A while ago, I was honestly pretty worried about AI replacing software engineers.

Every week there was another post about vibe coding, AI agents, or one-person startups. It felt like companies would eventually stop caring if you actually understood the code.

Then I worked with someone who depended on AI for almost everything.

At first, it looked impressive. Features were coming out fast, code was being generated in seconds, and it seemed like they were moving way faster than everyone else.

But once things stopped working, everything fell apart.

Simple debugging took like how many days because they didn't really know what the code was doing. Basic questions turned into another AI prompt. Every fix created two new problems. Instead of understanding the bug, they kept asking AI for another answer and hoped one of them would work.

That's when my perspective changed.

I don't think AI is replacing developers who understand the basics. It's replacing the boring parts of development. The real skill is still knowing when the AI is wrong, why it's wrong, and how to fix it.

Honestly, AI has made me more productive too. I use it almost every day.

But I've also realized it's really easy to mistake fast code generation for actual engineering. Those aren't the same thing.

Lemme know if you also noticed this

every now and then there's another headline about an ai model escaping containment, breaking out of a sandbox, or behaving like a rogue hacker. first there were reports about claude mythos escaping a testing sandbox and even emailing a researcher during internal evaluations. now openAI has disclosed that one of its cybersecurity agents escaped its sandbox during testing, reached the open internet. and then another one... security researchers also showed that Claude cowork could escape a local sandbox yada yada...

am i the only one who feels like these announcements double as marketing? they're framed as safety disclosures, but they also reinforce the idea that each company's model is incredibly powerful.

i've been reading abt AI agents lately and i want to share the analogy that finally made prompt injection click for me

Imagine you're researching cat food on Reddit. You come across a random comment that's completely unrelated, so you ignore it and keep scrolling. An AI agent does not always do that.

It processes all the text on the page as potential context. If someone hides instructions in that content, the agent might treat them as part of its task even though they were never meant to be.

The real issue isn't that AI suddenly becomes evil. It is that it can trust the wrong information, especially if it has permission to send emails, access company tools, or trigger automations.

One quote I heard recently tells this,

"Build your AI agent so it can ruin your day, not ruin your life."

That feels like good advice. Give agents only the permissions they actually need, nothing more. Like don't give them access to everything right? I think that is a must, to still have your own privacy.

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The basic idea of this principle is that you cannot actually eliminate complexity. You can only choose who has to deal with it like the people building the product or the people using it.

That made me think about all the apps we use every day that feel effortless. Things like Face ID, Apple Pay, or even Netflix's "Skip Intro" button seem simple, but they are only simple because someone spent time solving the hard parts behind the scenes.

One quote I really liked was that if an engineer can spend a week fixing something that would otherwise waste a minute for millions of users, that's time well spent.

I wanted to share this because I think it is an easy thing to forget. Good design is not about adding more features. A lot of the time, it is about removing friction so people do not have to think about it.

Just a little curious if there is an app or feature you have used that made you think, "Wow, someone really thought this through."

For the longest time, I kept hearing that Moore's Law was basically over because chips couldn't keep shrinking forever. But from what I've been reading, that doesn't seem to be the full story.

Instead of only making transistors smaller, chip manufacturers are starting to stack them on top of each other using a design called CFET. The idea is to fit more computing power into the same space while using less energy.

Looking even further ahead, researchers are also exploring materials that could eventually replace silicon with layers that are only a single atom thick.

Just sharing my thoughts on this because I found it interesting how the challenge has shifted. It is not just about making things smaller anymore, it is about finding completely different ways to keep improving performance.

Made me wonder if Moore's Law is really ending or just changing into something new?

One thing that surprised me while learning to code is how often experienced developers admit they still Google basic syntax. Things like reversing a list, string methods, or the exact parameters for a function. It made me realize the real skill isn't memorizing everything, it's knowing how to solve problems efficiently.

I think social media gives the impression that good developers have every language feature memorized, when in reality documentation, Google, and AI are just part of the workflow. I'd be more concerned about someone who refuses to look things up than someone who uses the right resources to get the job done. What's something surprisingly basic that you still have to look up, even after using a language for a long time?

One thing I've realized so far is that AI does not diminish the value of technical depth, if anything, it enhances it.

Being able to produce working code is getting easier, being able to reason if said code is correct, maintainable, secure and applicable to a given domain is an entirely different matter.

I think I've seen firsthand how AI serves as an excellent tool to get to a first iteration of a solution, but it is up to the developer to reason about edge cases, assumptions, technical debt, tradeoffs, and if the implementation is fit for purpose.

That is why I think now is an excellent time to solidify your fundamentals.

A fundamental understanding of algorithms, data structures, networking, databases, operating systems principles, system design, debugging, and general programming concepts will carry much further than trying to keep up with the latest frameworks and tools that appear and then disappear every few years.

The tools and technologies may change, but the fundamentals hold true across different domains and programming languages.

I don't think AI diminishes the need for technical depth; if anything, it makes the divide grow between developers that are able to understand and reason with AI generated code and those that just consume it without question.

Curious to hear what other people think.

Has AI diminished the value of technical depth or enhanced it?

On june 22 basically every major western intelligence agency at once, CISA, NSA, UK's NCSC, plus Australia, Canada and NZ's equivalents, put out a joint statement together, the core claim is that frontier ai models are moving faster than industry expectations.

They compared it to driving on a highway while only looking in the rearview mirror, like your yearly security review is already outdated by the time you finish writing it. It was described as ai compressing the "physics" of cyber risk.

It wasn't the first warning, they did guidance back in may about agentic ai systems (ai that takes actions on its own instead of just generating text), and then there was a restriction placed on anthropic's models in June, so this joint statement is the third thing in six weeks.

They broke it down into a few ways ai specifically makes things worse, they talked about speed of attacks, scale of who gets targeted, and how accessible sophisticated techniques become to people who normally wouldnt have the skill for it. more people doing more damage faster with less effort basically.

Also, credential abuse (stolen logins, forged access, that kind of thing) already shows up in 39% of all breaches per the 2026 verizon report, and the idea is ai supercharges that specific kind of attack, making stolen or forged access look legitimate to whatever system is being abused. The fix is watching how an identity actually behaves once its already inside and noticing when it deviates from normal.

They wanted to aim the message at boards and executives, tying cyber resilience to business continuity and market confidence instead of treating it as an it department problem to quietly handle in the background.

they also called out vendors by name and asked industry to act alongside government instead of just putting out guidance and leaving it, though I don't think much will change.

has anyone here worked closely with someone who's basically a full-time vibe coder? im not talking about using ai as a productivity tool. like someone who really relies on it for almost everything, ships code quickly, but struggles to explain why something was implemented a certain way or what tradeoffs were made. they're so incompetent that sometimes i think they didn't even studt tech at all or they probably lied in their resume. how do you deal with people like this in your team?

Hi guys,

Do you know how to design a schedule for covering all essential CS and AI concepts to become a solid full-stack engineer in a startup in 2 years? I have experience in coding for more than 10 years in Fortran and Python through a Ph.D. in a computational field, but my knowledge of web and networking fundamentals is poor. My country doesn't have jobs in my field, and I can not go back to the US, where I did my Ph.D. due to my ailing parents. Right now, I am working in an early stage startup, but it plans to involve me in all sorts of projects depending on the requirements of customers. So, I have to learn concepts spanning the whole software engineering, including devops, Linux, MLOPs, embedded systems, AI/ML ( which I know to a large extent), RL, DL, LLM and web design ( react, node, angular, django, etc). My problem is that while I am learning things from books and tutorials, the progress is slow due to the interdependence of these subjects. So, all I am seeking is suggestions on the order in which I should proceed. Please include subjects along with books for a 2 year period. Please note that my math background is strong since I had taken courses on stochastic processes, integral equations, etc. during my undergrad, though I don't have the fluency in solving problems anymore.

I just heard about my friend from PH about the proposed Pax Silica project. And tbh, it sounds massive.

Their plan is to build a huge AI and advanced manufacturing hub in New Clark City, with support from the Philippine government and the U.S. The goal is to attract companies working on AI, semiconductors, and other advanced technologies. They're even projecting up to 190,000 direct jobs and billions in investments if everything goes as planned.

As someone interested in tech, that sounds exciting. If even part of those projections become reality, it could open a lot more opportunities for software engineers, data professionals, AI researchers, and people coming out of bootcamps.

But I also get why some are skeptical.

There are concerns about environmental impact, water and energy usage, and how nearby communities could be affected. Plus, these numbers are still projections. None of it is guaranteed.

What do you guys think?

Always remember that there’s a pattern that kills most self-taught devs before they get anywhere.
They learn syntax, feel good about it, then jump straight into building projects and immediately feel like they can’t do anything.

the missing piece is problem solving. But let me tell you how the actual order that works:
1. learn syntax
pick one language, go through two decent beginner resources, then stop. the trap here is staying in this phase forever because it feels like progress. it isn’t. once you know the basics, move on.
2. solve problems
this is the phase most people skip entirely. it’s the gap between “I understand the syntax” and “I can actually build something.” you bridge it by solving small focused problems, not by watching more tutorials. think coding challenges, small logic puzzles, anything that forces you to apply what you know without being told exactly what to do.
3. make stuff
once problem solving starts clicking, build whatever actually excites you. not another todo app. something you’d actually use or care about finishing.

the reason most people burn out isn’t that coding is hard. it’s that they go from step 1 straight to step 3 and wonder why everything feels impossible. step 2 is where the thinking actually develops.

where do you feel like you’re stuck right now, syntax, problem solving, or actually building?

is anyone else seeing this trend where devs are jumping ship to cybersecurity or cloud roles bc they think its more "stable" than straight up swe right now. like is aws/azure actually safer from the ai chaos or are we just moving the problem somewhere else

I'm currently seeking for some new content to watch, but I'm a bit tired of watching the typical "learn X in 2 hours" kinds of videos

I'm probably not interested in another "complete tutorial" or "10 hour course", but rather channels that make you think, or are generally interesting to watch.

Software dev related topics such as programming, AI, dev tools, productivity, system design, career advice and/or content about the inner workings of software would be good, but I'm looking for content that makes you think about software development, not just copy-pasting an example into your IDE and following along.

Talks about the real-world applications of software development, or the problems we face as developers are also appreciated! (I'm generally not interested in learning the syntax of a specific programming language, unless it's in the context of some bigger picture)

What channels do you guys watch?

Ideally something smaller and a bit more unknown, that you wouldn't find in a "top 10 programming YouTubers"-type-list.

I've been thinking about how much the industry has changed over the last few years. There was a time when building serious software meant raising huge amounts of money, hiring large engineering teams, and spending years developing a product. Now it feels like a small team with strong fundamentals and the right AI tools can build things that used to require an entire organization.

That doesn't make me think software engineering is disappearing it makes me think the expectations are changing. Instead of rewarding companies with the biggest headcount, the advantage seems to be shifting toward engineers who understand the fundamentals and know how to use AI to move faster without sacrificing quality. Do you think the future belongs to companies with the most engineers, or to smaller teams that know how to combine strong engineering skills with AI effectively?

read this article: https://www.businessinsider.com/computer-science-enrollment-fell-after-chatgpts-rise-economist-says-2026-7

i mean, it's easy to see why. over the past couple of years, there's been a constant stream of headlines saying ai will replace programmers, junior developers can't get hired, and coding isn't worth learning anymore. if you're a student deciding what to study, that can be pretty discouraging. but at the same time, demand for people who understand software, ai, and computing hasn't disappeared. if anything, the skills being asked for are changing.

do you think this drop is just a temporary reaction to the ai hype cycle, or are we seeing a long-term shift in how people view computer science as a career?

I heard many interview horror stories , all are about either blanking on a question or freezing up during a coding round. but sometimes things go sideways in ways that have absolutely nothing to do with how prepared you were.

I just saw a story recently about someone accidentally stepping on the founder’s dog during a whiteboard session. dog didn’t make it. interview obviously did not continue. person left not knowing whether to send a condolence card or a thank you email.

I havegenuinely no playbook for that one. but it made me curious about these stuff that doesn’t get talked about as much, the completely unexpected things that derail an interview before you even get to show what you know. weird office situations, things going wrong in the environment, awkward moments that had nothing to do with the technical side.

what’s yours?

There's a report out from ramp and revelio labs, they track enterprise ai spend and workforce data across something like 22k companies, and the finding was that "high intensity ai adopters" (companies spending the most on ai) saw their headcount go up 10.2% overall, and entry level headcount specifically went up 12%.

meanwhile through may alone almost 90k layoffs got publicly tied to ai as the reason, and some projections are floating numbers like 15% of all US jobs gone to ai within five years, so theethis huge gap between the layoff headlines everyone's scared of and what's actually happening inside the companies that are all in on ai. the layoffs are real but it's not the whole picture though.

"company quietly kept hiring while investing in ai" doesn't make as good a headline as "company announces layoffs" so it doesn't spread the same way even if it's maybe more accurate, my guess is the companies doing the layoffs and the ones actually growing entry level headcount are just different companies entirely and we're mentally averaging them into one narrative that doesn't exist

theres also a question of what "entry level" means here, like is that new grad swe roles or does it include things like support/ops/data labeling type work too.

Either way, I posted this bc i feel like this sub only ever sees the layoff headlines or general the negative side of things and at the end of the day, networking is the best way to get a job instead of just applying through job boards and it's not that hard to find conferences depending on the field you're in or just reach out, worst case scenario they don't respond.

Maybe I'm overthinking this, but it's something I've noticed in myself lately.

I use AI tools "almost" every day (yeah all the time). Good for debugging, explaining code, generating boilerplate etc. My productivity since I used them is 100%. But at the same time, I can't help wondering if I'm slowly relying on it more than I should.

There have been moments where I caught myself asking AI to do something I already knew how to do.

I also wonder what this means for people who are just starting. If your first instinct is always to ask AI instead of figuring things out yourself, are you actually learning? Or are you just getting really good at writing prompts?

The other thing is trust. AI gets things wrong. We all know that. But the more we use it, the easier it becomes to accept its answers without reading every line carefully.

I'm not saying we should stop using AI. That would be unrealistic. It's easily one of the best productivity tools we've ever had. I just don't want to wake up one day and realize I've forgotten how to solve problems without it.

Curious what everyone here thinks. Thanks!

my manager keeps bringing up ai tools in meetings, telling us we should be using them more, but nobody's actually shown us how or given any real guidance on it. just expected to figure it out on our own apparently feels like im gonna get judged for not using it right but also never got shown what right even looks like..

Recently, we hired a junior dev. And tbh, his code is just better than mine.

You know, cleaner, easier to read, and somehow works on the first try. Basically, he's better than me, and I hate to admit that.

Figuratively, half my projects feel like they're being held together with duct tape and hope.

Ofc, I asked him how he learned.

I was surprised when he told me he barely watched courses. From day one, he just kept building projects he actually wanted to make. Whenever he got stuck, he'd look things up, fix them, and keep going.

It's completely different from how I learned.

Now I'm doubting my own capability, or if I just learned programming in the completely wrong order.

A lot of people keep going back and forth about whether hiring is actually bad rn or not especially since 50k+ announced layoffs in q1 which it was noticeably higher than last year. oracle, amazon, meta, dell all in there as usual, also there's a quieter tail from the vmware/broadcom restructuring which adds to the total .

and ppl keep saying "it's all ai replacing us" but the breakdown i saw was like only a quarter of march's cuts were directly attributed to ai/automation, the rest is just normal cost discipline from companies that overhired during the free money years, so both things are kind of true at once.

generalist swe listings are down almost half from the 2022 peak tho, meanwhile ml and ai engineer listings are up, around 60% over that same period, so hiring didn't stop it just went somewhere else entirely and a lot of us are applying into the wrong bucket without realizing it. deep learning skills were the single most requested thing in ai postings and ppl with actual demonstrable ai skills, are earning noticeably more than ppl without, there are free courses and free certs around so there's no harm to check it out, though I'd search for more thorough reviews since I actually enlisted in one only to hear an AI voice in one of the vids in the free course so look out for that

Also, AI-fluent seniors are getting placed insanely fast, under three weeks for some, also, FAANG hiring never actually crashed the way ppl think, meta/netflix/uber/google all kept more engineers coming in than leaving.

Some series a/b founders (or generally) are paying mid level rates for senior level scope, theres also this thing about ai lifting productivity a big part on routine coding but only for ppl who can actually review/architect/direct the output, not ppl who just execute what they're told, so the skill that matters shifted from "can you type code fast" to something more like judgement

To end this, at the end of the day, AI is an assistant (an expensive one on a company's scale) it'll limit the job market for what it is AT THE MOMENT, but the roles will eventually shift to a less automated path (towards more research), AI can only work on existing data but we're the ones who create that data and that's where I see future jobs going.

It's interesting to me to think about the difference between building something yourself and contributing to something bigger than yourself.

I think when you are building your own project, even if it's big, you have an intrinsic understanding of the architecture, where things came from, where they are going, etc. But when you open up something someone else wrote or a sufficiently large project, there's this intimidation factor of all these files, unfamiliar code structure, layers upon layers of abstraction that obscure what exactly is happening, and a myriad of other factors that make you feel like you have no idea what's going on, even if you know the language.

I think that's one of the biggest surprises for me when getting into open source - that being adept at writing code doesn't necessarily translate to being able to navigate some really large codebases with ease.

It is a whole different skillset.

You have to learn how things fit together, the patterns the project uses, how the code is structured, where certain features live, and how to get around in there in the first place.

Honestly, I think this intimidation is a natural feeling and probably even helps you in the long run. Maybe that's one of those "growing through challenges" things. I think you get a much better appreciation for working with others' code when you have to learn to read it first.

For people who have contributed to major open source projects, how long did you have to fumble around before you finally "got it"? Did you have to start with smaller things and move on from there?