I'm using Cursor's composer agent mode with Sonnet 3.5 (I don't use OpenAI on principle, snakes). It does a great job of finding the relevant code without overloading its context window.
I experimented today with Aider (to get R1 involved) and had less success, but it might be that I don't have the workflow down.
I have found cursor can handle a .NET C# back-end using highly standard code structures very well. SignalR for networking.
I've created servers and very basic HTML visualization for three projects - a fairly simple autobattler (took a day), a web-based beat-em-up (2 days), and now a bit more ambitiously my dream RTS-MMO (3rd weekend running).
I started with concise MVP specifications including requirements for future scaling, and from these worked with the AI to make dot point architectural documents. Once we had those down I moved step-by-step, developing elements and tests simultaneously then having the agent automatically run the tests and debug. The test-driven debugging is the part that saved the most frustration, as the initial implementation was almost always broken, but leaving the agent to its own devices (tabbing in and typing "continue" when hitting Cursor's 25 tool call limit, sometimes for hours) the tests guided bug fixing and amazingly it got there fairly consistently, though occasionally it will go off the rails and start modifying the tests to pass or inventing unwanted functionality.
The code is as standard as possible, with the servers all organized identically API -> Application -> Domain <- Infrastructure, and well separated between client/server. Getting basic HTML representations wasn't an issue, but it does begin to struggle and requires a lot more direction when it comes to client-side code that expands beyond initial visualization. I had a lot more success with Monogame C# than Phaser or other web formats (e.g. I quickly gave up on SFML, same issues you were having).
I'm a professional game developer but without formal CS/programming training, so I'm aware of my requirements but not always how to implement them cleanly. I understand the code it writes which feels vital when it occasionally rolls a critical miss, but these projects would have taken me months without AI.
I experimented today with Aider (to get R1 involved) and had less success, but it might be that I don't have the workflow down.
I have found cursor can handle a .NET C# back-end using highly standard code structures very well. SignalR for networking.
I've created servers and very basic HTML visualization for three projects - a fairly simple autobattler (took a day), a web-based beat-em-up (2 days), and now a bit more ambitiously my dream RTS-MMO (3rd weekend running).
I started with concise MVP specifications including requirements for future scaling, and from these worked with the AI to make dot point architectural documents. Once we had those down I moved step-by-step, developing elements and tests simultaneously then having the agent automatically run the tests and debug. The test-driven debugging is the part that saved the most frustration, as the initial implementation was almost always broken, but leaving the agent to its own devices (tabbing in and typing "continue" when hitting Cursor's 25 tool call limit, sometimes for hours) the tests guided bug fixing and amazingly it got there fairly consistently, though occasionally it will go off the rails and start modifying the tests to pass or inventing unwanted functionality.
The code is as standard as possible, with the servers all organized identically API -> Application -> Domain <- Infrastructure, and well separated between client/server. Getting basic HTML representations wasn't an issue, but it does begin to struggle and requires a lot more direction when it comes to client-side code that expands beyond initial visualization. I had a lot more success with Monogame C# than Phaser or other web formats (e.g. I quickly gave up on SFML, same issues you were having).
I'm a professional game developer but without formal CS/programming training, so I'm aware of my requirements but not always how to implement them cleanly. I understand the code it writes which feels vital when it occasionally rolls a critical miss, but these projects would have taken me months without AI.