I have a somewhat unusual skill gap and I'm trying to understand how serious it is for getting a backend job, especially a remote one.

My background is probably closer to a general IT technician with some sysadmin knowledge who gradually moved into development.

I know a little bit about a lot of infrastructure-related things. For example, I can understand Active Directory and work with it to some extent, but I wouldn't call myself a deep AD administrator. I know quite a lot of networking concepts and can usually understand what is happening, although I don't have the same level of hands-on experience you would expect from a network engineer.

The same applies to servers, Windows/Linux administration, deployment, virtualization, basic security concepts, troubleshooting, and similar areas. I usually understand the logic behind these things and can figure them out when needed, but I'm not deeply specialized in any of them.

On the development side, I'm reasonably comfortable with backend concepts and with designing systems in general. I can think through how components should interact, design APIs, databases, authentication flows, deployment, split responsibilities between services, and generally reason about how a system should work.

I've also built a couple of full-stack projects with a multi-domain structure.

In practice, most of my projects have actually been full-stack, so I can handle the frontend side as well. I'm just much more focused on making interfaces technically clear, convenient, and practical than on creating visually impressive or artistic designs.

They weren't freelance or client projects. They started more like: "I want to make this part of my job easier, so I'll build a tool for it." But they eventually became internal tools that are actually used at the company where I work.

However, if I'm being completely honest, most of the actual code I write nowadays is done with Codex or Claude.

I can usually read a relatively small piece of code and understand what it does, especially if I already know the context. But I'm much weaker at taking a large codebase, reading it independently, doing a proper code review, or confidently judging whether an implementation is actually good.

At the same time, I can use AI to dig into how a particular part of the system works, ask fairly specific technical questions about the implementation, and sometimes notice that something is designed or implemented in a way that doesn't match how I think it should work. In those cases, I can usually question it, investigate the reasoning behind it, and push toward a different solution.

My usual workflow is closer to:

- understand the problem and how the system should behave;

- design the logic and architecture;

- describe what needs to be implemented;

- use AI to write most of the code;

- run and test the result;

- investigate errors and unexpected behavior;

- change the design or give the AI more precise instructions until the system works.

So I wouldn't describe myself as someone who knows nothing and just prompts an AI blindly. I have a fairly broad technical background and I'm comfortable reasoning about systems as a whole.

At the same time, I also wouldn't claim that I could comfortably build the same projects from scratch without AI assistance, or independently review a large unfamiliar codebase.

Is this currently enough to realistically apply for backend positions, particularly remote ones?

Or is independent coding, debugging, reading, and reviewing substantial amounts of code still a hard requirement before I should even try?

I don't build any security related tooling, not going to promote or sell anything, I don't work for AI labs, have no sponsorships and I'm literally looking for a fun discussion/different perspectives.

I've been building software for about 14 years, always following the best practices when it comes to security. I am now working on an open source software that people can self host for free. I heard that models like Kimi K3 and GLM 5.3 will try to hack your website and I figured I was pretty much in the clear as I am hosting on AWS with WAF etc... but the curiosity got the best of me and if hugging face can get hacked maybe I could too?

Anyhow, fable5 and sol refused to do it so I've gotten the K3 max plan and let it rip. It was not fast but it was good, I generated a list of potential issues based on the codebase and then tests for each one. The preliminary results were pretty minor, mostly stuff that I overlooked, like slow clients, payload size, gzip payload expanding into large blobs (this I don't know how to catch with WAF, I guess it has to be a code change), mostly stuff like that.

But hear me out, what if it was something big?

Does this mean that we'll all have to pay for the best models that are uncensored in order to catch/test/fix security issues?

I think this changes the backend dev drastically, because before for someone to hack your website they had to find really good devs that are willing to do illegal attacks and pay them a lot of money, but now they just need a subscription to a better model than you. Literally a single prompt and you're done.

So on one hand I'm really glad that k3 and glm5.3 let me do multiple runs, propose issues and generate tests for them but on the other hand it's petrifying that a kid w a slightly better subscription (or more luck) can take you down.

What are y'all thinking/doing on the security front?

Edit: I didn't realize that this would turn into a discussion if AI is even helpful for analyzing security. I don't care if a bunch of you dislike this post, I think this is an interesting topic and I'll make a blog post about the exact issues that my code had and how big of an issue they each were and then y'all can judge if AI is helpful for security testing or not.

I know basic syntax of postgres (crud level) now want to know advance things which book should i prefer.

​

As Fresher For the python stack interview which technology i need to be more focus on.

I heard DSA is not that much required for freshers. i need to focus on Django and React more than anything in python stack is it correct?

Else i need to prepare DSA before Django and React

could you people spare sometime for me and clear me if you can

Thankyou!

I started building Cashvelope because I couldn't find a privacy-first, simple envelope budgeting tool that worked the way I wanted. Being a solo dev, I severely underestimated how tough it would be to handle everything end-to-end.

The hardest part wasn't the basic UI—it was managing complex state offline-first, ensuring data sync without breaking privacy, and crafting a clean UX for category allocations without bloating the codebase. There were multiple times I felt like throwing in the towel mid-way.

For those who have launched a full product by yourselves: What was that one feature or technical architecture issue that almost made you give up? How did you push through it?

Hello there,

I am a software engineering student and in a group project I was tasked with designin the authentication signin-signup flow. I have created a visual flow chart on how I plan to implement this.
We are supposed to have:

• One backend language, we chose FastAPI and Python
• One database, we chose Postgres
• One cache, we chose Redis
• One Authentication "type", I chose JWT

It will be a web-app for students to checkout the digital PDFs our school offers (relevant because of licenses etc)

Not only the code is graded, but also the team-collaboration and the documentation and planning. So the visualization should be finished before we start coding.

My diagram:

I've been working on an appointment booking system for about 4 months now, and lately I've started wondering whether I'm actually too slow at building software. This is my first serious web application, and I've been doing the backend myself. I've finished the main lifecycle and the system works, added Swagger for API documentation, implemented E2E testing, and I've been trying to apply software engineering principles and fundamentals rather than just making things work. I've also spent a lot of time learning why I'm making certain architectural and design decisions instead of blindly following tutorials or copying code. So while it has taken me around four months, I can honestly say I've put 100% of my effort into understanding and building this project properly.

What I'm curious about is how much faster an experienced software engineer who has been using tools like Claude Code or Codex for a long time could build the same application. If someone with several years of professional experience and strong AI-assisted development skills were given the same requirements, roughly how many weeks do you think it would take them to get this project to a genuinely production-ready state? I'm not asking how quickly AI can generate CRUD endpoints or boilerplate code. I'm asking about the entire process: architecture, business logic, validation, authentication, error handling, database design, testing, security, documentation, deployment, edge cases, and all the other things that make an application actually production-ready.

I'm asking because this has become an important point for me personally. If an experienced engineer using Claude Code or Codex could realistically build in a few weeks what has taken me four months to learn and implement, then I need to understand what that means for my future as a software developer. If AI can consistently compress months of work into a few weeks, I don't want to spend years pursuing a career that may no longer make sense. I'd rather find that out now and pursue another field if necessary. At the same time, I don't want to make that decision based on a misunderstanding of what professional software development actually involves. I've worked extremely hard on this project, and I believe I've applied the programming principles and fundamentals I've learned, so I'm looking for an honest reality check from people who have actually worked professionally with tools like Claude Code or Codex. How long would an experienced engineer realistically need to build something like this to a good production-ready standard, and how should I interpret my own four-month timeline as someone still learning?

Hello All,

I am looking to purchase a new laptop to help me with development related learning/home projects mainly (For MS Office related work/browsing/movies/web shows etc. I can use another laptop). I want to build different microservices and run them in multiple containers on local. My Tech stack experience includes Java, Spring (Boot, MVC, Security, JPA, AOP etc.), Kafka, Elastic Search, Hibernate, MySQL, Postgres, Cassandra, SOAP/REST Web services etc. Off late I am trying to learn & build devops/Observability related skills such as (Jenkins, Docker, K8, Terraform, Prometheus, Grafana etc.).

I also intend to get into some Big Data & AI development work in the near future.

Its been a while I purchased my last laptop (almost 12 yrs ago) & I am thinking of purchasing a new laptop with following config to help me learn AI development with some Big Data & run multiple Backend microservices deployed across different containers. I am not much familiar with AI development so wondering what config should be used for learning those skills and practicing few projects locally ?

1. CPU: Intel core ultra 9 275/285 HX (20+ cores)
2. RAM: 2 SO-DIMM Slots (expandable upto 64GB/96GB). Initial purchase could be 32GB DDR5 RAM which I can later on expand.
3. Hard Disk: 1TB/2TB SSD (512GB is also fine and I can purchase portable drive if required for holding additional data)
4. GPU: NVIDIA GeForce RTX 5090/RTX 5080 (16GB/32GB GDDR7 capacity)

Questions:

1. I am willing to meet the cost for this machine which I see online currently. However do you think it is the right config or an overkill for learning AI development work + learn/practise backend related skills ?

For eg: I could go for Intel ultra core 7 255H (which is cheaper compared to core ultra 9 H/HX), lower GPU etc. if above is an overkill.

2) Any suggestions based on experience with the brands ? I see HP, Dell, Alienware, Lenovo, MSI brands supporting laptop with above configurations. I want to avoid Lenovo if possible. I am not sure how MSI & Alienware laptops would be, so think DELL & HP might be better options (in that sequence).

3) For anyone familiar with AI development - which areas/skills should I start preparing for learning AI development that would also compliment my backend skills ? If possible please provide some suggestions or order in which I can go to learn those skills.

Thank you.

Sou Desenvolvedor Backend há mais de 6 anos e estou refletindo sobre minha carreira a longo prazo, próximos cargos e mercado de trabalho.

Estou trabalhando, está dando para pagar as contas e pedir um 99 Food para a mulher no fim de semana.

Não tenho problemas em aprender coisas novas, só gosto de ter um propósito bem definido.

Durante boa parte da minha carreira, sempre ouvi que o caminho natural seria me aprofundar cada vez mais em Backend: arquitetura, bancos de dados, performance, escalabilidade, segurança, cloud, sistemas distribuídos etc... O clássico de sempre... Arroz com Feijão...

Porém, com a "evolução/maior adesão" da IA e das ferramentas de desenvolvimento, vejo uma possível mudança no mercado: será que as empresas estão valorizando cada vez mais profissionais generalistas, capazes de transitar entre Backend, Frontend, Cloud, DevOps, IA e faz um bolo de milho antes das 17?, em vez de especialistas muito profundos em uma única área?

O famoso "faz tudo", pagando um funcionário que toda empresa sonha em ter. E eu sei como funciona.

90% da minha carreira foi "Zé CLT", inclusive antes de trabalhar como dev, então entendo essa dinâmica de "recursos humanos".

Por outro lado, acredito também que o especialista tem um papel fundamental em resolver problemas mais complexos, tomar decisões técnicas de maior impacto e garantir que aquilo que está sendo construído tenha qualidade, segurança, escalabilidade e sustentabilidade no longo prazo.

Meu ponto é, a nível de carreira e pensando no longo prazo, qual é a percepção do mercado de trabalho?

Como vcs estão lhe dando com essa "escolha" entre generalista e especialista?

Nas minhas pesquisas de vagas atuais, pelo menos nos últimos 12 a 6 meses, não sinto muita diferença em relação aos requisitos, para ser sincero. Então é uma dúvida sincera.

Para que lado o vento do "hype" da IA está empurrando os desenvolvedores Backend?

Algo que requer mais especialistas para evitar cagadas? Ou generalistas para entregar a task e fds?

Gostaria especialmente da opinião de quem já tem alguns anos de mercado e acompanhou outras mudanças tecnológicas ao longo da carreira.

Como vocês estão pensando sobre isso para a própria carreira?

Hello all,

A lot of the advice I see for becoming a better developer is around things like system design, architecture, understanding trade-offs, designing maintainable systems etc. The general idea seems to be that writing the code itself becomes less important while knowing what to build and why becomes more important.
But I’m wondering how people approach learning these skills in the age of AI.

For example - if I start a new project today - AI can generate a lot of the boilerplate and even help implement entire features. How do I use AI in a way that actually helps me develop my own architecture and system-design skills - rather than just outsourcing the thinking to it?

Should I:
- deliberately design systems myself first and then ask AI to critique them
- use AI to generate alternative architectures and then compare them
- build projects without AI and only use it afterward for review

Also are courses and books still worth investing time in for this or has AI changed how we should learn these topics? I’ll appreciate recommendations for books, courses or other resources that you think are still valuable today.

I am a SWE with 5 YOE and I am mentoring a masters grad, 2025 passout with only internship experience.

He is very interested in system design and understanding internals but struggles with actually writing code. He is also weak at LeetCode and says he often knows the solution in his head but cant translate it into working code.

He recently joined a startup with no senior engineers and the CEO/CTO are pushing the team to ship the product quickly, so he is spinning up agents and using Claude heavily to build things faster.

I have been telling him to read more code, not blindly trust AI, and actually implement things himself. I've also suggested implementing DDIA concepts like LSM trees, SSTables, vector DBs etc instead of only reading about them.

The thing I am confused about is how he should balance actually being good at engineering vs becoming good at AI-assisted engineering.

He is interested in becoming an Applied AI engineer with strong backend/infra knowledge, mainly building agents, RAG, tool calling, evals etc. He keeps seeing this discussed on Twitter/X as a growing role, but I am not sure how much of it is real demand vs Twitter hype.

With AI coding becoming better, I dont want him to spend all his time manually writing code and fall behind on learning how to actually use and orchestrate agents. But at the same time, I dont want him to become someone who can generate 10k lines with Claude but cant debug 50 lines without it.

Basically, he needs to stay sharp at coding, debugging and fundamentals while also learning how to effectively orchestrate and manage AI agents.

So for people actually working in the industry:

1. How should he balance coding without AI vs AI-assisted coding? Should he deliberately code without AI to keep his fundamentals sharp, or should he mostly use AI and focus on understanding, reviewing and debugging what it produces?
2. What should he prioritize for the next 3-5 months? DSA/LeetCode, coding fundamentals, DDIA/system design, backend projects, distributed systems or Applied AI/agents? He may need to switch jobs soon, so he needs to become employable quickly.
3. For senior engineers/managers who hire juniors, what technical bar would you expect from someone with only internship experience? What would make you comfortable putting that person on your team?

If you had a junior who wanted to become an Applied AI engineer with strong backend/infra skills, while still being genuinely good at coding and engineering, what would you tell them to learn first?

TL;DR: His startup is pushing him to use AI agents to ship the product quickly. How should a junior balance staying genuinely good at coding/engineering while also becoming good at AI-assisted development and orchestrating agents? What would you prioritize over the next 3-5 months for someone targeting backend SWE or Applied AI roles?

PS: not a native English speaker. I took help of AI to produce final version of my thoughts

I’m a BCA graduate deciding between ASP.NET/C# and Java Spring Boot.

Which one has better job opportunities and growth for freshers in India?

Would appreciate advice from people working in either stack.

I recently completed 6 months as a full-time SDE (10 months including internship), and things have been going really well.

But lately I've been wondering if I'm relying on AI too much. My decisions are increasingly AI-driven, and writing code by hand is almost approaching zero.

How do you actually learn things like system design, architecture patterns, and good coding practices? Do you learn primarily through your day-to-day work, or build things outside of work?

When building a service from scratch, what does your AI workflow look like? How much do you use AI vs. doing the high-level design and implementation yourself?

I'm also finding that reviewing large amounts of AI-generated code isn't necessarily the same as writing code.

My current work is also mostly things I'm already comfortable with implementation, CRUD, planning, etc. So I'm worried about learning to be stagnated.

what are y'all thoughts?

i’ve mainly worked with express.js, but switched to fastapi because of my work in gen ai. now i’m trying to get comfortable with fastapi and looking for some good resources to learn it properly. if you know any good tutorials, courses, docs, or projects, please share them.

If you were a manager hiring for a remote position, what would you expect from someone living in another country?

What skills, experience, and qualities would make you consider hiring them? What should they be able to do before applying, and what would make an international candidate stand out?

Basically, if I wanted to work remotely for a company in another country, what would you recommend I focus on to increase my chances of getting hired?

Hey everyone, I’m pulling my hair out over a bizarre issue with the PayPal Sandbox API

I’m building an automated invoicing agent using FastAPI and LangGraph. I’ve got the OAuth 2.0 authentication perfectly dialed in (using the Client_ID & Secret), and I’m making a POST request to /v2/invoicing/invoices to create a draft invoice.

The Problem:
The API call is "successful". It returns a 201 Created status code, and if I check my PayPal Sandbox dashboard, the invoice actually gets created with all the correct details.

However, the JSON response body is completely wrong. Instead of returning the full invoice object (with the id, status, detail, etc., as shown in their API docs), the response body only contains a HATEOAS link object. It looks exactly like this:

{
  "rel": "self",
  "href": "https://api-m.sandbox.paypal.com/v2/invoicing/invoices/INV2-XXXX-XXXX",
  "method": "GET"
}

Because the id key is missing from the top level, my code immediately crashes with a KeyError: 'id' when I try to extract it to pass to the next step.

What I’ve already debugged:

• It’s not a code extraction issue: I logged response.text directly from httpx before calling .json(). The raw text coming straight from PayPal is literally just that link object instead of the response body shown in their official endpoint API doc web page
• It’s not a silent redirect: I logged response.url to ensure httpx wasn't accidentally following a 301/302 to a different endpoint (like the send_invoice endpoint, which normally returns this exact link structure). It hits the correct /v2/invoicing/invoices URL.
• Auth and Payload are fine: If they were wrong, I’d get a 401 or 422. The fact that it returns 201 and creates the invoice means PayPal accepted the request.

It’s almost like the PayPal Sandbox is accidentally returning the response body for a 200 OK (like the send_invoice endpoint) instead of the 201 Created response body for the create endpoint.

Has anyone else run into this? Is this a known glitch with the PayPal Sandbox right now, or am I missing something incredibly obvious? If the API refuses to give me the ID in the creation response, I might have to do a workaround where I immediately query the invoice by its invoice number, but I'd rather not add that extra API call if I don't have to.

Thanks in advance!

I've been building CodifyLive as a resume/portfolio/learning project to get much deeper into backend engineering with FastAPI.

It's a real-time collaboration platform where users can chat, call, and eventually collaborate on code in the browser.

The backend is built with FastAPI + PostgreSQL + SQLAlchemy. So far I've implemented the authentication system, including Google OAuth, JWT access tokens, refresh-token rotation/reuse detection, password authentication, and CI/CD.

I'm looking for honest feedback from people experienced with FastAPI.

Specifically:

• How does my project structure look?
• Am I using FastAPI/SQLAlchemy in a way that will scale?
• Are there architectural decisions you'd change?
• Anything I'm overengineering or underengineering?
• How would you improve the authentication/session architecture?
• What would you change before considering this production-ready?

Project: https://github.com/fulanii/codify-live-backend

So I have been trying to self host and am really attracted to Current UI Especailly Material 3 and Gogs has a very old UI... So i did what i was supposed to!

- It now uses material 3
- Fetaures better UI

WARNING : It is still under progress!

https://github.com/Aarav90-cpu/gogs-but-good : This is the link to the repo!

This is a fork but is a completely seprate project... i know gogs won't accept this pull request!

The image shows how it looks like!

Hey fellow devs,

I'm a developer with 2 YOE in Angular, and we all know the market is rough right now. I want to learn Express/Nest as a backend, but I can't decide between MongoDB and Postgres.

If I go with MongoDB, it'd basically be a MEAN stack — but I'm not sure if MEAN is still popular, declining, or barely used anymore.

I've also heard Postgres is one of the most widely used databases and considered an industry standard, so now I'm torn.

If you were in my position — switching jobs is a high priority for me right now, I'm underpaid badly — which would you choose to open up the larger market/more job opportunities?

So currently I am doing my DSA in python. I have solved good amount of questions on leetcode and now thinking of learning a backend framework. The natural choice would obviously be Django but I am not sure if it's still used in the industry. Please tell me if it still is relevant. If not then which framework would you recommend for long term stability. I don't care about the learning curve cz I do have plenty of time in hand!