[anigbrowl]

How card can it be? You have a favorite IDE, right? Pick an AI service you can afford. If you are a student at an institution you probably get heavy discounts, if not pick the cheapest monthly tier on an AI service you respect. If you are not sure about committing to a subscription then just buy tokens on Deepseek.

Find an agent plugin aka a harness for your IDE that doesn't require a subscription, unless of course you enjoy giving money away. Some agents are configured to work with particular providers, others are 'bring your own key', so again it depends on whether you like being locked in to someone else's ecosystem and paying acordingly. you can also get harnesses that are self-contained rather than being part of an IDE, but I assume you like your existing tools.

Take the API key you got from your AI service, plug it into the agent. Start building a toy project; the easiest is something that you have previously made yourself so you know something about the data structures involved, problems you're likely to run into and so on. Treat the agent like your assistant. I prefer asking it to draft a plan of what it's going to do which I then approve rather than just setting it free and then puzzling over the output, you may evolve a different approach.

[e-topy]

Well I've done everything you're saying, but I got stuck after that. I've tried claude code, I've been using OpenRouter, but after that it just seems like a million different options.

Take tools, there's a million different options, and I don't know how to choose between them. Or the general mentality of treating it as an assistant vs the pilot. Or the massive ecosystem of skills and MCP. It just feels like a massive unexplored ocean to me. But I guess I'm just getting analysis paralysis.

[vunderba]

None of that is necessary. It’s like people who install Obsidian and then spend weeks obsessing over the “top ten must-have plugins” for the ecosystem instead of just using it as it’s intended: as a tool to take notes.

The purpose of a coding LLM is basically to assist with building something. So start with an actual concrete goal: something you want to build. Maybe even take a past project and try to recreate it, but this time using an LLM-augmented workflow.

Don’t get bogged down in a bunch of orchestration/MCP/skills/etc. - keep it simple and expand as you go. The best thing you can do is actually use these tools to help you develop an intuitive sense of their strengths and weaknesses.

You don’t need to watch videos or tutorials. Grab something a basic agent harness like oh-my-pi [1], throw $30–50 on OpenRouter, and start playing with it.

[1] - https://github.com/can1357/oh-my-pi

[anigbrowl]

You are. Obsessing over tooling is a form of procrastination. Once you have it up and running and it can generate code, documentation etc and write and save files, just start building things. you can try one-shotting it (Clause, build me a better version of Thing), or tell it what you want to do and have it sketch the framework out for you or whatever. You might find it easier to work with long detailed specifications, or to limit your prompts to a paragraph at a time and keep laser focused. different models have different benefits and they also have their own pain points, like getting carried away and writing too much or starting to loop back to earlier in the context window etc. etc. Just roll up your sleeves and start playing. You don't know what you want to optimize for yet so you're just comparing yourself to the fake personas of other people on the internet who are trying to pose as the next Elon Musk. After you have been doing it for a while you can refine your own workflow. But focus on outputs more than workflow.

You have a huge advantage from your CS studies in that you're already knowledgeable about algorithms and data structures and the concept of algorithmic efficiency, so once you get prototypes of your ideas up and running, refining them should yield rapid improvements.