Hot take, and one that people don’t like to hear here, be careful with using LLMs for learning to code; and frankly, my advice would be don’t use it al all. They can certainly provide helpful answers, but they also provide a ton of incorrect answers. And if you’re a beginner it’s basically impossible to sus out what’s true and what isn’t. If you’re looking for feedback on smaller samples of code forums like this or the code review StackExchange is still your best bet. More on reddit.com

https://github.com/ajalt/fuckitpy More on reddit.com

Updated: December 6, 2024 Currently the best model for code generation is Sonnet 3.5 I've been a software developer for a few decades now and I will say that overall the code quality generated by AI is not "excellent" by any measure, however there is definitely some give and take here. AI likes to use common patterns and generally favors simpler code over more complex. This isn't always the case of course, sometimes it will abstract something unnecessarily when a simple if-statement would do, but usually the code is at least "good enough". The code quality and general understanding of the prompt seems to favor Sonnet, so for your regular go-to model, I would suggest Sonnet 3.5. This might surprise some people since o1 has made some major waves, but I believe for new code Sonnet is still king. Because it is usually on-point, and if you're not giving it too much to do at once, you can really fly with this model. I have created entire new features where 4 files were modified and 8 files were created with a SINGLE PROMPT. This can only be done in certain situations where the work ends up being very serial in nature, but Sonnet can do this quite well with only minimal after-tweaks. The best model for debugging, if Sonnet fails, is o1 There will invariably be times when Sonnet is not able to fix something. You might be describing an issue you're having with your application, or sharing some logs, and Sonnet may think it has the answer but it just doesn't. This is not dissimilar to the experience EVERY software developer runs into even before AI. Spinning your wheels on a weird debugging problem is just par for the course. However when Sonnet gets stuck, rather than hitting stack overflow, give o1 a shot first (or maybe 2). OpenAI's o1 is particularly good at solving very specific, less open-ended problems. If you ask o1 to create a new feature for you however, you might find it ends up making unwanted presumptuous decisions about your code. I believe this happens when the internal dialogue that o1 uses to think something through, strays the thought process into areas that was not necessarily discussed by the prompt. This is great when you're debugging but it's not so great when you're trying to get it to implement something specific. You still need a human in the loop, here's why... First of all, you can't trust that the AI will maintain the overarching structure of the application. It just doesn't have that kind of context unless you give it to it. He also have to be constantly watching that the AI isn't giving you code that is less than ideal. It might work but sometimes it can be over engineered with patterns that are not necessary for a given situation. Lastly you have to be aware of the code base and what was implemented because at some point you are going to have to debug. AI is not very good at debugging yet and you'll have to take over at some point, guaranteed. The best AI tools have the following features Integration directly into your IDE (imo) - While this isn't absolutely necessary, the speed at which you can operate with an AI that interacts directly with your IDE just feels right. The ability to change and create multiple files with a single prompt - Most AI assistants can do this these days, but some are better at it than others. For a long time Cursor's ability to apply code changes like this was very lacking (although I think they have gotten better with more recent iterations). Codebase understanding through vectorization of your codebase - Some of the best AI assistants can do this. Basically it creates a database of your entire codebase so that the AI can find relevant files and code based on your prompt. There is a lot of variety with how this particular solution is implemented with current assistants. Voice mode - You should be able to use your voice to speak to the AI. This obviously isn't going to work all the time (even if you like the feature), like when you're surrounded by people in a traditional work environment, however the benefits are more than convenience. When people type out a prompt they tend to be more concise but AI benefits from long-winded conversation, even if you're correcting yourself! So speaking your prompt naturally tends to give better code quality and understanding in my experience. Include web sources into your prompts - This one is very important because you can't just rely on what the AI has been trained on. Sometimes you want to implement something new that has little presence online, sometimes you find a fix for a bug on stack overflow and you want to implement it. Being able to easily reference a webpage for this is very necessary sometimes. Autocomplete - When you want to code something yourself, having autocomplete makes this process that much faster. The usual cycle of prompting and waiting for the updates can sometimes be a bit too tedious when you know exactly the line you need so that's where autocomplete comes in. Which tools I use personally after 2 years of AI coding I have tried a bunch of tools, including using ChatGPT directly, Cursor, and tried out a handful of others but I have settled on 2 tools that I've been using for my day job as a software engineer, as well as for my personal projects: Github Copilot - I use this for it's autocomplete functionality, and it's integration with the IDE. The code quality is really not that great compared to a slower AI, but it's fast and that's what you need it for. I don't use autocomplete all that much, but when I do I'm really glad it's there. Codebuddy - This one ticks all of the boxes I mentioned above (minus the autocomplete). I use this for the vast majority of my coding these days. Granted the majority of my projects in the past 2 years have been new projects, which allowed me to have 80-90% of the code be AI generated. With established projects you'll find you'll get less use out of AI in general, but it's still a huge time saver and at the very least can help you to know what's what when dealing with a codebase you're not familiar with. Also don't get me wrong, AI can do a lot of generation for you with existing projects, it's just easier with new projects because there are a few things you can be doing to structure your project in a way that benefits AI assistants. There have been a lot of developments in the AI assistant space recently and I have to admit I haven't tried some of the latest variants yet (though I certainly will). I think one of the biggest benefits of using Codebuddy comes down to the way it has a separate planning and coding step. This process will cost twice as much since you're technically issuing more than 1 prompt "per prompt" but the results are far less frustrating and that's worth it to me in the end. More on reddit.com

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