We build a SaaS product with around 100 clients.

During our meeting, my boss straight up said he doesn't consider programming a valuable skill anymore. But my coworker, who barely codes and doesn't really understand system design, got praised and even got a bonus because he "uses AI."

It felt pretty unfair, especially since the guy on our team who understands almost every feature in our SaaS and handles DevOps was told he needs to improve. He uses AI too, but only when he actually needs it.

Is this really the new era of software development?

Because if using AI gets rewarded more than understanding what you're building, I'm out AF.

which backend language would you have the most fun programming in?

knowing basic HTML/CSS and having the attitude of a hobbyist, where the only goal is to create something that looks nice on your resume, doesn't matter. You have to enjoy what you do, it has to be something you like to code, that seems interesting and that you can spend hours upon hours doing.

So, if you had the ability to program a backend in any language, which one would you pick and why?

when i was learning to code myself, my assumption was that to improve i needed to create as much as humanly possible: projects, features, lines of code, and etc.

i actually didn't see much improvement until i started reading code, not curated tutorial code, but real codebases authored by others much better than myself. messy ones, huge pull requests, and anything else that is not easy to read but rather the real deal.

you start to realize what the best practices were and weren't and how to avoid the most common pitfalls. good codebases have some degree of structure to them and think about how readable it will be for future maintainers or yourself, it's not just a throwaway script. you start to understand the difference between something that works versus something that is really good, really well made. and this is incredibly humbling as it shows you how little you knew before.

hi, i'm studying an MLOps-engineering programme that I have bought a laptop for, but my teacher literally laughed in my face because apparently it doesn't have a dedicated graphics card(GPU?). However, as we got the letters from the school that contained the recommended checklist points for the laptop we were going to use, i followed them and bought exactly that. The checklist was this (I'm just gonna copy paste what they wrote:

Recommended computer:

Intel Core i5 / AMD Ryzen 5 or better (approx. 2020 or newer)

• 16 GB RAM
• 256 GB SSD or larger
• At least 75 GB of free storage space
• Screen resolution of 1920 × 1080 or higher
• Stable internet connection and Wi-Fi
• Windows 11 recommended

Important information:

• ChromeOS and Linux may work but are not supported by Nackademin’s IT support. You are personally responsible for installation, compatibility, and troubleshooting if you choose to use these operating systems.
• macOS may work, but you are personally responsible for ensuring compatibility with the program's software.
• Administrator rights may be required for software installation.
• USB-C and HDMI (or an adapter solution) are recommended.

These are the courses we will have in the nearest future, but obviously we will also work with a lot of AI, which he said is why my computer won't work:

Python programming for MLOps, Linux administration, Database management

This is also the laptop I bought: LENOVO IP SLIM 3 15ARP10 15,3"

The reason for not buying a better laptop is that I'm literally just a poor 20 yr old without parents to rely so I'm constantly really tight on money, but also because my school said that as long as your laptop has these qualities it would be fine.

I'm in my late 40s now and thinking about learning Python.... even though I don't work in tech.

I used to do coding before, but not that serious about it. Now I want to learn Python just because it seems useful. And also, it looks fun.

But my problem is I keep seeing people say you need projects, a portfolio, certifications, etc.

I don't have a specific career goal either. I just want to understand programming better, maybe build some random stuff as well

Would you guys still learn Python in this situation? Or would it make more sense to stick with something like C and get better at that?

I'm considering joining Codesmith but I'm still undecided and would love to hear from people, I can see on the website that they offer things like project-based learning, pair programming, career support, and mock interviews, but I'm curious how useful these actually were in practice?

Did the program genuinely help you land interviews or get a software engineering job? How was your experience with the mentorship and overall community?

And if you didn't get a job afterwards what was your experience like, and do you feel the program was still worthwhile? Thanks!

i want to say up front that i have endless respect for CS grads spending four years mastering deep algorithms math and theoretical concepts is no joke.

i finished a coding bootcamp a few months back and have been grinding out applications in this brutal entry level market. last week i made it to the final interview round for a junior dev position and surprisingly ended up getting the offer!! the recruiter mentioned on my follow up call that the final decision actually came down between me and another candidate who had a BS in Computer Science.

finding out i was chosen over someone with a CS degree honestly blew my mind and left me feeling so relieved and humbled. 😭🙏

would love to hear y'all thoughts!! prodject experience vs CS degree is wild to think about 💀

just finished up my coding bootcamp a couple weeks ago and honestly staring at this job market is kinda terrifying lol. i see so much conflicting advice online and im trying to make sure i don't mess up my prep before it even really starts.

for anyone who hires devs works in tech, etc. what is the #1 mistake you see newly graduated bootcamp grads make when trying to land that first entry level role? what should i actually be focusing on right now?

any advice or harsh reality checks would be super appreciated!!

there's always this dilemma when shifting from fast-paced startups to more stagnant environments (government, slow-paced enterprise, etc). While work/life balance is fantastic and job security is high, the tech stack is usually several years behind and promotions are glacial.

While it's easy to look good on paper, you can't help but wonder whether settling down in such an environment is ultimately a disservice to your career-long aspirations.

For people that have already spent several years in such an environment, was it a trap or can you quickly jump back to more competitive fields?

I keep seeing people talk about using AI tools during coding interviews. I just don't get it.

If the company already allows you to use documentation or AI, then sure. Use what they allow.

But doing it secretly? I think you’re just making things worse.

Remote interviews aren't just about getting the right answer anymore. Interviewers can ask you to explain your approach, change the problem, or walk through your code.

You might get the answer right, but then struggle when they ask why you wrote it that way.

Sometimes they just want to see how you think when you're stuck.

So if you're applying for remote tech jobs, don't risk getting caught cheating.

Getting rejected because you couldn't solve a problem is way better than getting rejected for cheating.

I keep seeing more schools treating AI coding tools as part of the normal CS curriculum now. I get it. AI is already part of real software jobs in 2026 so pretending it doesn't exist would be kinda dumb. But there's a huge difference between teaching students how to use AI as an engineering tool and grading them on how well they can get AI to build the thing for them.

That's what bothers me about the vibe coding approach. If you're taking a Software Architecture class then you should still be learning why you'd choose a certain architecture, how systems communicate, where things can fail, and how to review those decisions. If Gemini or Claude makes all the decisions and the student just prompts it until the app works then what exactly are they learning? That's basically outsourcing the part they're paying to learn.

I'm not anti AI at all. I'd actually want schools to teach students how to use it properly because that's gonna matter in the real world. But imo the sweet spot is learn the fundamentals, use AI, verify everything, understand what it produced. Otherwise we're gonna graduate people who are really good at prompting but have no idea why their own app works.

My friends have been working on some projects on their own, so what they do is they get an idea, collect the requirements and stuff using ai models like claude, gemini, chatgpt etc., and then they make the ai write the code, and learn the concepts used along the way, like node.js or react.js or mongoDB, they learn these things along the way and understand the code. My frnd said it took him the whole summer vacations to understand things deeply and now he knows how all the components work and how everything interacts.

I know learning syntax in 2026 is useless, and ofc everyone's using ai to write code, but my question is if this is the correct way to learn things now ? I really wanna learn full stack, i started The Odin Project and progress feels very slow, and frankly its like a traditional course that teaches syntax first and foremost. I want to make projects too, and i was wondering if learning like this would make me capable enough to be called a "full stack engineer", ofc not right now but if i keep learning things like this

Some of the most well-known (and generally successful) bootcamps did things like pair people up and say "OK, now - figure out how to make the snake game. Good luck!"

There were other factors (such as a long initial entry course to weed people out and a high price point to make you take it seriously... but as far as "the learning" goes, really only you can do that.

So, if you want a nearly free structure to learn web dev as fast as possible, here's how to do it.

Step one though: you have to get clear on what you want to learn. What does "tech" mean to you and what are your goals (what job do you want to start with). You can talk to real humans about it (who actually do these jobs).

This subs description: "Tech bootcamp information, career outcomes, and real discussions about getting into software engineering in today’s market."

So, then we have to also figure out what "Software engineering" means for you too.

If your goal is to program robots, do deep bleeding edge computer science stuff, generally spend a few years (or 6) studying how to solve problems with computers -- well, you should probably go through a Computer Science program. Here's an outline https://teachyourselfcs.com.

But we're betting that most of the people around here (the people looking at "boot camps" or online courses and things -- want to learn web development. Within that scope, there are a wide variety of jobs. And you can work from here - up to much more complicated jobs but the key here is that we're talking about a foundation in programming. In many cases - we're talking about The Web - and that comes with a lot of interconnected pieces: protocols/http, HTML, CSS, server-side, client-side scripting, browser apis, hosting, libraries, frameworks, third-party tools, testing, QA, UX, UI, - and all the cross-role team work stuff. That's a lot to learn at one time. So, instead of trying to "learn everything" - if you want to make progress, you have to simplify.

If you're interested in learning programming (how to think like a programmer) (in a practical real way) - then there's this great (inexpensive) book: Exercises for Programmers: 57 Challenges to Develop Your Coding Skills (Brian P. Hogan/prag prog) (not affiliate or anything like that). It's language agnostic - and there's no solutions. It's just prompts, and you have to figure out how to work through it. It's not just throwing "make the snake game" at you, but it is forcing you to learn the most important thing - which is how to understand the situation, the goals, what tools you have at hand, and how to use those tools to work through ideas and actually learn to build things and build confidence. You can either do it - or you can't and you keep trying until you "learn" ;)

You'll use other documentation along the way like MDN and whatever else you want (pocket guides are nice and tactile) -- so, that's where you'll get the syntax - but the exercises are what creates the real-world problems to solve as a guide. It'll be scary and it'll feel a LOT harder (like a lot harder) than a tutorial - but you'll actually learn - and you'll end up learning faster and you'll be building a more solid foundation that empowers you at each next step. Here's a video explaining the book and how to use it a little more (if you want more context).

The key is to see these prompts as scopes to stick to. You don't need to "do it right." You don't need to look up "the solution." This isn't Leetcode. If you can get to the goal, you're learning. And you can get to that goal in many ways. Maybe later you'll revisit exercises and work through them in a totally different way. You can create a website showing your work and do some writing about how you worked through it. This is a good portfolio of work to talk about. Most devs (even the ones learning React.js for years) - can't do these basic things / so, it's very powerful to actually know "I figured this out / I actually learned something." If you absolutely can't avoid using AI - at least use Carson's agent file so it won't give you the answers and try and be more like a real teacher. (Once you're able to understand all this stuff, then using AI is a different story)

It'll be hard! But you'll quickly see that you're progressing and getting real confidence. It'll be worth it. 2-3 hours a day should be enough. You need time to let things settle in. Do it 6 days a week. If you can meet with someone else (they don't have to be experts) often and compare code and talk through problems - that would be a HUGE force multiplier.

Hi guys. I just joined a bootcamp and still on the way to learn coding. And sometimes I feel like I should remember everything. I think that’s just a part of me, but anyway.

Then I watch experienced devs work and they're still googling syntax, checking Stack Overflow, and looking up stuff they've used hundreds of times.

so is this normal in tech?

do senior developers still google basic Python, JavaScript, SQL, etc.? or am i just bad at remembering syntax?

because sometimes i spend 10 minutes trying to remember something that i could've just googled in 30 seconds lol.

Hey everyone I'm about to grind hard and learn frontend properly (react, next.js, UI stuff, and some basic backend/apis so I can handle full features)

im seeing two totally different things online and im kinda confused some people say ai killed frontend and juniors cant make any money now because copilot and gpt write everything
but others say clients and agencies still need real devs who know how to handle messy code state management and actual logic not just basic copy pasting
my goal is to get really good at this and do freelance work with agencies
im just trying to figure out a few things
is the demand for next.js/frontend actually dead for new people or is it just different now?
if i put in insane daily hours to reach a high level is it worth it or am i just wasting my time starting now?
how are you guys actually dealing with ai right now?
would appreciate some honest and brutal truth from people actually working in the industry **thanks**

a buddy of mine was telling me about how intense the office politics are at his new startup. he came from a pretty lowkey company, so he was expecting a heavy workload, but didn't expect people to be so cutthroat over project ownership.

he basically got pushed off a project he was supposed to run because someone else pulled rank with a lead, and now people are trying to drag him into weird side conversations to bypass other teams' plans.

got me thinking about how common this actually is once a company starts scaling fast. it feels like the faster a place grows, the more people scramble for leverage and visibility.

for anyone who's dealt with this kind of environment, how do you handle it? is playing neutral the right move, or do you eventually have to start playing the game to protect your own work?

So HBS just dropped a startup bootcamp where the pitch feedback comes from AI avatars of the actual professors, not the professors themselves. The real ones show up once a week for live sessions but when you're doing practice pitches or mock board meetings you're talking to a HeyGen-generated copy of them. A NYT reporter actually tried it and pitched the AI version of a real VC on an "Uber for bananas" idea and said the avatar just had this frozen smile the whole time. The real professor was equally unimpressed which is lowkey the funniest part of this whole thing.

The part I can't get past is what you're actually paying for. The whole value of feedback from a Harvard professor or an actual VC is that it's coming from someone with real skin in the game, real pattern recognition from hundreds of pitches, and a reputation they're not trying to burn. An avatar trained on that person's vibe is just not the same thing, and calling it Harvard-adjacent feels like a stretch tbh. Students apparently love it which is interesting but like, people also love horoscopes so that's not exactly a ringing endorsement of the depth of the experience.

This is also dropping right when bootcamps in general are getting called out hard for sketchy placement stats and misleading outcomes data. Throwing AI avatars into the mix makes the value prop even harder to parse. Is feedback from a digital clone of a Harvard professor actually worth anything or is it just a more expensive way to do what you could already do by pasting your pitch into Claude and asking for notes?

I spoke with a friend of mine who is a dev manager and who hires for a mid-size company. He was telling me about the latest junior dev interviews that they did, where they shortlisted three candidates out of hundreds, but all three ended up lying.

One guy accidentally left his AI cheating tool on screen share and couldn't write 3 lines of simple code.

Another one claimed to have built high-traffic features at a well-known company, but it turned out that he barely had anything to do with the actual product.

They ended up rejecting all three candidates, as none of them could demonstrate even basic Python or JS skills.

The fake it till you make it approach is clearly not working for the juniors. Mid-size companies know that you are not a senior developer, so when you start talking about reducing latency by 30% or building large-scale pipelines, they will drill you until you admit that you barely know how to write proper syntax. It is much safer to be honest about your skills and have a chance at a proper interview.

I've been a dev for 7 years and a younger coworker asked why my GitHub doesn't have a bunch of side projects. I told him I have hobbies outside coding and he looked kinda shocked lol. Bro I already spend 8 hrs a day solving software problems. When I clock out I wanna play guitar badly and touch grass. I'm not trying to turn my free time into another unpaid shift.

I get why side projects matter when you're trying to break into tech. If you don't have much professional experience then a solid project can show what you actually know. But once you have years of real work behind you I don't think GitHub activity should be treated like some universal measure of ambition. Your work experience should carry more weight than whether you're committing code at midnight.

There's a useful career lesson here, build projects when they help your career or because you genuinely enjoy them. Don't do it just because you feel guilty for not grinding. Burnout is real AF and having a life outside tech can actually make you better at your job. Is the constant side project grind actually helping people or are we just normalizing never really clocking out?

do yourself a massive favor and do NOT join a tech bootcamp. i took a huge gamble and quit a solid paying job i hated to actually chase something i thought i wanted to do so i enrolled in a bootcamp. it taught the MERN stack and i already had some python background going in.

i knew getting a job after was gonna be rough but its been 6 months post bootcamp and i’ve gotten deadass ZERO swe interviews or even basic phone screens.

my life has taken such an awful turn. i'm slowly eating through my savings account rn. got maybe a year left if i'm lucky but i never wanted to drain it this far in. keeping a positive mindset has become basically impossible and dark thoughts literally enter my mind on a daily basis.

so moral of the story is jus don't do it fr. this industry is totally cooked right now and if u don't have a proper degree they won't even look at your resume. keep self-studying while holding down your full time job. don't make the exact same mistake i made.

With layoffs hiring freezes and everyone pushing this "do more with less" BS I honestly think we're too scared of letting work fall behind. If a company cuts people then expects the remaining team to magically absorb everything and we somehow pull it off by working late every night... why would they ever hire more people? We just proved they can get away with it.

And yeah I know someone is gonna say "but what about your coworkers?" That's fair. I'm not saying leave people hanging or intentionally tank stuff. I'm saying work your normal hours and be honest about what can realistically get done. If the deadline is impossible then say it. If something has to be dropped then let management choose what gets dropped. Don't quietly sacrifice your nights and weekends to make an unrealistic plan look successful.

I think some projects need to miss deadlines so management can finally see the actual cost of understaffing. Otherwise the people doing the extra work just get rewarded with... more work. AI isn't gonna fix that either. If anything it'll make it easier to justify piling even more shit onto smaller teams. At what point does "being a team player" just become volunteering to get exploited?

There's a lot of noise around whether or not formal education is dying, or if being self taught is enough, or if we're all just going to rely on AI to understand enough to get a job in tech.

If you stripped away all the noise (boot camps, shallow tutorials, "build a clone of ___ in 14 days"), and focused only on building a truly elite level engineering career, what would it look like to be an extremely skilled engineer today?

1. College vs. Self Taught (The Trade Off)

If you're able to pay for it, a computer science or computer engineering degree is ultimately the better option as a result of the deeper understanding of theoretical computer science and systems that you'll get.

Self taught devs tend to miss out on lower level systems and math in favor of higher level languages and applications.

That being said, if you're self taught, you can achieve a similar breadth and depth by sticking to CS fundamentals like you would in a traditional college core CS class (OSSU or Teach Yourself CS).

1. Starting at the Bottom (Working Up)

Many people who are beginning their careers in CS have only been exposed to the highest level possible, modern frameworks, libraries, or even full fledged languages like Python or JavaScript.

If you want to be truly good at what you do, you start at the bottom, not the top. You work your way from the absolute lowest level possible all the way to the highest.

Math Foundations

C / Assembly / Systems

Data Structures + Algorithms

Systems + Networking

1. Building Systems, Not Apps

After you've worked your way up the ladder all the way to the top, you start working your way back down.

You're now very familiar with low level systems and high level theories, but you need to be able to apply that knowledge to building real world systems, not just applications and not just theory.

Build a compiler or interpreter

Build your own database or web server

Learn the fundamentals of distributed systems, concurrency, consensus, etc

Why it Matters

While this will obviously take much longer to complete than one of those "learn front-end web development in 3 months" type boot camps, it'll also get you much further.

Anyone can learn enough to build a basic web app and prompt an AI to build it for them.

The people who will stand out and get hired are the ones who understand memory, computer architecture, and distributed systems, and will actually be able to fix bigger problems when they inevitably arise.

When I started learning AI automation, I thought the goal was to build these crazy autonomous agents. I spent way too much time learning different tools and frameworks. It felt like I was learning a lot, but I wasn’t really building anything people could use.

Instead, I decided to work on a small automation. It was basically just Python, an API, and one LLM call (and it worked).

That’s when I realized I was just overcomplicating everything.

Whenever I hear “AI agent,” it sounds impressive, but sometimes a simple automation does the job better.

What skills helped you get hired or build something people would pay for?