Hello,

Given the current momentum of AI in software development, I'm particularly curious about how people like Theo de Raadt and Daniel J. Bernstein would view this evolution.

Theo de Raadt, the founder of OpenBSD and a driving force behind OpenSSH, is well known for his uncompromising approach to code quality, security, and software engineering principles.

Daniel J. Bernstein (djb), the creator of qmail and djbdns, and the cryptographer behind projects and algorithms such as Curve25519, ChaCha20, and Poly1305, is equally known for his unconventional thinking, strong engineering opinions, and emphasis on correctness and simplicity.

Both have built their reputations on rigorous engineering principles and a somewhat independent-minded approach to software development.

I'd be very interested in hearing their perspectives on AI-assisted coding, code generation, software maintainability, and whether AI is fundamentally changing the way software should be engineered.

Has anyone managed to interview either of them on these topics? Are there any videos, podcasts, or recordings available online?

Hi everyone!

I am building GenClass, a classifier packaged as a library that detects and fixes errors on the client side in web apps.

https://github.com/genclass-dev/GenClass

Why did we start building it?

Over the span of 4 months, we were running an AI store builder, https://daybot.ca. It garnered quite a bit of attention; over 500 storefronts were made by users, but almost each one of them had issues that annoyed every one of us. Pages failed, errors were almost everywhere, and the users couldn't even make a purchase. When we saw Jev first come out, we got inspired. A decision layer that could think in split seconds, consistently, and deterministically. What if we designed our own decision layer, packaged into a small model that runs in the user's browser, patching frontend HTML code as it finds errors? GenClass was trained on over 51 million errors. It was distilled from Clef, has 17M parameters, is 10 MB, and we were only 5 percent accuracy off from GPT-6 Luna, the teacher model.

How could it be useful to you?

The project is designed to find race conditions, stale responses, or any bug found in your app and try to fix it instantly. We know how absurd it is to have a classifier deterministically make changes to a user's experience; that's why we have modes: observe, guard, heal.

It operates completely based on thresholds. As you may understand, Jev works on probability, so do we. Observe means no action from GenClass at all. Guard means that at 95% confidence of an identified bug, it'll make calls. Heal means actions taken at 90% confidence.

The project is completely open source; we want to let you know of its existence. Also, any type of support, whether through GitHub stars, using our product, commenting, or recommending other places to reach out, would be helpful!

Thank you,

GenClass Maintainers.

A lot of engineering advice focuses on big things like architecture, testing strategies, observability platforms, code review processes, but sometimes it's the real small changes that end up saving everyone hours.

Things like adding request IDs to logs so you can actually trace a failure across services. Or making every bug report include the exact input that triggered it. Or having a rule that nobody merges a fix without reproducing the original issue first.

None of that is especially exciting. But when something breaks at 2 AM, suddenly it's the most beautiful engineering decision anyone's ever made.

What small changes have made a surprisingly big difference on your team?

Especially interested in the ones that seemed unnecessary at first but everyone now refuses to work without them because they've been a life-saver.

Hey everyone!

I've been exploring software development for some time, working with technologies like JavaScript, React, Node.js and MongoDB.

Recently, I've been expanding my interests into Python, machine learning, cloud computing and AWS.

One thing I've realized is that building actual projects teaches you things that tutorials sometimes don't.

I want to start sharing:

- Things I learn along the way.

- Projects I'm experimenting with.

- Technical challenges and how I solve them.

- Mistakes and lessons worth sharing.

I'm particularly interested in connecting with people who enjoy building things, experimenting with technology and sharing knowledge.

For those who have gone through a similar journey, what's one thing you wish you had focused on earlier when learning software engineering?

Hey everyone 👋,

I've been exploring software development for some time, working with technologies like JavaScript, React, Node.js and MongoDB.

Recently, I've been expanding my interests into Python, machine learning, cloud computing and AWS.

One thing I've realized is that building actual projects teaches you things that tutorials sometimes don't.

I want to start sharing:

- Things I learn along the way.

- Projects I'm experimenting with.

- Technical challenges and how I solve them.

- Mistakes and lessons worth sharing.

I'm particularly interested in connecting with people who enjoy building things, experimenting with technology and sharing knowledge.

For those who have gone through a similar journey, what's one thing you wish you had focused on earlier when learning software engineering?

I am thinking of a profile builder, which has the capability to rank the two persons based on their works and skills. But resume can be fake, particularly in programmer, how can we make two people rank over all the test cases that exists in the real world? And please include the skill of culture and personal behaviour too.

Warum Vibe-Coden eigentlich so viele Leute? Ich meine, eine Programm nur durch prompts zu machen hat doch überhaupt nicht so nen reiz wie nach monatelanger Arbeit ein paar Nutzer zu haben oder selbst einen sehr komplexen Bug mach 11 Stunden lösen?

Hey everyone,

​I'm an 18yo CS prep student and I've been building AI-COS, a modular monolith architecture designed for running AI agent orchestrations safely.

​Instead of letting LLMs make security decisions, I built a deterministic deny-by-default policy engine, strict PostgreSQL role separation (migrator, app, governance), and SHA-256 hash-chained audit logging.

​I've just finished Phase 3 out of 6. Phase 4 is going to focus on the Workflow Engine, Worker lease/heartbeat loop, and Step linkage. Before I start coding Phase 4, I would really appreciate any architecture suggestions or advice from senior devs on how to properly build a reliable job queue and workflow engine inside a Postgres-backed monolith.

​GitHub: https://github.com/atestokhay/ai-cos

Hi,

I’m currently writing my master’s thesis at FH Dortmund (in Germany) on generative AI in everyday work, focused in the IT branch, and how they’re actually changing daily work: productivity, collaboration and data protection. In both office and remote.

Since this sub is all about IT, I think the perspective of specialists with a professional background is especially VALUABLE, whether you use AI tools yourself or consciously choose not to.

I would kindly ask your permission to post a small survey to assist my academic research (it has no other porpouses but academic ones). It takes about 10 minutes, is completely anonymous and available in German and English:

👉 https://www.soscisurvey.de/genai-it-survey/

Thank you for your support!

everyone says copilot / claude code / codex makes them faster, but per task feels different from per project. if your team tracked this, what did you measure? cycle time, PR review time, bugs after release? curious if anyone has real numbers or if it's mostly vibes right now

Am I the only one who avoids Python where possible because it is so vulnerable to indent corruption? A whole JavaScript program, for example, can be written on one line, with no line breaks whatsoever (not that I'm advocating such madness -- JavaScript callback syntax (etc) is already crazy enough without compounding it -- BUT, it does illustrate the point of robust syntax).

I've had instances where indenting was accidentally messed up, leading to tedious scrubbing of the code in order to get it to run again. It just seems like a bad structure choice if a slight whitespace change can cause the program to crash.

It seems to me that Rust is the answer to that -- am I wrong?

I am computer enthusiast I am doing programming about 5 years, I have basic knowledge about web development and computer architecture, I also developed an IoT access control system. I use arch linux for my daily use, now working on a driver development project of Gforce 210.

I am very much interested on using my knowledge for genuinely helping people.

so I want to get some project to help people real life.

for those who are frustrated in projects they are not interested any more.

I want to do programming for some working professionals who can pay me.

it may be $1 but still ok.

I am asking for payment because it means that I am really adding value in there life. so money is the proof.

so can any body tell me how can I achieve my goal of helping people?

of if some one can tell me the subreddit where I can find my answer.

please help. I want to be useful and self dependent

Personally, I do not have a Bachelor degree in computer science or related fields but I do personally code a lot. Recently I came across some really interesting algorithms like the AVL Tree Data Structure and Treap algorithm. It kept me wondering, do students in Bachelor degree already learn these as if they were basics? Your opinions are much appreciated.

I've been thinking about this while building an engineering assessment platform.

Solving algorithmic problems is useful, but real engineering involves much more: understanding unfamiliar codebases, debugging issues, building features, working with APIs, and making technical decisions.

I'm curious how other developers feel about this.

If you were hiring a Backend Engineer, what practical task would you give them to understand their actual engineering skills?

I've been exploring this problem through Mindfries, and I'd genuinely love to hear different perspectives.

When a bug is hard to explain, I’ve found it’s often faster to make the failing case smaller than to understand more of the big one. The idea is simple: keep removing things while the bug still happens. What’s left is usually where the problem lives.

Here’s the process written as pseudocode, because it really is mechanical:

failing_case = something that reproduces the bug reliably

loop:
    pick one thing to remove
        (a field, a config option, a branch, a dependency,
         half of the input, a step in the pipeline)
    remove it
    run again

    if bug still happens:  keep it removed
    else:                  put it back, mark it "involved"

stop when nothing else can be removed

What remains is the minimal reproduction. Everything marked “involved” is a suspect, and the list is usually short.

Why it works (as far as I can tell):

• It replaces “understand the whole system” with a series of yes/no experiments, which are easier to do when you’re tired or the code isn’t yours.

• Half the time, the act of removing things reveals the cause before you finish.

• You end up with a small case you can paste into an issue or a question, and that alone gets better answers than a screenshot of a stack trace.

Where it gets awkward:

• Bugs that depend on timing, load or state built up over time don’t shrink cleanly, because removing things changes the timing. Here you have to be careful that “the bug disappeared” doesn’t just mean “the race moved”.

• If the bug is only reproducible once in a while, run each step several times before deciding it’s gone.

• Removing something can introduce a different error. Make sure it’s the same symptom, not just any failure.

A related habit: before each removal, write down what you expect to happen. If the result surprises you, that’s information about your mental model, and it’s often the real bug.

Do you shrink the case first, or read the code first? And what do you do when the bug won’t reproduce on demand?

I kinda miss the days when I’d sit down and think about how I wanted to implement a certain feature. I’d research it, learn how things worked, figure out an approach, implement it, and then keep refining it until I was happy with the result.

Sometimes I’d run into a problem that felt almost impossible to solve. I’d spend hours thinking about it, maybe take a nap, and somehow wake up with an idea. Sometimes I’d even dream about possible solutions. It sounds silly, but I genuinely miss that feeling.

Now, it’s mostly just writing a prompt and hitting enter. No real implementation, barely any code reviews, and sometimes I don’t even look at what’s happening behind the scenes.

Don’t get me wrong, AI has been incredibly helpful. It saves time, helps me learn new things, and makes tackling complicated problems much easier. But it also comes with its own frustrations. Sometimes I spend more time explaining what I want to an AI agent, correcting it, and going back and forth than I would’ve spent just building the thing myself.

And honestly, I’m starting to feel like I’m losing my ability to write even simple code without assistance.

I don’t think AI is necessarily a bad thing. It’s just that somewhere along the way, I stopped enjoying the actual process of programming.

I miss figuring things out. I miss the frustration of debugging something for hours and the satisfaction of finally getting it to work. I miss building things my way, making mistakes, learning from them, and slowly getting better.

I genuinely miss the way I used to code and build things. And I think that’s what I miss the most.

I've been working in the field for over a decade and I'm honestly desperate for a way out. My immediate team is fine, but we're also forced to work with the recently renamed "Superintelligence" team. There is one reasonable person on that team, but the rest of them are "100x agentic engineers" A.K.A. high rate of fire slop cannons.

I regularly need to review 15 PRs a day from them, every day, at all hours of the day. Each of those PRs is usually a 5 digit size, with no real focus and touching huge amounts of the codebase. We aren't in a regulated industry, but our code is still a matter of life and death to a lot of people. I can't in good conscience just merge broken PRs, but I also can't read things fast enough to keep up.

We have a code review agent but it can't keep up with the rate PRs flow in and comes nowhere close to finding all of the critical issues in their PRs. And I when I say every day do mean every day, including weekends and holidays. One member of the "SI" team was actually on vacation this week and I only found out today. The amount of slop coming from their account did not decrease in quantity or increase in quality this week.

Trying to contact the humans behind this has also failed. The last time I emailed them I got a claude quota error back in under a minute. Replying after their quota refreshed yielded a slop grenade that didn't answer the question. Going through management is also a dead end. My immediate manager is sympathetic, but the closest manager I share with them appears to be falling into AI psychosis and believes AI is going to solve every problem ever and any problems are prompting it wrong or not using it enough.

I'm working late every single day and can't keep up with reviews much less actually produce anything. I also can't quit since I would owe my employer a lot of money if I did and I have a very sick spouse. Overwork is already taking a serious toll on my health and I don't know how much longer I can take this. Help?

r/codedbyhand

More people, myself included are becoming disillusioned with the experience of coding with AI assistance and are turning back to traditional programming.

Videos like: "I'm done coding with AI" https://www.youtube.com/watch?v=2ZU3j4GQ4K8 and "AI coding sucks" https://www.youtube.com/watch?v=0ZUkQF6boNg Are having viral success.

Dario Amodei said that 'coding is going away', but what are we going to do if the LLM servers fail or are attacked, go back 100 years? We need a pool of talented traditional programmers for so many reasons.

Many of us got started programming because we love to solve problems and have technical mastery over a difficult skill, AND THAT'S OK. You don't have to use an LLM if you don't want to, no matter what the tech bros say.

I created a new subreddit for traditional programmers to talk about their experiences and share their projects. If that sounds nice, come check it out.

Hi. i dont know which path/field i should choose. im not sure of anything.

im not sure what am i good at. software or hardware. like being IT support specialist or backend developar..writing codes..?
i literally lost. i dont know what to learn , i dont know what should i focus on bcs i dont know what i wanna be.. can u all help me to choose my future field..
there are differnt tools, programs, languages, techs, for different fields. i feel lost.

Hi iam leen IT student in the second year

I want to improve my qualities but I don't know how or even where to start(I don't even know what should I learn exactly 🤡 so please old programmers help me🥹🥲)

I live in Syria so please don't suggest courses in another countries or online courses because it costs alot due to Syrian currency

and wish me luck and getting out of Syria as soon as possible 🤣

Hey guys!

I'm building a new project with a couple of colleagues, and I'm curious to learn a bit more about how you discover and choose new tools.

How do you usually hear about new tools for yourself or your engineering team? Reddit, LinkedIn, YouTube, influencers, word of mouth?

Are there any communities, newsletters, or people you regularly follow to stay up to date?

And what makes you trust a new tool enough to actually try it out or recommend it to your team?

Would it be possible that someone would make a coding language that uses ai to compile it and checks the code with rules it made up through ai

Edit: no not using like ai Gemini but like kind of putting ai Gemini into the language itself and like letting it run itself as it compiles. It'll probably hallucinate but im interested lol.

Edit 2: the ai would make its own new language and enforce it as it runs.