u/yousefemz

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.

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.