Hi everyone! I'm a Java backend developer trying to break into the backend field with a particular tech stack: the Spring Boot ecosystem. I've decided to focus only on that, which means I don't want to learn much frontend (even though I know a bit — I'm talking about frameworks and building UIs) or do it at work. So in my head, I would be looking into Java Spring Boot jobs for backend only. But I'm starting to realize that a job offer is just about a need for a particular project, so even if I do land the desired job, I might end up doing frontend work or backend with other frameworks (tools like Docker, Kubernetes, and other Ops tools don't bother me). But if that's the case, I'm starting to think that obsessing over specializing in the Spring Boot ecosystem may be pointless. I mean, most people who consider themselves Java Spring Boot developers are just people who happen to work with it most of the time. They didn't consciously choose it. And that's what bothers me about it.

The software we worked on for 18 months is ready. Its purpose is writing backend projects with AI without needing to write code directly.

With this software there's no need for Git and change management, everything is built inside one platform, from starting the code to deploying on stage and production and debugging and monitoring.

If I want to explain it in one line, it goes like this:

You can build and deploy backend from zero to a hundred in one platform with prompts, and get automatic documentation.

The code structure it writes for you is organized and scalable and maintainable, it writes code based on DDD and arranges everything neatly for you from the very start, all the code goes through 4 stages of lint and rule and compiler check and all the code is written uniformly, though you don't have direct access to writing code at all and whatever you need you have to tell the AI and it does it for you.

There's no need for a database and an SDK has been written specifically for the platform, and it handles working with the database and Rabbit and NATS and these kinds of tools for you, and you don't get involved with the config and maintenance of these tools either.

The code is written in Node.js, but it doesn't run in a Node.js environment because of the heavy RAM consumption in Node.js, so a runtime similar to Cloudflare Worker has been implemented (similar to AWS Lambda), and the cold start of what we have is close to Cloudflare Worker and is about 200ms, meaning it's faster than AWS Lambda. This runtime is written in a way that it can handle several million files, something that definitely isn't possible with Node.js.

Overall it has the entire stack of a relatively mid-level backend project automatically and you don't need any other tool.

Its difference with Insforge is that you don't need to have a Cursor, you don't need to have Supabase, you don't need to pay several times, and also because the platform is unified and everything is in it, both the costs become much lower, and project management becomes simpler and more organized, and we can also provide much stronger monitoring.

Its difference with Cursor is that you don't get involved with getting a server and configuring it, you don't get involved with CI/CD either, and you also don't get involved with monitoring and debugging and everything is ready in the platform.

Its difference with Supabase is that the AI is integrated and a very good chat has been built into the platform itself and you can tell it to write the code for you without needing Cursor. This chat works like Cursor, it has several different tools, it has several sub agents that break the work down and split it up so they can write more accurate code.

Its difference with Replit is that this platform is dedicated to backend and gives you much better monitoring and debugging, also Replit brings up each project as a container which has much higher RAM consumption, so the final cost for several million users on Replit is much higher than several million users on my platform because of the very large infrastructure difference they have.

The LLM model used is Gemini Flash Lite 3.1, meaning it comes out much cheaper compared to Sonnet which is the advantage of this project, and it's optimized so that it can write and debug code well with ordinary AI models, which makes the final cost per user much lower than other platforms.

Hi guys , I am in my 3rd year of CS and i am currently learning Java Backend development (Springboot) , as i have been quite interested in AI , i want to know what are things i need to learn for become a backend dev who can intregate AI and work with it , i know about things like RAG , Vectordb etc
but i dont know the exact path or things to follow from basic to a level that i can build AI powered projects

I'm a backend developer with about 3.5 years of professional experience, mainly using Ruby on Rails.

I've been trying to find more remote and freelance/contract opportunities, but I haven't had much luck. It feels like there are fewer Rails openings than before, so I'm thinking about adding another backend technology instead of specializing only in Rails.

At the moment I'm considering Go or Python/FastAPI, but I'm open to other suggestions.

If you were in my position, what would you learn today? My goal isn't to abandon Rails I still enjoy working with it but to increase my chances of finding remote or freelance work.

I'd especially love to hear from people who have switched stacks or work across multiple backend technologies. Has learning Go, Python, or something else made a noticeable difference in your job opportunities?

knowing react, typescript and next.js, what is the best resources available out there on the internet that teaches the backend stuff from scratch? been browsing and watching through playlists and tutorials since the whole day, could not find anything, if anyone has any recommendations, kindly suggest... would be really helpful 🙂

I'm learning programming (currently C# and ASP.NET), and I'm trying to find the best learning method for my situation.

My main problem is that I lose focus easily while someone is explaining. When watching video courses, I sometimes find myself thinking about something else for a few minutes, and then I realize I missed important parts of the explanation. I have to rewind many times, which makes learning slower and frustrating.

I was wondering if books or documentation would be a better option in this case, because I can read at my own pace, stop whenever my attention drops, and reread difficult parts.

For experienced programmers or people who had similar focus issues, what worked better for you?

Did you rely mainly on videos, books, documentation, or a combination? And how did you structure your learning?

Context:

Lately, I've been thinking a lot about how to improve as a developer. Because of this, I started talking to coworkers, book authors, highly experienced devs on Reddit, professors, and more. I reached the following conclusions, but I'd love to hear your thoughts. Keep in mind that the goal of this post is to see if I'm on the right track to level up as a professional.

What I learned from talking to others:

Every developer I spoke with followed a different learning path, and I never heard the same opinion twice. However, there is one thing everyone agrees on: practice is what matters most, far more than any theoretical resource. You need to actually work, question the "why" behind every decision, compare different solutions across different scenarios, and even put in extra hours of practice by building long-term personal projects. This is why I started working at quite a young age. Thanks to that, now that I'm almost graduated with a Computer Science degree, I already have several years of experience in the industry, where I've learned a lot.

This doesn't mean you shouldn't study, though. Personally, I'm just two courses away from graduating and I'm far from done studying, as I deeply believe that having a solid knowledge base helps you get much more out of your working hours.

My final conclusion:

While there doesn't seem to be an "ideal" learning path, I lean toward finishing my degree and supplementing it with extra knowledge in the area I'm currently most interested in: Software Architecture. That's why I look to get a high-level overview of different architectural patterns, software design patterns, solutions to common problems, etc., using resources that are easy to digest (like roadmap.sh, YouTube, courses, or books). The goal is to get familiar with the concepts, even if just superficially, so I know they exist.

If I ever need to apply a new architectural pattern that I haven't used before, I'll likely have to dive deeper into it at that moment—and that's totally fine, because that's the right time to do it. But I don't think it's important to read deeply into every existing pattern and concept from the start, because knowledge only sticks when you take it out of the theoretical framework and apply it to a project with real clients.

My question regarding this conclusion:

Does this approach of getting a high-level overview of concepts in my area of interest, and then diving deep only when I need to apply them, sound right to you? Or is it better to further strengthen my fundamentals first, keeping in mind that I've almost completed 5 years of university and have a couple of years of industry experience as a full-stack dev?