This is a really interesting idea! I'll be honest, it took me a minute to really get what it was doing. The GitHub page video doesn't play with any audio, so it's not clear what's happening.
Once I watched the video, I think I have a better understanding. One thing I would like to see is more of a breakdown of how this solves a problem that just a big model itself wouldn't.
Yeah we rushed to create a "Plexe in action" video for our Readme. We'll put a link to the YouTube video on the Readme so it's easier.
Using large generative models enables fast prototyping, but runs into several issues: generic LLMs have high latency and cost, and fine-tuning/distilling doesn’t address the fundamental size issue. Given these pain points, we realized the solution isn’t bigger generic models (fine-tuned or not), but rather automating the creation, deployment, and management of lightweight models built on domain-specific data. An LLM can detect if an email is malicious, but a classifier built specifically for detecting malicious emails is orders of magnitude smaller and more efficient. Plus, it's easier to retrain with more data.
Just open the root folder in cursor and it'll still do all the stuff for you. Just go build it in MSVC. This is how I build apps. I create an empty project in Xcode, and then I go over to Cursor and have it write all the code. And then I go back to Xcode to build and run it.
Also, if you're using Cursor AI, it seems to have much better integration with Claude where it can reflect on its own things and go off and run commands. I don't see it doing that with Gemini or the O1 models.
One personal niggle: "Code Review For The AI Era". I hate when people say era in relation to AI because it reminds me of Google's tasteless Gemini era thing.
Once I watched the video, I think I have a better understanding. One thing I would like to see is more of a breakdown of how this solves a problem that just a big model itself wouldn't.
reply