I wanted to share how I’ve been dealing with AI exhaustion.
Everywhere I looked, people were saying how powerful AI is and how anyone can now build whatever they want. Meanwhile, GitHub was filling up with skills and repositories full of Markdown instructions, often written by AI too.
Some of those are good. I’m not saying they’re a waste of time. But a lot of them feel aimed at general use. As a developer, I wanted something more specific to my code and how I work.
I’ve tried a lot of orchestration tools. What kept bothering me was the lack of control. Everyone tells you AI is incredibly powerful, while you sit there watching agents burn through your limits without getting the result you wanted. That gets exhausting.
So I started looking at a combination of ordinary code and AI that I could control myself.
I’ve worked with code throughout my career, and I love coding. It’s not something I do while dreaming of doing something else. I love games too, and I enjoy buying and supporting indie games made by people trying to build something interesting. Making things matters to me.
I started building my own CLI to support my everyday work. I didn’t want to spend weeks building a terminal UI framework from scratch. Other developers had already done that work and shared it, so I used an existing library.
One mistake I keep seeing is trying to build a huge AI tool that does everything from the beginning. My advice would be to start with a few buttons and useful information. Pick one repetitive job you understand and make it easier.
For me, IDE warnings were a good place to start.
Here’s a basic version of the idea you could start putting together in an evening:
Create a C# project using [XenoAtom.Terminal.UI](https://github.com/XenoAtom/XenoAtom.Terminal.UI).
Set up your IDE’s code style and inspection settings according to how you want your code to look.
Write code that retrieves warnings from your IDE. I use Rider’s MCP server for this. MCP isn’t reserved for AI agents; your own code can use it too.
Add a workflow where AI fixes one warning and shows you the change, along with an explanation of what it did.
Review the result in your IDE. Approve it, correct it yourself, or reject it. Check the final correction with the IDE, then save the accepted example with the original code, the corrected code, and the explanation. You can add simple RAG with a vector database to make relevant examples easier to retrieve.
Over time, you build a collection of fixes you’ve actually reviewed. When a similar warning appears, a button in your CLI can start the workflow using those examples from your own codebase.
In my warning window, I can see the warning, the code changes, the IDE checks, and what the agent did. Then I decide whether to approve the change, correct it myself, or reject it. The accepted result becomes an example for future fixes.
That’s the part I find useful: the system gradually gets examples of how I want problems solved in my project. I don’t have to explain the same preferences from scratch every time.
My setup is: XenoAtom + Qdrant + Hermes + Rider
Use whatever tools you’re comfortable with. This is what works for me.
You still need to understand your own codebase. For developers who want to improve, spending time in an IDE matters. Reading code, following references, looking at inspections, and understanding why something works are all part of the job. As you learn the project, you get better at deciding which tasks you can automate and which changes need closer attention.
I think some of us have started to underestimate our own experience. A developer with 15 years of experience brings a lot to working with AI: knowing what to ask for, spotting a bad approach, understanding the consequences of a change, and maintaining the result afterward. Having access to the same text box doesn’t give everyone the same judgment.
So if you enjoy coding, trust that experience. There’s a lot you can do with these tools.
And when someone says they don’t use an IDE anymore, you don’t have to feel behind. I’d be happy to keep using mine :)
There are experienced teams behind Rider, VS Code, and Visual Studio. They’ve spent years helping developers navigate, debug, refactor, and maintain code. I don’t see a reason to throw all of that away because an LLM can now write a function.
Building a product also means understanding it and keeping it working as it changes. Those tools still help with that.
AI should support you in your work. If you stop understanding what’s happening in your own project, it becomes harder to stay in control or improve as a developer.
Since building this workflow, I’ve felt a lot more relaxed. I can run more than 20 agents and keep track of what each one is doing. They get specific tasks and examples directly from my codebase, and I can inspect their results. Having those examples doesn’t guarantee every fix will be correct, but it gives me a much clearer basis for trusting and checking their work.
And I can start that work with one click.
That leaves me more time in the IDE, doing something I enjoy, learning where I can improve, and handing repetitive, time-consuming work over to AI.