Ask HN: What's the best way to get started with LLM-assisted programing?

> Using Cursor, I felt like it was too easy to modify too many things in one go.

Same experience. It seems like it's just overloading the standard vscode UI, with no regard for what you're used to. Trying to press tab for what looks like a "normal" autocomplete will invoke the LLM autocomplete that will replace a bunch of lines somewhere else without you noticing. Terrible UX!

Modifications made by the chat interface are also hard to restrict to a specific part. Sure, you can give it context, but asking it to modify one thing (by highlighting a bunch of rows in a specific file and giving them as context) is likely to end up modifying a similar-looking thing somewhere else.

The potential is there with Cursor, but right now it's a mess for serious use. Waiting for editors that incorporate LLM-based autocomplete in a disciplined way where I'll actually feel like I'm the one in control. Maybe once the dumb hype around vibe coding dies down.

Admittedly, I’ve achieved so much with the assistance of extensions like Cline/Roo that I have a sort of youthful exuberance that wants to proclaim that the barrier to entry to building applications with AI is falling.

It’s not.

These tools will literally waste your time and empty your wallet if you don’t have a solid approach — and that starts with knowing how to detect code and architecture smells at light speed. These tools are writing code faster than you can read it so you better know what you’ve (actually) instructed it to do, and be able to monitor the quality in a 300ms glance or you’re hosed.

Having said that, there are antidotes:

1. Use a memory-bank. A memory bank is a little folder where you instruct the agent to research upcoming tasks, store progress info, post gotchas, generate retrospectives and more. It’s super helpful if the thing you’re building is large and complex.

2. Instruct the agent to implement strict TDD/BDD principles and force it to follow a red-green-refactor workflow at a granular level. The downside to this is twice the code (read $tokens) per feature but you’ll spend it anyway when your agent has 600 lines of untested code written while you grabbed coffee and you come back to the agent stuck in a loop, spending $0.20 per request sending the same large files trying to figure out some syntax error between test and code files.

3. You need to know what you’re doing. The model/agent is not going to automatically apply second and third order thinking to look around systems design corners. It does exactly what you tell it to do (sometimes well) but you also have to always be considering what you forgot to tell it to do (or not to do). Prompt engineering is not natural language. It’s a form of spellweaving where you have to find the edges of all relevant forms of existence and non-existence, codify them into imperative language, add the right nuance to finesse the style and have the confidence to cast the spell — over and over and over again.

4. You need money. Building a serious endeavor with an AI agent will cost you around $400-600 a month. Leave room for lots of mistakes. Prepare for weeping and gnashing of teeth when the task counter hits $10.00, you’re like 20% done with the feature, the model has lost context about what it was doing and you have to choose between making a broken commit and hoping the agent can sort it out in a clean task, or eating the cost and starting over. I spent $50 on starting over just this past weekend. This is why I wrote numbers 1-3 above.

I’m committed to figuring out where all the pitfalls are so others might not have to. I personally feel like anyone who’s doing this right now is really just burning some cash to see what’s possible.

I use Codeium, mostly in autocomplete mode, I generally only ask it to write code by itself if there's a self contained pure function.

Probably a good place to ask — any good AI tools to help review GitHub pull requests? Something better than copy/paste into a chat app