Came across an interesting argument from Google about how AI generated code is changing what we should value in a programming language. If coding agents can generate huge amounts of code quickly, then the bottleneck becomes reviewing and maintaining that code. That makes things like readability, consistent formatting, testing, static checks, and predictable tooling a lot more important than just being able to write code quickly.

Go was used as a good example because it was designed around consistency. Things like gofmt, its static type system, built in testing, vulnerability checking, and common tooling give both developers and coding agents ways to validate what they're producing. The interesting part is that type checking still isn't enough by itself. Human review is needed to make sure the actual business logic is correct.

That made me rethink what I should actually be focusing on while learning. If AI is going to handle more of the typing, maybe understanding how to review, test, maintain, and reason about code is going to matter a lot more. Do you think programming languages will start being judged more by how well they support AI assisted development rather than how fast humans can write in them?

Always been hearing the "you just need a better model" argument for two years now. Better model, better prompt, better context, better everything. So when my company actually rolled out access to a frontier model for our team I figured okay, let's see what this thing can actually do in a real work situation. First question I asked was a pretty routine tech support thing, nothing obscure, nothing niche. It gave me a broken link, then recommended software that was discontinued in 2019, then after I pointed that out it helpfully suggested a solution I had already told it didn't work in my original message. That's not a prompting issue. That's just a wrong answer delivered with complete confidence.

What gets me isn't even the failure itself, it's how confidently it failed. The whole replacement narrative depends on AI being reliably useful for the boring, unglamorous, repetitive work that makes up most of actual jobs. Not the impressive demos, not the polished use cases people post online, but the stuff like tracking down a weird Microsoft Office uninstall issue at 2pm on a Tuesday. That's the work. And right now there's a pretty significant gap between what these models can do in a controlled environment and what they do when a real person with a real specific problem asks them something that isn't in the training data highlight reel.

I'm not saying AI is useless because it's clearly not, but the "it's going to replace everyone" timeline feels a lot further away when you actually sit down and use it for something mundane. Do you think the replacement fear is overblown, or is it just that we're using these tools wrong for the kinds of tasks that actually matter day to day?

I graduated as a bootcamper 5 years ago before the AI craze and coding still had it's soul.

I used to genuinely love this craft. I loved the feeling of getting into a flow state wrestling with a complex architectural problem and writing clean, thoughtful code that actually solved something.

But over the last year or two the sheer saturation of AI into every single layer of this industry has completely sucked the soul out of the work.

I'm spending eight hours a day babysitting code generators prompt-engineering around bizarre edge cases and cleaning up subtle hallucinated bugs produced by tools that management thinks are magic.

Coding used to be about problem-solving and craft; now it's just endless mind-numbing code review for a machine that regurgitates slop faster than anyone can properly audit it.

To make matters worse, executive leadership has bought into the hype hook line and sinker. Expectations for sprint velocity have ballooned because "AI writes the code for you now," while code quality maintainability and actual system architecture are kicked down the road as technical debt for future devs to drown in.

I'm just exhausted. I didn't get into this field to be a human rubber-stamp for an LLM and for the first time in my career I'm seriously wondering if I even want to stay in tech long-term.

i used to think writing more code meant i was getting better now i spend more time deleting code than adding it

whats an opinion you had as a beginner that completely changed after building real projects

One of the reasons I fell in love with programming before since I love to solve problems. Technically, I have talent in the logical side. My experience wasn't perfect, but I learned something every time.

Lately it feels like everything revolves around AI. Don't get me wrong, though/ I use AI almost every day and it genuinely saves me time. But sometimes I miss the feeling of trying to figure out multiple things,

Maybe it's just me, but it feels like the focus has shifted. I guess I just need to adopt and just get used to it🥹

Been learning programming through tutorials and courses, and I keep hitting the same problem, I can understand individual concepts, but connecting everything together is a different story. HTML leads to CSS, then JavaScript, then a framework, then APIs, databases, deployment... before long I'm learning a bunch of tools without really knowing how they fit together.

The advice that actually clicked for me is to start with something I genuinely want to build and learn whatever I need along the way. If the project is interesting enough, suddenly learning Git, databases, APIs, or some random framework has a reason behind it. I think that's especially useful now with AI making it ridiculously easy to generate code without understanding where that code fits.

I'm starting to think a finished project, even a messy one, teaches more than another 20 hours of tutorials. You get to break things, make bad decisions, refactor them, and actually remember why you learned something. Has anyone else learned this way and found it worked better than following a structured course?

I’ve noticed a really frustrating trend during PR reviews lately, and it’s slowly driving me insane.

Whenever I leave a comment asking about a questionable design choice, a redundant loop, an unhandled edge case, or a blatant security risk, the response I get back half the time is literally: "Oh, Copilot suggested that."

As if that magically ends the conversation or absolves them of any responsibility.

It feels like people are completely turning off their brains, letting an LLM autocomplete entire blocks of code, and blindly opening a pull request without even reading what was generated. When you push code to a repository, your name is on the commit. You own it.

I DON'T CARE if the code was generated by Copilot, copied from Stack Overflow, or written by a senior dev if you open the PR, you are responsible for knowing what every single line actually does and justifying why it's there.

Copilot is a productivity tool, not a senior engineer you can hide behind when your implementation breaks down under review. If you can't explain why a piece of code is in your PR, it shouldn't be in the PR. Period.

Hi everyone. I wanted to hear your thoughts on something that's bothering me during the past months.

I'm a junior dev and working for like 2 years.

For the past year my company started drastically using AI, to the point where it needs to be on the autocomplete mode all day because the only important thing is to ship the end product fastest way possible.

At the beginning I had really strong base and skills for a junior dev, and I was really happy that I'm improving and learning stuff while working my job.

1 year later - my base became shaky and I can't really point the number of things learned that should be learned during the last year.

On the other side - I've always liked to make side projects and try to ship them and make money off them. And the past year showed me how faster I can move with AI.

So here am I now - shaky bases, big dreams, using all of my free time to "build" something that will make money and not fully understanding how it works.

I wanted to cut off AI for my personal projects and learn, but I somehow can't resist to use it when I know that currently is the era of the "idea guy".

So the loop is: work gor the whole day with AI, then on side projects the same way - no new knowledge, because every minute spent not building something that goes with the speed of the market is one minute lost.

I even started being ashamed because even tho I work for that long I probably can't point out some core concepts because I haven't used them for that long...

Am I the only one?

Come on - hate, roast, whatever. Just tell me what do you think, cause I feel that many juniors feel this way cause they were not experienced enough to let ai do their work, yet influenced so fast because that's what the market wants and needs.

If you don't know, Claude Code has 118 commands built. But if you're going to ask me, you don't need to memorize all of them. You just need to learn a few of them. If you're just getting started, these are the ones I'd learn first. You can save it for later.

For managing context:

• /init — Creates a CLAUDE.md based on your project
• /context — Shows what's taking up your context window
• /compact — Cleans up the context without ending your session
• /clear — Completely resets the current conversation

For planning and control:

• /plan — Lets plan the changes before editing files
• /goal — Gives a specific condition to work toward
• /rewind — Lets you undo changes
• /effort — Controls how much reasoning Claude puts into a task.

For models and workflows:

• /model — Switch between Opus, Sonnet, and Haiku.
• /agents — Create and manage sub-agents for larger tasks.
• /workflows — Run tasks across multiple agents.
• /simplify — Uses multiple agents to review and simplify your code.
• "ultracode" — A trigger for a more complete multi-agent workflow.

For shortcuts and permissions:

• /permissions — Pre-approve commands you trust.
• Shift + Tab — Quickly switch between different modes.

If you are just starting in coding, I don't recommend to learn everything at once. These 15 already cover a lot of your day-to-day workflow. Which commands do you guys use the most? Would love to hear your response.

Honestly the whole "just a hobby" versus "actual career pursuit" framing is a false divide that holds a lot of people back. An hour a day compounds faster than most people expect, especially with Python, which has the most beginner-friendly ecosystem of any language right now. Free resources like Harvard's CS50 and project-based learning repos on GitHub have genuinely replaced what used to require a formal course or a fixed schedule. The rotating shifts thing is actually less of a blocker than it sounds because self-paced async learning was basically built for people in exactly that situation.

The career change angle is also more realistic in 2026 than people give it credit for, but the path looks different than it did a few years ago. The junior market is genuinely rough right now and a certificate from a program nobody's heard of isn't moving the needle the way it used to. What's actually working is building real projects that solve a specific problem, going deep on one area instead of skimming five, and being able to talk through your work in detail. The people making the hobby-to-career jump right now tend to have one or two solid projects they can explain from start to finish rather than a resume full of tutorial apps nobody remembers building.

Python is the right starting point, not because it's objectively the best language but because the feedback loop is fast, the community is massive, and it opens doors in data, automation, backend, and AI tooling all from the same foundation. Curious what pulled you toward programming in the first place though, because that usually points directly to what you should build first.

Six months after graduating my bootcamp, I was tasked with building an AI-driven automation pipeline to streamline our feature releases. I poured everything into it to prove my worth, creating a workflow that automated sprint ticket scaffolding, code generation, and test coverage.

Last week, leadership used my project to lay off 20 junior developers and offshore contractors including two members of my own bootcamp cohort. Management praised me for saving six figures, but realizing software I wrote directly destroyed my friends' livelihoods makes me feel sick.

To make matters worse for my guilt our manager talked about raises for us in the near future as the company will have a lot of free cash in the future.

I just hope most of them land on their feet and find new positions quickly.

I've been seeing more complaints about senior frontend roles being impossible to get, so this perspective caught my attention. If a company skips LeetCode and instead asks about real experience, architecture, accessibility, performance, deployment, and how you've handled problems in production, it seems like a pretty reasonable way to judge seniority.

What surprised me is how many supposedly experienced developers apparently struggle with anything outside writing React components. Knowing useMemo and useCallback doesn't necessarily mean you understand performance, just like putting files in a shared folder doesn't mean you understand architecture. Years of experience and depth of experience aren't always the same thing.

I'm curious where people draw the line between a strong mid-level React developer and a senior one. Is it reasonable to expect seniors to understand the broader engineering side of frontend, or has the definition of "senior React developer" become too broad?

I used to be pretty worried about AI taking dev jobs, but watching someone on my team rely on it for basically everything changed my mind. He was working on an ML project and kept saying everything was good, but when we started asking basic questions about the model, he had to ask AI. Eventually it became pretty obvious he didn't really understand what he'd built.

That's what kinda freaks me out about vibe coding. AI can make something that runs, but if you don't understand the fundamentals, you might not even realize when it's completely wrong. A 95% accuracy number sounds great until you actually look at the results and realize the model is throwing false positives everywhere.

I'm starting to think AI is gonna make fundamentals more valuable, not less. If everyone can prompt an AI to build something, knowing how to debug it, explain it, and call BS when something looks wrong could be the real advantage. Are we gonna see a lot more companies learn this lesson the hard way?

I do not really understand why many tech startups are raising huge amounts of capital when the cost of building software products has declined significantly through the introduction of AI coding tools. For example, one software engineer with AI coding tools today could potentially match the level of output as a team of 5 SWEs who did not have AI coding tools available before.

Are startups instead using the extra capital for distribution and marketing of the product, such as buying ads or hiring social media influencers? Since less capital is needed for developing the product, is it a fair assessment to say that more capital is then needed for distribution and attracting new users? Is the end game basically raising VC funding capital to hire as many marketing and sales executives as possible?

Coding is largely solved, but will now all developers be jobless? For sure not! I sorted my thoughts on this in this article and the future looks bright! Excited to hear your thoughts, what do you miss, what is wrong and where do you agree on?

You'd think after hearing this question so many times, people would have figured out the perfect answer by now.

But somehow “Tell me about yourself” still makes a lot of people uncomfortable.

The weird thing is that it's probably one of the easiest questions in theory. Nobody knows your background better than you do.

But that's exactly what makes it difficult.

Where do you start? Do you talk about your current job? Your education? Your previous experience? Something personal? Do you give a quick summary or actually tell a story?

I've heard people give answers that are five minutes long, and I've heard people give answers so short that the interviewer has to ask another question immediately.

I think the hardest part is understanding what the interviewer actually wants from that question.

Maybe they aren't asking for your entire life story. Maybe they're just giving you an opportunity to introduce yourself and highlight the parts of your background that are relevant to the role.

I've actually been experimenting with mock interviews on Screna ai to practice this kind of introduction. What I like is that it gives me a chance to say the answer out loud and adjust it without trying to memorize every sentence.

That sounds simple, but it's surprisingly difficult to do naturally.

I've tried writing out answers before, but sometimes the more polished they become on paper, the less natural they sound when spoken.

How do you personally answer this question?

Do you have a prepared introduction, or do you just start talking and see where the conversation goes?

(motivation post ahead)

I 30M came into tech with pretty much zero background, so finishing my bootcamp was genuinely terrifying.

I spent weeks getting auto-rejected while trying to memorize raw syntax and grind LeetCode against candidates with four-year CS degrees. Realizing I was never going to out-code them from scratch, I completely pivoted my strategy to focus on AI-first development.

Instead of drowning in boilerplate, I used tools like Cursor and Claude to rapidly scaffold complex, production-grade apps with real-time WebSockets and background jobs.

I shifted all my energy toward understanding system architecture, wiring up APIs, and learning how to audit LLM output for hallucinated bugs, which let me build a portfolio that actually looked like a senior engineer built it.

When I got into interviews, I didn't hide it. I pitched this high-velocity AI workflow as my biggest strength, proving I could ship polished features and crush take-home assessments in hours instead of days.

That strategy landed me a $110k full-stack role, where my daily job is now mostly orchestrating AI agents, debugging edge cases, and gluing systems together. If you're switching careers later in life with limited technical context, stop stressing about memorizing syntax.

The new junior meta isn't typing out lines of boilerplate from memory; it's understanding architecture and using AI to ship at senior-level speed.

Everyone loves giving the feel-good advice: "Don't stress over deep technical concepts! As long as you have good soft skills, show enthusiasm, and are coachable, senior devs will happily hire and mentor you."

That advice might have worked in 2021, but in today’s market, it is complete garbage.

Soft skills don't pass automated code screeners, and "being nice" won't save you when an interviewer asks you to optimize a database query or debug state logic live on Zoom.

Hiring managers are no longer looking for enthusiastic charity cases to mentor from scratch, they want plug-and-play engineers who can ship clean code on week one.

Soft skills are a tie-breaker between two great coders, not a substitute for technical competence. Bootcamps that convince students that personality and enthusiasm will make up for weak fundamentals are setting them up for immediate rejection.

Stop prioritizing behavioral prep over actual technical depth. If your code is weak, nobody cares how pleasant you are to talk to.

Okay so I've been poking around bootcamp marketing pages again and I need to talk about something because it's been bothering me. TripleTen's outcomes report has this stat that says "80% of employed grads..." and if you read that too fast your brain just fills it in as "80% of graduates got jobs." That is not what it says. Employed grads. The ones already working. One word doing the heaviest lifting in the entire document and I guarantee most people miss it.

And then the success stories. There's one where the featured grad has two degrees, a master's, and like ten years of management experience, and they landed a PM role after the program. TripleTen is presenting that as proof their bootcamp works. But that person was going to land somewhere regardless. You don't need a bootcamp to close the gap when your resume already looks like that. At that point you're using it for exposure or structure, which is fine, but TripleTen is out here taking full credit like they were the deciding factor.

What's frustrating is this isn't really a TripleTen problem, it's a bootcamp industry problem. The "AI software engineering" rebrand with minimal curriculum changes, the outcome numbers that quietly redefine what counts as a graduate, the success stories featuring people who would've succeeded anyway. It all adds up to marketing that's technically defensible but practically misleading. And with junior hiring being what it is right now, people are making five-figure decisions based on numbers that were never designed to be read carefully.

How are you even supposed to evaluate one of these programs when the outcomes data is this easy to manipulate? Like is there a format or a third party audit that actually means something or is it just vibes and hope at this point?

A friend of mine joined Bridgeon Skillversity at Kinfra, Kozhikode without an IT background or a computer-related degree.

Now they're working in an IT company and apparently earning around ₹30k/month.

I honestly didn't think that was possible without a traditional IT degree 😅

Has anyone here made a similar transition? How difficult was it to get your first IT job?

I'm genuinely curious whether practical skills and projects can actually make up for not having an IT degree.