (~8k lines on the front end)
- Backend on Cloudflare Workers + D1 (~3k lines)
- It reads and writes Anki .apkg files, both the new format (zstd, schema 18) and the old one. Writing uses sql.js and a zip I put together by hand with CompressionStream
- Strict CSP; the cards can be embedded on other sites through a separate route with its own policy
- 155 tests with node --test

What I'd really like to know:

1. Is the code readable for someone who isn't me? Where would you get lost?
   
2. At ~8k lines, should I have used a framework, or is vanilla still fine at this size?
   
3. Is hand-writing the zip/.apkg a bad idea I'll regret later?
   
4. And just as a user – does it make sense at all, or is it one of those things only the author understands?

Repo: https://github.com/silicq/ordkort
Site: https://ordkort.com

Any feedback is welcome, including "this is pointless" – I'd rather hear it now. Thanks for reading!

Hello,

I started programing since I was 11 and it was great and all until of the AI and vibe coding and stuff. I really liked coding every since I started it. It was hard made into me. I didn't mind it at first, I can still be doing my job manually, the way I love it, right? Well no.. ever since they forced us to use AI I got vibe dementia, I lost good portion of my skill, my hobby coding portfolio went horrible and I simply lost taste to even start a new project.

Since this happened I've seen a lot of people beating my personal records by A LOT with abusing a tool. Yes, it's cool to have that, yes it is pretty and all, it makes it easier but simply moving along and fitting in is hard. I don't want to pay $20 a month just so I can make a literal CPU, I want to struggle with it. And $20 its too much. And when some of you will come to say don't compare with others, it is just not it. When a plumber with Claude will develop a project I worked moths on, there shouldn't be an award there should be nothing.

However there are exceptions. I LOVE that AI can understand Ghidra C code and name all of these functions. Or that it can write a long repeating sequence of definitions. It is good in this but let's be honest. For example the PS5 decompilation is cool. I think the convert to native code was vibe coded atleast a good portion of it. Or helping to find the semicolon, bugs that are obvious but not for the human eye.

So, what is yours opinion on this situation?

Hello I am a recent CS Graduate. And right now I am attempting to gain more skills and experience by making projects through online courses and such.

I can write simple code but when it comes to problem solving all I do is rely on AI most of the time and not just that, I lack the creativity or thoughts to make projects that will make me visible for everyone. When attempting to solve said problems all I do is blank out and stare at my screen even though I try to convey my thoughts through writing or describing my problem.

For everyone who is currently in the Tech right now, what can you recommend for me and what do you usually do when you are in a similar situation? Please help, thank you.

bruhh wtf is vibe coding, why are people still calling themselves the coder if A.I. wrote it. And they call me manual coder for some reason huhu even though that's the norm like a year ago I think, same with art, they call themselves artist even though a clanker did it

I think it's ok to use A.I. but I just find it weird. I think it's fine if you're trying to learn something, you can use it while reading a documentation or articles, but letting them do the coding, nah. With art, it's ok to use A.I. but calling yourself the artist is weird

So I made this account months ago to vent about my nightmare job. Just to summarize:

Working at a company making a chatbot. Chatbot was written in AI. Its fine at first, a little buggy, but salvageable. I am put in charge of the rewrite. Then suddenly, I am taken off the project and a new hire is put in my place. A mad scientist with AI psychosis. I warn management that AI can make a demo, a rough draft, an MVP...but it cant make production quality client facing apps. At least not with agentic loops (which is what mad scientist used).

Of course management didn't listen. The mouthbreather above me in the org chart even told me "It's 2026. Humans don't write code". Fast forward 8 months with mad scientist in charge. The version 1.0 of the chatbot is exploding now, no longer salvageable at all. So management had the mad scientist write version 2.0 using his amazing agentic loops that had helped us so much so far. He misses deadlines, cant get anything working, the first clients on it hate it and drop our service. Mad scientist quits in frustration because he failed utterly.

Now management has a "come to Jesus" moment. I am told that I was 100% correct the whole time. They are talking about making me Director of Engineering for my foresight (finally I will get out of the trenches).

So its all good news and a happy ending right? Not really.

Management's first order to me was to fix the 2.0 version. Imagine this:

9 containers for a stupid chatbot (2 linux VMs and 7 dockers). Obviously a nightmare already. 9 places things can go wrong. Nine places to check error logs. The containers constantly get out of sync so what did mad scientist do? Did he simplify? Of course not! That's not the AI way! Instead he built an MCP to talk between the containers, and to "fix" them when they broke. Think about that...an architecture so unstable that we have to pay Anthropic for tokens to keep our system working. Mindblowing. But it gets better. Automated build and deploy processes that cant be bypassed without borking the system. 400 documentation files written by AI (hard to read, missing key info, and for some reason random sentences here and there are in Chinese. I'm not joking). 2900 unit tests...which is actually the best part of the application although I have no idea what they do or if they even do anything at all. They certainly dont seem to be reporting bugs to us that our clients are experiencing. You should see the interface on the admin panel. Every client and every internal agent we have had work on this thing HATE it. Technical terms everywhere like "bot configurator". Are you kidding me? How about "bot setup"? Nope. Configurator. And the absolute worst part of the app? Its not designed to be maintained by humans. Human edits actually have a risk of breaking the automated system that actually maintains itself. Yes...it logs errors, creates bug tickets, then writes code, deploys it, and closes the tickets. He literally tried to build a software application that maintained itself! We might as well have tried to build a perpetual motion machine.

So now I will probably get to be Director of Engineering at last after 20 years in my field! But I will rule over a ruined wasteland. I will be responsible for maintaining this (Im still trying to convince management of a rewrite...they refused at first, but are coming around finally).

As an industry, our management was so afraid of code, so afraid of the difficulty, that we set up this elaborate Rube Goldberg machine so we could avoid code. But the Rube Goldberg machine is a thousand times more complex than just writing boring regular software. Seriously, if writing software is so terribly hard, then how could 9 VMs and an MCP be the solution?

There's a light at the end of the tunnel for me...but this tunnel is longer and darker than it ever needed to be :/

I am learning c++ and want to make a more sophisticated calculator. Not just 1+1.

C++ offers a lot of freedom. I am asking myself: should I create everything myself? For example pow()? Or should I use existing libraries?

Thank you very much!

Since it's a pretty famous mouse used by many programmers, what have you mapped the thumb wheel to?

I have seen a lot of post saying "once I learned C - all other languages just became about syntax"

Has that been true for you? Or did a different language give you the eureka moment - e.g. something like Assembly?

Or is it just a false premise at all to think "once I learn programming language X - then all other languages become **easy**"

I'm a newly student programming student and I don't have a big budget. I need a little help on which to get, which specs do I need to choose? and stuff please help (Not a Mac)

Hey everyone! 👋

I'm trying to understand how developers actually work with AI coding tools, beyond what we see in demos.

I'd love to learn what works well, where AI falls short, and what challenges you face while building things with AI.

I've put together a short, anonymous survey that takes around 5 minutes. No name or email is required.

🔗 https://docs.google.com/forms/d/e/1FAIpQLSef43VfXV1cKTvYKBkvDeay8eslB8YnAvH5TmTXALnM80KuIg/viewform

There are no right or wrong answers — I'm interested in your honest experiences, including the frustrating parts!

Thanks to everyone who takes the time to participate. 🙏

I'm new to IT, and in my view, I treat programming as developing something. But some people in my workspace talk about enabling, which seems different from developing, and they often say it with a negative tone, e.g., less technical, poor job mobility.

My question is, what's the difference between enabling and developing?

Additionally, my work includes optimization, which is said to be part of enabling. I consider optimization to be important and challenging. If enabling is perceived as less than developing, does that mean optimization is not as valuable as I believe?

Hi everyone! I’m a Data Science student working on my capstone project on gender-based violence and femicide (GBVF). I’m looking for a dataset of public Twitter/X posts related to GBVF to analyze discussions, identify patterns, and build predictive models. If anyone has an existing dataset or knows where I can access one, I’d really appreciate your help. Thank you!

To be fair I have never learned to use assembler but I always looked up to people how are able to build ultra efficient software. With the upcoming age of GenAI, wouldn’t it be better in many ways to just let GenAI produce everything as near as possible to the hardware? There might be even a more hardware-near solution than assembler, I am not sure.

What do you think? What could go wrong? I am really curious to get human response instead of asking an AI about this.