CogVLM VS OpenAdapt

CogVLM is very good in my (brief) testing: https://github.com/THUDM/CogVLM

The model weights seem to be under a non-commercial license, not true open source, but it is "open access" as you requested.

increasing growth most places in world

https://twitter.com/elonmusk/status/1743028102446408026

heres a total feature map of what was released in 2023:

https://twitter.com/enriquebrgn/status/1740950767325024387

I think thats definitely a signal that the B and C teams werent needed, considering they cut 90% of staff LOL.

As for the bots, AI is making it easier than ever to bypass those systems. CogVLM is just sitting there menacingly on github https://github.com/THUDM/CogVLM

Jump to heading for benchmarks and examples: https://github.com/THUDM/CogVLM/tree/main?tab=readme-ov-file...

Honestly at this point there is no need for human for captioning except maybe for NSFW content. Img2text is just good enough for nearly all images. GPTVision or open source equivalent (like CogVLM https://github.com/THUDM/CogVLM ) are just good enough.

A core Llama.cpp contributor, named cmp-nct, discovered stumbled upon what might be the next leap forward for vision/language models. CogVLM (which uses a Vicuna 7B language model combined with a 9B vision tower) excels particularly in OCR (Optical Character Recognition), detail detection, and minimal hallucinations. It effectively understands both handwritten and typed text, context, fine details, and background graphics. It even provides pixel coordinates for small visual targets. CovVLM surpasses other models like llava-1.5 and Qwen-VL in performance.

https://github.com/THUDM/CogVLM (really impressive)

I'm researching using LLMs for alt-text suggestion for forum users, can you share your finding so far?

Outside of GPT-4V I had good first results with https://github.com/THUDM/CogVLM

I've got some decent results with CogVLM. Resolution kinda sucks at 490x490, though.

> Rabbit has said the device will be able to learn any app, if you teach it.

We're building this over at https://github.com/OpenAdaptAI/OpenAdapt. OpenAdapt learns to automate tasks in desktop apps by observing human demonstrations.

Early demo: https://twitter.com/abrichr/status/1784307190062342237 (more coming soon!)

The demo is overly simplistic to keep it short -- it also works with arbitrary applications and operations.

Also, we're open source. Contributions and feedback are welcome and encouraged :)

Very interesting, thank you for making this available!

At OpenAdapt (https://github.com/OpenAdaptAI/OpenAdapt) we are looking into using pm4py (https://github.com/pm4py) to extract a process graph from a recording of user actions.

I will look into this more closely. In the meantime, could the authors share their perspective on whether Memary could be useful here?

See https://github.com/OpenAdaptAI/OpenAdapt for an alternative that works with desktop GUIs.

Not mentioned: learning from demonstration. This is the approach we are taking at https://github.com/OpenAdaptAI/OpenAdapt.

Congratulations on shipping!

Check out https://github.com/OpenAdaptAI/OpenAdapt for an open source (MIT license) alternative that also works on desktop (including Citrix!)

https://github.com/mldsai/puterbot is designed for all desktop applications, including browsers. We're also working on a chrome extension to support reading/writing directly to DOM: https://github.com/OpenAdaptAI/OpenAdapt/pull/364