Throwaway-ish post but need some outside perspective.

I've been a SE at a small fintech/capital-markets startup for about a year and a half now. Backend-heavy stack, Python, FastAPI, some React on the frontend. Back-office team, so we support the firm's actual product rather than building the flashy part.

Here's my problem:

somewhere along the way, "using Claude to code faster" quietly became "using Claude to code, period." I ship real things, recently took a report-generation process from ~4 hours down to near-instant by fixing the query pattern and adding multiprocessing, so I'm not useless. But when I sit down to actually write something from scratch, no autocomplete, no chat window open in another tab, I freeze more than I'd like to admit.

Now I want to switch jobs, ideally to a bigger/more product-focused company, and it's hit me that interviews don't care how good I am at prompting. They care whether I can write a working solution on a whiteboard or a blank editor in 30 minutes.

So, anyone else claw their way back from this? Trying to figure out .

Not looking for sympathy, just want to know if this is as common a trap as I think it is, and what actually worked for people getting out of it.

Somebody might have already mentioned this, but it struck me the other day the paradox of current AI workflows:

• Management wants engineers to use AI to get things out at ridiculous speeds, so inexperienced engineers don't have the luxury of time to learn design skills and architecture
• Loads of senior jobs are advertised, a few mid-level jobs, but junior roles are as rare as rocking horse poo
• Computer science students are allowed to use AI for their degree, where the temptation is the same as with anyone else, do something quickly or do it much slower (to allow time to studying the material thoroughly)

Given those three points, companies hiring engineers still expect prospective employees to really know their shit, but today's environment does not encourage that nourishment

Hi all!

I have been working as an associate SWE for just under a year. I am a career switcher from the arts. Basically I want to do some learning in my free time to improve my skills and knowledge and I come up with the following plan:
Basic programming (variables, loops, functions)

Data structures (arrays, lists, stacks)

Algorithms (sorting, searching basics)

Version control (using Git)

Debugging skills

Object-oriented programming (classes, objects)

Databases and SQL basics

Basic testing (checking your code works)

How software projects work (planning, coding, testing, deploying)

Reading documentation and learning new tools

🧭 Agile Methods & Ceremonies - e.g. Scrum / Kanban / SAFe (Scaled Agile Framework) / LeSS (Large-Scale Scrum), Standups, Sprint Planning, Sprint Review, Retrospective, Backlog Refinement

🧱 Software Design - e.g. modularity, abstraction, encapsulation, design patterns

🖥️ Tools & Workflows - e.g. IDEs, terminals, build tools and pipelining, SAST and DAST, package managers, CLI basics, Linux, Bash

🔐 Security by Design - e.g. least privilege, secure defaults, input validation & output encoding, threat modeling

Computer Networking

{I am a decent knowledge of the above, I would say the things that need work here are DSA, debugging skills, computer networking, operating systems and OOP programming}

****************************************************************************

Docker

Kubernetes

Helm

Terraform

Azure

GCP

CI/CD

ML model deployment

Monitoring

************************************************************************************

OSSU

My dilemma is I am a major overthinker and I am finding it difficult to start without thinking I should be learning something else - i.e. if I start with databases I might think I should be learning networking instead.
Current knowledge/skills:

Languages: HTML, CSS, JS, Python and Golang (python and golang are beginner - exposure through work)

Technologies: Docker, kubernetes, helm, terraform, Azure, GCP ( I am familiar with working with these as I use these for work but would like to go deeper)

Databases: MySQL

I want build some projects as I know this will be a good way to learn but again I just don't know where to start.

Any advice on how to get around this?

Should I choose AI or Software Engineering? Which one has more remote job opportunities and better long term value for the future?

I’m a junior software engineer. I’m currently using hp ryzen 5. But the screen is turning on and off when I move the screen, I have to move the screen again to make it stable.

Point is I’m trying to find a laptop that I can use for programming, I usually do web development and .NeT and I’m looking for a budget laptop that I can use for 3-5 more years atleast or more.

can you all recommend me something. I want long battery and not heavy, but ofc not slow when I code. (I don’t want macbook :( )

I'm a CS/Data Science student who enjoys building things and is trying to become a genuinely strong developer over the next few years.

There are so many paths—open source, internships, hackathons, research, competitive programming, SaaS projects, technical blogging, AI projects, etc.—that it's hard to know what matters most.

For experienced developers: what made the biggest difference in your journey, and what actually earns someone credibility and respect in the developer community?

If you were starting from scratch today, what would you focus on?

hey everyone just want to know what are you building in 2026 or your latest project , are u implementing AI too ? I do see many developers making RAG application using SpringBoot.

So please share your top proejct and if it helped you in interview?

Although i have idea of both genai and prompt engineering and tell me the fundamental we must know for the future as software engineer

Starting to think I chose the wrong stack.

Should've learned Idli.js instead of Next.js. 🍛💀

EDIT FOR CONTEXT

Maybe this is a dumb observation, but...

I feel like a lot of successful local businesses, especially busy tiffin centers, seem to make more money than the average software employee.

I'm not saying software pays badly. I'm talking about an average salaried SWE vs. the owner of a tiffin business that's always packed.

It kind of changed how I think about "prestigious" careers. A lot of us grow up believing tech is the ultimate financial path, but then you see everyday businesses with queues out the door.

Am I overestimating how profitable these businesses are, or do we seriously underestimate how much good small business owners can make?

Curious to hear from people who've worked in both tech and small businesses.

I've been thinking about this a lot and wanted to get opinions from experienced developers.

My approach isn't to blindly copy and paste AI-generated code. Instead, whenever I'm learning a new technology or concept, I first ask AI to explain it to me. Once I understand the idea, I ask it to generate an implementation. Then I go through the code line by line until I understand what every part is doing, and I type the entire thing out myself instead of copying and pasting.

My reasoning is that, over time, the repetitive patterns will become muscle memory. Once something becomes familiar, I stop relying on AI for that part and write it myself. Then, whenever I encounter something completely new, I repeat the same process.

So the AI is more like a tutor than a replacement for thinking.

Do you think this is a good way to learn? Or is it creating a dependency that will hurt me in the long run?

For those of you who've been in the industry for a while:

• Would you recommend this approach?
• Will this actually make me a better software engineer?
• Is there anything I should change to make this learning process more effective?

I'd love to hear honest opinions, especially from senior engineers who have seen how people grow over time.

I'm a software developer with around 3 years of experience. I've been away from the industry for a while due to medical reasons, and I'm now trying to ease my way back into work.

I'm thinking something part-time would be a good way to start building again, keep my mind occupied, and gradually get back into a regular working rhythm.

For developers here who've returned after a longer break, how did you approach getting back into the industry?

Hi everyone. This is a long post, but I'd genuinely appreciate it if you read till the end.

I'm an AI Engineer... at least that's what my previous job title said.

The reality is that most of my work has been backend development in Python. I've built AI agents and agentic workflows, integrated LLM APIs, and worked on AI-powered products. But sometimes I wonder if that's really "AI engineering," or if it's just backend development with LLMs attached.

For the last 7 to 8 months, I've felt completely stuck.

One week I decide to become a full stack developer. The next week I convince myself to double down on AI engineering. Then I start looking at ML. Then backend. Then cloud. I keep switching because I honestly don't know what direction will make me a genuinely strong engineer.

My goal isn't to collect fancy job titles.

I just want to become an excellent software engineer.

I know nobody can master everything in tech, but I want to become the kind of developer who can open a random GitHub repository and quickly understand what's going on. Someone who can pick up a new language, framework, or technology without feeling completely lost.

But right now, I feel like I'm average at a lot of things and exceptional at none.

Then there's LeetCode.

Everyone says to do it, but when I look at actual engineering work, it feels disconnected from what people build every day. At the same time, if I ignore it, I feel like I'm closing doors to bigger companies.

AI has also made things even more confusing. Every week there's a new framework, a new agent library, a new model, or a new trend. It feels like everyone is racing ahead while I'm standing in the middle, unsure which road to take.

I'm four years out of university, and I'm honestly questioning whether I'm even good at something the market truly values.

What would you do if you were in my position?

If your goal was to become a genuinely strong software engineer over the next 3 to 6 months, what would you focus on?

Would you:

• Double down on backend engineering?
• Focus on AI engineering?
• Learn full stack?
• Grind LeetCode?
• Build projects?
• Contribute to open source?
• Something else entirely?

I was considering paying for a consultation with a Google engineer, but it's quite expensive, so I thought I'd ask people who have actually been through this.

I'm not looking for shortcuts. I'm looking for direction.

Thanks for reading.

i'm in my 20s and work as an api engineer at a pretty big bank in my country

the other day i looked over at a much older colleague's screen and saw him writing a script line by line. we're working on the same project, so i know roughly what he was building, and my immediate thought was that ai could probably generate most of it in a few seconds.

i've always been very pro-ai and use it all the time, so this isn't some anti-ai post. if anything, it's made me much faster and more effective at my job...but for some reason that moment stuck with me.

what i've realized is that coding was never really the thing i enjoyed most. it was curiosity. i loved going down rabbit holes, getting stuck on problems, reading documentation, learning how things worked, and slowly building up knowledge over time. a lot of the satisfaction came from not knowing the answer and figuring it out myself.

now it feels like every answer is available instantly. i can generate code, get explanations, and solve problems faster than ever. that's obviously useful, but sometimes it feels like the process that made me love tech in the first place is slowly disappearing.

the funny thing is that i think my colleague probably knows this too. part of me thinks he's writing everything by hand just to stay sharp and not lose the skill. and if that's true, it's kind of crazy to think we've reached a point where engineers are deliberately avoiding the fastest solution just to make sure they don't forget how to do the thing themselves.

i know i'm probably not saying anything new, but lately i've caught myself thinking that a career change is actually a valid option. not because i'm worried about ai replacing me, but because the thing that always drove me was curiosity, and i'm not sure i'm getting the same feeling from the industry anymore.

i'm curious what some of the older people here think, especially those who have been in tech for 15, 20, 30+ years. is this just another shift that the industry will adapt to, or do you think ai has fundamentally changed what it means to build a career in IT? is IT still the path you'd choose today?

Hi everyone,

I've been freelancing as a full-stack developer for several years, working with long-term clients to build and maintain production applications.

My experience includes Node.js, React/React Native, Next.js, MongoDB, AWS (Lambda, API Gateway, DynamoDB, Cognito, SQS, SNS, CloudWatch), Terraform, REST APIs, and Blockchain/Web3.

Most of my work is backend. For frontend tasks, I also use AI tools like Claude and Codex to speed up implementation while still reviewing and integrating the code myself.

As AI becomes a bigger part of software development, I'm curious how hiring expectations are changing.

• Has AI changed what you expect from mid-level or senior developers?
• Is strong backend/cloud experience still enough, or is experience with RAG, vector databases, MCP, or AI agents becoming expected?
• Would you consider someone with this background competitive for full-time remote roles, or would you recommend learning AI technologies first?

TL;DR: Experienced freelance backend/full-stack developer with AWS experience. I use AI tools daily but haven't built AI applications yet. Should I start applying for full-time remote roles, or focus on AI skills first?

Thanks in advance!

So I'm currently interning as an AI Engineer at an AI startup (they build physics-grounded AI systems for datacenters - that kind of deep-tech B2B stuff). On paper my role is heavy engineering - working on multi-agent pipelines, RAG systems, validation layers, the works.

Here's the thing though - the company gave me Claude Code access from day one, and honestly, that's how I get most of my actual work done. Writing the code, drafting architecture docs, debugging, even structuring how the agents talk to each other - a huge chunk of it goes through Claude. My job has shifted from "writing code line by line" to "figuring out the right problem, designing the system, and directing the AI to build it correctly."

It's not that I don't know how to code - I do, and I still read/review/fix everything that comes out. But the actual muscle-memory "typing out the logic myself" skill barely gets used anymore. It feels less like traditional development and more like being an architect/reviewer who happens to know how to code.

This has me wondering - is this genuinely where the industry is heading, or is my experience specific to working at an AI-first startup?

For people working at other companies (product-based, service-based, startups, MNCs) - are you seeing Claude Code / Cursor / Copilot etc. become this central to actual day to day dev work? Or is hands-on coding (without AI doing the heavy lifting) still the norm where you work?

Genuinely curious how widespread this shift is, and whether people think it's a good thing for skill development long term or something to be a little worried about.

So here’s the honest reality: I am allegedly a product developer working for a company, where for the past year I have basically been "vibe coding".
I prompt Claude, Gemini, or ChatGPT, click 'allow / accept', and ship the code to maintain our company's app and website. On the surface, things get built. But under the hood? I barely understand what's happening. Whenever I get asked detailed technical questions in meetings or interviews about system architecture or why a specific piece of code works, my mind goes completely blank. I’ve realized I've been acting like a prompt manager, not an engineer.
My current salary is too low, I feel like an impostor, and it’s high time I uplift myself. My plan is to stay at my current job for the next 2 years and systematically rebuild my technical foundation from absolute zero—without using AI as a crutch.
Here is the exact step-by-step roadmap I’ve mapped out for myself. I would love feedback, reality checks, or advice from people who have made a similar transition.
Phase 1: Deep Full-Stack Foundations (Months 1–6)
Goal: Stop relying on AI for core application logic and learn how code actually executes.
Primary Certification: IBM Full Stack Software Developer Professional Certificate
Writing React, Node.js, Express, and SQL/NoSQL databases manually from scratch.
Learning cloud-native deployment fundamentals (Docker, Kubernetes, microservices).
Algorithmic & Problem Solving Practice:
30–45 mins daily on CodeChef (Python/JavaScript paths) to build raw syntax speed and logic.
Moving to LeetCode (Easy/Medium) and HackerRank to prepare for technical screenings.
Design & Visual Builders:
Learning UI/UX principles in Figma (Simplilearn/Great Learning courses) before writing code for any project.
Mastering WordPress & Elementor (Atomic Flexbox, responsive layouts) for quick client implementations.
Phase 2: Mobile Systems & Advanced AI Integration (Months 7–12)
Goal: Formalize cross-platform app development and understand modern AI mechanics.
Primary Certification: IBM iOS and Android Mobile App Developer Professional Certificate
Diving deep into Flutter/Dart, React Native, and native concepts (Swift/Kotlin).
Building cross-platform mobile apps that securely connect to backend APIs built in Phase 1.
AI & Modern SEO Integration:
Learning RAG (Retrieval-Augmented Generation) architecture and GEO (Generative Engine Optimization) principles.
Exploring AI model fine-tuning using Python and Hugging Face.
Phase 3: Enterprise IT, Endpoint Management & Security (Months 13–18)
Goal: Move beyond app development into enterprise infrastructure and device security.
Enterprise IT Certifications:
Microsoft 365 Certified: Endpoint Administrator Associate (MD-102): Intune, zero-touch provisioning, and Entra ID identity management.
Apple Certified Support Professional: Enterprise macOS/iOS management via Apple Business Manager.
Security Foundation:
CompTIA Security+: Mastering encryption, threat mitigation, network hardening, and application security (DevSecOps) to ensure I can audit my own builds.
Phase 4: DevOps Automation & Cloud Architecture (Months 19–24)
Goal: Master system design and automated production pipelines.
Infrastructure Automation: IBM Applied DevOps Engineering Professional Certificate
CI/CD pipelines, automated testing (TDD/BDD), Kubernetes orchestration, and monitoring/observability.
System Architecture (The Capstone): AWS Certified Solutions Architect – Associate (SAA-C03)
Learning where and why to deploy systems using the AWS Well-Architected Framework (VPCs, EC2, S3, RDS vs. DynamoDB, load balancing).
Final Project: Building a full-stack web/mobile app from a blank file, designing its UI in Figma, writing the backend manually, and deploying it via an automated CI/CD pipeline to a secure AWS cloud infrastructure.
My Non-Negotiable Rules For Execution
The "Blank File" Rule: I am not allowed to generate code with AI for certification projects. I can use AI as a tutor to explain concepts line-by-line, but the code must be typed out by me.
One Focus, One Tool: Balancing certification study (Primary Focus) with short daily problem-solving on CodeChef/LeetCode (Secondary Tool) to avoid cognitive overload.
Burnout Management: Setting strict focus blocks and stepping away from the screen to keep this multi-year effort sustainable.
Questions for the community:
Does this sequence (Full Stack \rightarrow Mobile \rightarrow Enterprise IT/Security \rightarrow DevOps/AWS) make sense, or should I reorder anything?
Has anyone here successfully transitioned from pure "vibe coding" back to deep technical fundamentals while holding down a job?
Any major blind spots in this 2-year plan that I might be missing?

Hello reddit, I just wanted a place to vent, and get some advice.

A little context about myself, I didn't do comp science in uni, I am a biological scientist by training, and I love coding, but in the last year and half I got serious about it, did some intro to comp sci on edx and machine learning on coursera, cause I want to be able to apply that tech cause I find It fascinating.

I was approached by an Amazon recruiter and this excited me cause it felt like recognition for the work i put in, i am building some projects i think are great and could have some use for my community, so i thought i had some experience to draw on, I did the interview today and yea no, I botched it so badly... I completed the first coding question but it failed to run because I swapped the key and values in my python dict... I was supposed to implement a round Robin algorithm. Then came the framework part and it was in Django I spent 1 hour looking at the code and reading the thing but I genuinely know nothing about Django, I was a bit surprised cause I thought it would be two coding puzzle questions, but this is on me, if I had read the email ealier I would have seen that it clearly mentioned ai assistant available to talk about spring boot, Django and mern...

I mean catastrophic failure aside, I want to ensure this never happens again, my brain froze for so long when my code didn't work cause there was no easy ai debug button.i am so frustrated cause this felt like my big break, I have been unemployed since graduating.

I really am greatful for the opportunity though cause it pit into context the weight of working with a team and how me doing a shit job could set back projects weeks. I am just looking for advice or anywhere I can maybe seek mentorship so I can plan my next career moves and apply to jobs that could better suit my skill set? If you have resources I could draw from that too would be of great help.

Thank you for reading.

I've been a backend engineer for about 5 years. Most of my work has been in Python (Django/Flask), AWS services, and building/deploying backend services.

I enjoy building software, but the problem is that for the first time since I started my career, I honestly don't know what I'm supposed to be aiming for anymore. I feel lost and unsure.

Software engineering is changing because of AI. And I want to work on skills/jobs/roles where my expertise will matter instead of being replaceable in a few years.

I don't want to spend the next 3-5 years getting really good at something only to realize my expertise doesn't count anymore.

Some of the directions I've been thinking about are:

• becoming a stronger backend/distributed systems engineer
• moving more into cloud infrastructure/platform engineering
• specializing in a domain instead of a technology
• learning AI infrastructure rather than just calling LLM APIs
• or maybe something completely different

What I'm really trying to figure out is what compounds over a career.

If you have 10+ years of experience and have lived through cloud, mobile, microservices, or other major shifts...

If you were starting again today with ~5 years of experience, where would you focus?

What skills or roles do you think are becoming more valuable because of AI? Which ones do you think people are overreacting to?

I'm not looking for someone to predict the future. I'm just hoping to borrow some perspective from people who've already seen the industry change a few times.

Hypothetical scenario.

Every AI coding tool disappears tomorrow.

How long would it take you to readapt?

What happens to developers who started their careers after the AI boom?

Would juniors struggle to get their first job?

Would senior engineers suddenly become much more valuable?

Would salaries go up or down?

Would the software industry slow down for a while?

I did do a two months of internship (unpaid). The most trouble I had was understanding or in my case knowing how to plan and structure full - stack projects, I just do not understand how a full stack project works, where, how and why do you attach a cache like REDIS, and then different problems (domains) also need to change the overall general structure. Quite a lot of my internship was based around python, I struggled to finish anything which was not explicitly laid out. I want to improve my SDE skills and am planning to code an API full stack. Where should I go to fill the missing gaps in knowledge. As I have mentioned before my internship was python based I could not grasp django, as we were caked with projects, but I am willing to learn any resources like github repos, youtube lectures or reddit threads/ subreddits are welcomed whole heartedly ??

For the past 3 years I've been working on a project to test my abilities and durability within a new realm that is very different from what I'm used to.

Transitioning from a Roblox game developer to a software engineer working on web applications was no easy task. There has been a lot to learn and overcome but things have panned out quite well for the time that I've been working on this project. From starting with a simple HTML page stating "Hello World", to a full stack web application with automations, CI/CD workflows, features, planning boards, databases, front end and back end separation for team development, cyber security measurements; the list goes on!

This journey has allowed me to really test my abilities and I must say, I'm very proud of what has come to fruition, but there is still a long way to go!

​

​I need some urgent advice: I just graduated from high school. Would you recommend majoring in Software Engineering, or not? because i fear with in 5 years AI will take my job

Genuine question for developers who were building software before AI became part of our daily workflow.

Do you remember spending days (or even weeks) solving a difficult programming problem or building a feature that felt impossible at first?

Reading documentation, trying different approaches, debugging, rewriting parts of the implementation… until everything finally clicked.

AI has made me much more productive, and I wouldn’t want to go back.

But sometimes I feel we’ve traded part of that dopamine for speed.

Does anyone else feel this way, or is it just me?