pinglin

It's reported working but not with LM Studio: https://www.reddit.com/r/LocalLLaMA/comments/1j9reim/comment...

Thanks for the reminder.

How does aiPDF compare with AirOps, Stack AI, etc., the kind of flexible LLM workflow apps that can handle PDF input?

This is what I really need! My eyes are very sensitive to sunlight. Good stuff.

I am not sure if this post can pass the algorithm of HN this time. Nonetheless, we are eager to learn feedback about the way we build Instill VDP for its no-code and low-code functionalities. Any comment is very appreciated.

A UDP email contact is a very unreliable and careless way of making communication.

Please make sure you can implement a TCP-type communication with your customers for this kind of critical movement.

Cost me 10 min to solve the puzzle. Quite an enjoyable game! My only feedback is to highlight the condition which blocks the path on the UI.

One can absolutely lock versions in their Dockerfile. I can see that the design principle of DevBox is to pin the versions. At the end of the day we all need to consider versioning (i.e., the image version) the versions (i.e., package versions) anyway.

6 months after we posted the manifesto (http://go.instill.tech/4bcxuf), we're releasing an Alpha version of VDP under the open-source Apache license 2.0.

VDP is the future for unstructured data ETL, where developers won't need to build their own data connectors, high-maintenance model serving platform or ELT pipeline automation tool.

Check out VDP on GitHub: http://go.instill.tech/4f7wrp

Another author here. As VDP is still in alpha, we are very keen to collect feedback and shape its features with the community. Thanks in advance!

VDP supports both real-time and on-demand inference.

Blazingly fast speed or super cost efficiency? It’s on your call.

Real-time inference speed bundles with the genuine Vision AI model performance. You can get the fastest inference result ever with VDP from a model serving’s point of view. Thanks to the integrated Triton Inference Server and the high-performant Go backends.

On-demand inference performs batch operation. You can get the most economic inference cost for non-time-critical vision tasks. Schedule your inference tasks and access the structured data results in your data warehouse later.

Give a star on GitHub : https://lnkd.in/eAdURFfJ Join our Discord : https://lnkd.in/eh-za9MA

Self-hosted Vault within a minimum Kubernetes cluster in GCP costs us roughly $35 a month. Maintenance effort can be neglected if not scaling. Vault has its learning curve there but I think it's totally worth it, given its secret management and API-first features integrated with many other DevOps tools.