OpenDevin VS scrapio

Open Devin is an innovative open-source project that builds on the ideas and capabilities of Devin. Employing technologies like Docker and Kubernetes for secure code execution and featuring a user-friendly interface designed with React, Open Devin supports real-time developer interactions and adjustments. Its versatility is supported by a community-driven approach, encouraging developer contributions and rapid adaptation to new technologies.

We have an issue in OpenDevin to add Aider as an agent, if anyone wants to take a crack at it:

https://github.com/OpenDevin/OpenDevin/issues/120

So… don’t hold your breath. If you see someone building a UI (like this https://github.com/OpenDevin/OpenDevin/tree/main/frontend, https://github.com/stitionai/devika/tree/main/ui; just read the commit log, it’s basically just ui

Yes. If you link to the UI sections of a repo you will likely see "basically just ui" commit history.

I guess you could argue that it’s important “setup infrastructure” stuff that any project starts with… but I’m just sceptical.

Of course it's an early stage pilot, it's only been developed since the Devin hype, why should it compete in quality at this very early time?

Is this similar to Devin (https://github.com/OpenDevin/OpenDevin)?

> @justlin610: Me and @huybery are discussing about reproducing Devin. https://github.com/opendevin/opendevin Come and join us and see if we can make something great together!

Developers can thrive thanks to open source! I believe that AI's future lies in sharing and open-source principles. With tools such as Devin, developers have nothing to fear about job security. Rather, they gain a free personal programming assistant. This allows them to concentrate on generating innovative ideas, while the AI handles the technical tasks, ultimately freeing up their time.

> Nah these things are all stupid as hell. Any back and forth between a human and an LLM in terms of problem solving coding tasks is an absolute disaster.

I actually agree in the general case, but for specific applications these tools can be seriously awesome. Case in point - this repo of mine, which I think it's fair to say was 80% written by GPT-4 via Aider.

https://github.com/epiccoleman/scrapio

Now of course this is a very simple project, which is obviously going to have better results. And if you read through the commit history [1], you can see that I had to have a pretty good idea of what had to be done to get useful output from the LLM. There are places where I had to figure out something that the LLM was never going to get on its own, places where I made manual changes because directing the AI to do it would have been more trouble than it was worth, etc.

But to me, the cool thing about this project was that I just wouldn't have bothered to do it if I had to do all the work myself. Realistically I just wanted to download and process a list of like 15 urls, and I don't think the time invested in writing a scraper would have made sense for the level of time I would have saved if I had to figure it all out myself. But because I knew specifically what needed to happen, and was able to provide detailed requirements, I saved a ton of time and labor and wound up with something useful.

I've tried to use these sorts of tools for tasks in bigger and more complicated repos, and I agree that in those cases they really tend to swing and miss more often than not. But if you're smart enough to use it as the tool it is and recognize the limitations, LLM-aided dev can be seriously great.

[1]: https://github.com/epiccoleman/scrapio/commits/master/?befor...