r2ai VS plandex

I think Mistral-2-Pro would work really well for this, judging by the great results I've had with it on another heavy on tool calling project [1]

[1] https://github.com/radareorg/r2ai

This all seems pretty sensible. Another area that would be nice to see addressed are strategies for balancing latency/cost/performance when data is frequently updated. I'm building a terminal-based AI coding tool[1] and have been thinking about how to bring RAG into the picture, as it clearly could add value, but the tradeoffs are tricky to get right.

The options, as far as I can tell, are:

- Re-embed lazily as needed at prompt-time. This should be the cheapest as it minimizes the number of embedding calls, but it's the most expensive in terms of latency.

- Re-embed eagerly after updates (perhaps with some delay and throttling to avoid rapid-fire updates). Great for latency, but can get very expensive.

- Some combination of the above two options. This seems to be what many IDE-based AI tools like GH Copilot are doing. An issue with this approach is that it's hard to ever know for sure what's updated and what isn't, and what exactly is getting added to context at any given time.

I'm leaning toward the first option (lazy on-demand embedding) and letting the user decide whether the latency cost is worth it for their task vs. just manually selecting the exact context they want to load.

1 - https://github.com/plandex-ai/plandex

Today I tried to post my open source project on the /r/opensource subreddit. It's an AGPL 3.0-licensed, terminal-based AI coding tool that defaults to OpenAI, but can also be used with other models, including open source models.

The subreddit's rules in the sidebar state that a project must be open source under the definition on Wikipedia (https://en.wikipedia.org/wiki/Open_source) and also that limited and responsible self-promotion is ok.

My post was automatically blocked, seemingly by the mere mention of "OpenAI". The auto-message stated that "ChatGPT wrappers" were not allowed on the subreddit.

I messaged the mods to tell them about the mistake, since my project plainly was not a "ChatGPT wrapper". One of them replied saying only "Working as intended" and that because my project uses OpenAI models by default, that it isn't welcome in the subreddit.

I asked why projects using OpenAI in particular are penalized (despite this being mentioned nowhere in the rules on the sidebar), considering that there are many posts for projects interfacing with MacOS, Windows, AWS, GitHub, and countless other closed source technologies. I received no answer to this question. I was only told that any project "advertising" OpenAI was "against the spirit of FOSS" and therefore did not belong on the subreddit. The mod also continued derisively referring to my project as a "ChatGPT wrapper" and "OpenAI plugin" despite my earlier explanation. I was also called "egocentric" for wanting to share my project.

It made me sad that a subreddit with over 200k members that seems to have a lot of cool discussions going on is being moderated like this. What's with all the gatekeeping? Why are people so interested in excluding the "wrong" type of open source projects? As far as I'm concerned, if you have an open source license and people can run your code, then your project is open source.

Am I right to be miffed by this or does the moderator have a point? Have you experienced this kind of thing with your own projects? How have you dealt with it?

This is my project, by the way: https://github.com/plandex-ai/plandex

In this video, I use the latest 0.9.0 release of Plandex - https://github.com/plandex-ai/plandex - an open source, terminal-based tool for building more complex software with LLMs. This release includes many options for using models beyond OpenAI's.

I used Claude Opus 3 via OpenRouter.ai to fix a tricky state-management bug in a new feature I'm working on for Plandex.

I built the demo video for https://plandex.ai myself using CleanShot X (https://cleanshot.com/), Adobe Premiere Pro, an effect I bought in Adobe's marketplace, some AppleScript automation, and music from SoundStripe (https://soundstripe.com/).

It was my first time using all these tools. It took me a couple days to make the video. Premier is a bit of a beast, but by just asking ChatGPT how to do everything, I was able to get up to speed with it pretty fast.

> plandex went into some kind of loops a couple of times so I stoped using it for now.

Hey, Plandex creator here. I just pushed a release today that includes fixes for exactly this kind of problem - https://github.com/plandex-ai/plandex/releases/tag/cli%2Fv0.... -- Plandex now has a much better 'working memory' that helps it not to go into loops, repeat steps it's already done, or give up too early.

I'd love to hear whether it's working better for you now.

I'm building Plandex (https://github.com/plandex-ai/plandex), which currently uses the OpenAI api--I'm working on support for Anthropic and OSS models right now and hoping I can ship it later today.

You can self-host it so that data is only going to the model provider (i.e. OpenAI) and nowhere else, and it gives you fine-grained control of context, so you can pick and choose exactly which files you want to load in. It's not going to pull in anything in the background that you don't want uploaded.

There's a contributor working on integration with local models and making some progress, so that will likely be an option the future as well, but for now it should at least be a pretty big improvement for you compared to the copy-paste heavy ChatGPT workflow.

I'm looking forward to trying this out with Plandex[1] (a terminal-based AI coding tool I recently launched that can build large features and whole projects).

Plandex does rely on OpenAI's streaming function calls for its build progress indicators, so the lack of streaming is a bit unfortunate. But great to hear that it will be included in GA.

I've been getting a lot of requests to support Claude, as well as open source models. A humble suggestion for folks working on models: focus on full compatibility with the OpenAI API as soon as you can, including function calls and streaming function calls. Full support for function calls is crucial for building advanced functionality.

1 - https://github.com/plandex-ai/plandex

The server does quite a bit. Most of the features are covered here: https://github.com/plandex-ai/plandex/blob/main/guides/USAGE...

I actually did start out with just the CLI running locally, but it reached a point I needed a database and thus a client-server model to get it all working smoothly. I also want to add sharing and collaboration features in the future, and those also require a client-server model.

In my opinion, an "AI software engineer" is the wrong target for the current generation of models, though it's obviously good for generating buzz.

I'm working on an open source, OpenAI-based tool that uses agents to build complex software (https://github.com/plandex-ai/plandex), and I have found that the results are generally much better when you target 80-90% of a task and then finish it up rather than burning lots of time and tokens on trying to get the LLM to do the entire thing.

Results are also better, in my experience, when the developer remains in the driver's seat--frequently stopping, revising prompts and context, and then retrying rather than expecting to kick back and let the model do everything.

This is interesting. I'm working on an OpenAI-based tool for coding tasks that are too complex for ChatGPT - https://github.com/plandex-ai/plandex.

It's working quite well for me, but it definitely needs some time spent on benchmarking and ironing out edge cases.

I'm especially curious how it will do on more "obscure" languages. Not that Cobol is obscure exactly--I suppose there's probably quite a bit of it in GPT-4's training considering how pervasive it is in some domains. In any case, I'll try out this benchmark and see how it goes.