agree. i do believe authors of those experimental projects do not intent to create such buzz, it is just too many shitinfluencers pushing the projects in that way.
Strikes me that some sort of inflection point has been reached in terms of buzz, hype, desperateness to make money and get out of the rat race, snake oil, etc.
We literally watched crypto/web3 do the entire speed run from weird nerd project to spam/ponzi/get rich quick/celebrity/vaporware shell game in a couple years. Like All The Way to stadiums being bought and international manhunts and average people losing lots of money with something they didn't understand to most people being annoyed by bots and influencers to virtually 0% of projects having any other purpose but to generate hype
(with actual ongoing interesting tech being built underneath it)
the fact that autogpt in One Week went from AI experiment to "why doesn't this ship money to my bank account or what is github I'm here for the free money" based on youtube videos and spam articles etc really does note bode well for the hype phase of AI
Starting from 0.5.0 finetuner is no longer an open-source.
> From 0.5.0, Finetuner computing is hosted on Jina Cloud. The last local version is 0.4.1, one can install it via pip or check out git tags/releases here.
But there are some cool ideas implemented there as well, I encourage you to try both!
Unfortunately, it's no longer open source, but requires using their cloud.
"From 0.5.0, Finetuner computing is hosted on Jina Cloud. THe last local version is 0.4.1, one can install it via pip or check out git tags/releases here."
Of course, solving a problem is the most important thing at the end of the day. However, there are some data privacy constraints you may sometimes need to fulfil, and sending the data to an external cloud, managed by 3rd party is not an option.
Moreover, fine-tuning might be just one of the applications of neural networks in the organization, and you may already have some pipelines built to train them, so it should be also unified.
And more importantly, Jina's finetuner gives you some pretrained models to choose from, while Quaterion is PyTorch Lightning based, so you can easily integrate it if you already use PyTorch and have the flexibility to fine-tune any custom network as well.
i think the main focus are different, jina is more on searching multimedia data, image, video things like that - things that can not easy to be solved by classic symbolic search/keyword matching
Jina AI | https://jina.ai | Senior/Lead Python Engineer • Lead Dev Rel Specialist | Remote • Onsite (Berlin/Beijing)
We're building "Tensorflow for search" in open-source. https://github.com/jina-ai/jina We are founded in 2020, backed by GGV Capital, working on Search AI ecosystem. We are expanding our R&D team and looking for engineers, dev-rels who are passionate about neural search, opensource and enjoy working in a cross-border team.
Honor to see our opensource company Jina AI and our opensource neural search framework Jina (https://github.com/jina-ai/jina) inspired another startup. I see quite some similarity between Jina's Protobuf and Pinecones's Protobuf, some even on the comment & naming level. Great starting point for potential synergy.
Honor to see our opensource company Jina AI and our opensource neural search framework Jina (https://github.com/jina-ai/jina) inspired another startup. I see quite some similarity between Jina's Protobuf and Pinecones's Protobuf, some even on the comment & naming level. Great starting point for potential synergy.
Honor to see our opensource company Jina AI and our opensource neural search framework Jina (https://github.com/jina-ai/jina) inspired another startup. I see quite some similarity between Jina's Protobuf and Pinecones's Protobuf, some even on the comment & naming level. Great starting point for potential synergy.
tho dont see anyone building paywall yet