HACKER Q&A
📣 e-topy

How to Start with LLMs/Vibecoding?


Inspired by LLMs lately becoming (terrifyingly) capable, and my new work project being no longer fun, I wanted to ask: how should I (or anyone experienced in programming but new to using AI) get started with AI in programming?

I understand a lot of the theoretical basics (NNs, LLMs, Agents), but I haven't really tried using them for my professional work. I'd normally just search the internet, but considering the amount of conmen and slop, I'd rather ask people who actually use it. Guides, blog posts, videos, anything useful or informative would be very appreciated.

For context, I am studying CS and have been working as a full stack ~junior for 3 years now.

  👤 armanckeser Accepted Answer ✓
If your aim is to just get started, get yourself a Claude account and just download Claude Code. You don't really have to read anything on it, as you use agents you will get to learn what they are good and bad at but defaults are quite good anyway if you use a capable model like Opus 5. Truth is there are a million guides and plugins in the open but they really provide incremental gains for a beginner as they would overwhelm you.

If you are more conscious of the price, try out opencode or pi and research OpenRouter or similar for coding with models like GLM 5.2 or Kimi K3 or even Deepseek 4, they will be less out of the box but cheaper.

👤 turtleyacht
Build Applications with Local AI Models on a Mac

https://www.manning.com/books/build-applications-with-local-...

👤 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.

👤 digitaltrees
You will have to decide how you like to work, some people are trying to go all in on how agents work: let them run long sessions, set constraints through /skills, evals and context but then trust them to fully implement the code (dont even bother reading any code, reading code is an anti pattern). Others use AI like a junior developer, giving clear discrete tasks, monitoring and steering for architectural and coding style course correction and working on teaching it better practices through context management and rules. I am in the treat it like a junior camp but have also had products where I end up reading less code than I would expect.

You'll notice the tools push different perspectives, coding in VS will probably push more of the discrete task, AI as a junior dev approach, claude code/codex/pi/opencode etc can be more "autonomous, dont even show the diff" vibes with long running tasks that spit out 10,000 lines of code without even asking for any feedback or review.

What I have found is that planning documents are critical, start with an agile roadmap that links to epics files outlining large features. They should have clear acceptance criteria that act as gates to close out stories/sprints/epics. Every document should say "follow red/green TDD" so that testing discipline is a firm requirement.

I have also found that AI will go rogue no matter how good your context, planning, evals or TDD, so always work on a clean git branch so you can blow it up if the AI does something wrong. Treat AI sessions as experiments and prototypes and be careful of feeling sunk cost fallacy. Code gen is cheap so do implementations 4 times until you get "good" code/ui/ux architecture etc.

I would be happy to do a pair programming session to show how I do things. I built www.propelcode.app an agentic IDE so I can experiment with ways of coding and not be dependent on the frontier labs or big company products.

https://calendly.com/ryanwmartin/open-office-hours