You trying to learn python, or already know python and want a ai companion? If you're learning, I wouldn't use any of em. More on reddit.com

There are going to be a lot of opinions here, likely all are correct and this is just one opinion. Claude Sonnet 3.5 is the best LLM coding model out right now. How you use it, is up to you. CoPilot, Cursor, Cline or Roo are all active projects that integrate very well with it. For $20, you can pay for Cursor and you will immediately see the value but once you tap out your premium credits it starts to trail off on quality. Roo or Cline (VS Plugins) are both fantastic, each does things slightly different but you will use OpenRouter.ai and pay per use... which also can get expensive. The bigger your project becomes, the more you will consume. That's just the state of AI coding. It will get caught in loops, so focusing on the right prompts will help considerable. Roo/CLine/Cursor all have chat systems which can help you create better prompts. Using working memory by having the model update a readme with tasks, seems to help some. It's not perfect but when it works, especially in the beginning, it is pretty magical. Good luck! More on reddit.com

Kilo Code maintained here. Claude 4 Sonnet just got released and seems to be a potential home run. Claude 3.7 was really really good and even though Open AI and Google got close with their new models a lot of people kept going back to Sonnet. You could go and pay Anthropic 20/month, but I would recommend you go with one of the Open Source Agents that you can hook up to any model, especially if you use orchestrator mode you can use a more expensive model for planning and a less expensive mode for coding (Gemini 2.5 Flash) you can get a good bang for your buck. Recommended in order of popularity: Cline, Roo Code, Kilo Code (us) More on reddit.com

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. More on reddit.com