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.

Please help

Hi guys so I will be starting my backend journey in golang,already done with the basic syntax and functions but I have no idea how to approach the beginning topics.Any guidance and resources will me much appreciated.

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

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 🙂

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.

Is CS50x, then CS50 Python, and then CS50 SQL a good way to start backend engineering? I am really excited! My main goal right now is to land a part time job.

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?

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?

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?

After doing AI consulting to hundreds of companies in recent years, here are my key insights to explain why most AI transformations fail. One word - it's the PEOPLE. Reason 1) The company management's grand AI vision does not percolate down to other junior managers and employees. This means that most people in the company don't believe in AI, even after the executives talk about AI for many hours. This is a true story -- a Senior Director at a large customer told me she "thinks AI is good but that is no value in models". In fact, she said AI models have "no value" many times. And she is not the only person in that company (won't say name) who said this point. Even though the company told the entire world that they are an "AI-first bank". In reality, no AI is done. Reason 2) Many people in the company talk about AI, but few are actually doing the real hands-on AI work. Another true story -- I was providing consulting work to this large insurance customer. I gave many AI hands-on enablement workshops, taught the data scientists how to fine-tune, distill models. Then they asked me to help advise on this interesting project. They want 1 Director, 2 Managers, 1 project manager, and only 2 architects in the project. Only 2 out of the 6 people are doing the real AI work. They should have ALL 6 people doing the AI work. Reason 3) The "Know-it-All" mindset because of "experience". I give AI consulting to some large end-user companies, where the experienced tech Leads are usually hired from Big Tech. They are reluctant to learn new AI. Another true story -A tech Lead told me to put AI Agents in a company GitHub. When advised about cybersecurity, he told me it is "not important". Solutions: Solution 1) The management in the company must truly believe in the AI, do the real AI work themselves, and KNOW what is the AI they are talking about. This will give more confidence to the employees, so they will start believing and adopting AI in their work. I had talked to some CEOs that don't even know what is model trainings and model inferencings. How would their employees believe what they say about AI? Solution 2) Get as many people as possible to do REAL hands-on AI work. NOT do simple PowerPoint slides, or just draw simple diagrams on the board. Do real AI work. Build an entire AI Agent from scratch. Configure the parameters. Track the tokens. Spin up an instance. In some of my customers, I see too many "AI managers" who just tell their team to do the work, but they themselves sit back, relax, and only work 2 days per week. These type of managers should be removed. A real AI manager will do hands-on AI work, coach and mentor the team. This is known as "player-coach" working style. During this AI transformation, this hands-on process will enable many different stakeholders to gain a deeper appreciation of the AI challenges, and together, we can know what AI capabilities to build and grow.

I am trying to learn modular monoliths and I want to use this auth solutions for repetitive use for my applications.

If I asked you to convert the repo linked, into monolithic patterns, how would you do it for app/.

Repo: https://github.com/auth0-blog/auth0-rbac-fga-fastapi

fastapi-openfga-project/
├── app/
│ ├── main.py # FastAPI application entry point
│ ├── config.py # Configuration settings
│ ├── database.py # SQLAlchemy database setup and models
│ ├── models/
│ │ ├── organization.py # Organization Pydantic models
│ │ └── resource.py # Resource Pydantic models
│ ├── routes/
│ │ ├── organization_routes.py # Organization management endpoints
│ │ └── resource_routes.py # Resource management endpoints
│ ├── services/
│ │ └── authorization_service.py # OpenFGA integration
│ ├── utils/
│ │ └── auth0_fga_client.py # OpenFGA client wrapper
│ └── openfga/
│ └── model.fga.yaml # OpenFGA model definition
├── app.db # SQLite database file (auto-created)
├── requirements.txt
└── README.md

I’ve been working as a backend dev for about a year and a half. I’d call myself "intermediate," but I’ve realized most of my experience is limited to building CRUD applications. I can create, read, update, and delete data all day, but I feel like I’m missing the "big picture" of how production-level backends actually function.

I’m trying to wrap my head around a complex use case—something like a **Freelancing Platform** (Upwork/Fiverr).

If I were building it, I know the CRUD basics:

* **Create** a job post. * **Read** user profiles. * **Update** a proposal status. * **Delete** a draft.

But I know there is a massive world "beyond" those four letters. I understand there are things like async tasks, security (JWT/OAuth), and background workers, but I’m struggling to visualize what the logic looks like when it isn't just "saving or fetching from a DB."

**My questions for the senior/lead devs here:**

1. **Business Logic vs. CRUD:** In a complex system, what are some examples of logic that *isn't* just a database wrapper? (e.g., Escrow payment release logic, reputation scoring algorithms, or recommendation engines?)
2. **State Machines:** How much of a "complex" backend is just managing states (e.g., a contract moving from 'Pending' -> 'Active' -> 'Disputed' -> 'Resolved')?
3. **The "Invisible" Layer:** What are the things that separate a "tutorial project" from a "production system"? (e.g., Idempotency in payments, race condition handling, audit logging, or rate limiting?)
4. **Architectural Patterns:** When CRUD isn't enough, what patterns should I be studying?

I really want to level up from "the person who builds APIs" to "the person who builds systems." What should I be looking at next?

**TL;DR:** I know CRUD. What is the "meat" of the backend in a massive app like Upwork and fiverr, that isn't just basic database operations?

Hello guys, so for the past month I was learning C#. I did some projects, and I think it is time to moveon too SQL. I am not learning through a course path or something; I am just watching some YouTube videos, and I learnt C# with Bro Code videos. It was very easy for me since I had some foundation in Python and I just transferred my skills. SQL is way more confusing for me. Do y'all have any videos or courses in mind I can learn it with? Keep in mind I want to learn SQL because I want to become a .NET developer, as I know there are different types of SQL, so just saying.

We tightened scanners, pushed some checks into the IDE, and cleaned up CI. The failures that still hurt all have the same shape: the code is syntactically fine, but nobody can explain why the agent wrote it that way. The code has Git. The reasoning doesn't.

The questions we end up asking in incidents aren't "what does this function do" but "why this approach over another viable option" and "which constraint did we assume was nonnegotiable, and who agreed on that." Historically that lived in ad-hoc docs, chat logs, tickets, and people's heads. None of those are authoritative, and most of them are gone when you need them.

We added a repo-level, append-only ledger, one log per repo capturing intent, decisions, open questions, and resolutions, each with optional evidence references (a file, commit, or test) that get checked for existence, not correctness. Humans and agents both read from it before starting work and append to it as decisions get made. It lives in Git, so it versions and ships with the code like everything else.

We scoped it to hot zones only: auth, payments, and the core messaging layer, rather than trying to cover the whole repo from day one. Keeping the surface area small made it much easier to keep the ledger from turning into a junk drawer.

The other thing that helped was making the ledger part of review culture, not just a file on disk. If a PR touches a service and there's no matching entry, reviewers ask what's the intent and what constraint are you relying on. That social pressure has been more effective than any process doc.

What would you add or change before trusting a ledger like this as part of your release decision?

So I'm still a year 1 who took a BE course in ASP.NET MVC and thinking of making my 1st project during summer semester. How should I structure my project as a yr1 and deploy it?

P.S: My idea is a simple status and position app for healthcare that is doable with the course I took and is manual positioning rather than tracking constantly.

Hi everyone, I’m a software engineer with 3.5 years of experience, mainly in web/backend development.

I enjoy building real products, solving practical problems, and learning by doing.

I’m looking to connect with other developers, founders, and builders who are into shipping projects and sharing ideas.

If you’re working on something interesting or open to collaboration, feel free to comment or DM me.