rusht

One thing to note is the license: AGPL vs Apache.

Really appreciate this great, detailed answer! 100% agree with getting to an MVP with PMF as quickly as possible should be the top priority for a startup.

If you had to do it all over again, would you still start with MongoDB or would you go with Cassandra right off the bat?

I am assuming they want to provide a hosted service just like Discord at some point.

ScyllaDB looks interesting but that AGPL license is a no go for some organizations.

“Best” depends on your use case. I don’t even think a SQL database is an option for a chat app since it’s write heavy. FB Messenger uses HBase and Discord uses Cassandra and they’ve done their research at scale, so those could be possible options.

Curious why you decided to use MongoDB as opposed to HBase or Cassandra? Discord moved away from MongoDB because its sharding is “complicated to use and not known for stability.” Are these issues not relevant anymore?

I’d be interested in OP’s answer here as well.

I know dapr injects a sidecar so you can code in any language and then use their SDK to call other microservices. Encore looks to be Go centric and uses code generation to create actual Go functions, which provides much better IDE support.

I did not see this in the docs but I’m not sure if Encore supports retries with or without exponential backoffs; dapr supports both.

Thanks! You can see the estimated hourly cost and list of required resources for each cloud provider in our "Cluster information" section [0]. Would be happy to talk about the product and the effort, I will reach out to you to discuss.

[0] https://docs.onepanel.ai/docs/deployment/cluster/cluster

Hey HN,

I am Rush, one of the makers of Onepanel. Onepanel is an open source, production scale vision AI platform, with fully integrated components for model building, automated labeling, data processing and model training pipelines.

We built Onepanel to significantly reduce the complexities with managing infrastructure and disparate tooling and at the same time allow teams to easily integrate their own tools into reproducible pipelines.

Under the hood, we integrate our own and other best of breed open source components [0] to provide a seamless user experience and abstract away infrastructure complexities that come with running parallelized data processing and training pipelines on different cloud providers. We leverage Kubernetes and deploy cloud provider specific components for networking, network policies, auto scaling, automated TLS certificate provisioning, logging, GPU plugins and more [1].

Our near future goals are to add APIs for inference and VNC enabled workspaces [2] so teams can also run simulation environments inside of Onepanel.

We're excited to share Onepanel with the HN community and look forward to hearing your feedback! And of course we welcome and encourage any contributions [3].

[0] https://github.com/onepanelio/core#acknowledgments

[1] https://github.com/onepanelio/manifests

[2] https://github.com/onepanelio/templates/tree/master/workspac...

[3] https://github.com/onepanelio/core#contributing

Hey HN,

I am Rush, one of the makers of Onepanel. Onepanel is a Kubernetes-native deep learning platform for computer vision with fully integrated components for model building, semi-automated labeling, data processing and model training pipelines.

We built Onepanel to significantly reduce the complexities with infrastructure and disparate tooling so teams can be productive at every step of their workflow but at the same time have the flexibility to change them and bring their own tools.

Under the hood, we integrate our own and other best of breed open source components [0] to provide a seamless user experience. We also try to abstract some of the complexities of Kubernetes by deploying cloud provider specific components for networking, network policies, automated TLS certificates, logging, GPU plugins and more [1].

Our near future goals are to add serverless APIs for inference and VNC enabled workspaces [2] so teams can also run simulation environments inside of Onepanel.

We're excited to share Onepanel with the HN community and would love to hear your feedback! And of course we welcome and encourage any contributions [3].

[0] https://github.com/onepanelio/core#acknowledgments

[1] https://github.com/onepanelio/manifests

[2] https://github.com/onepanelio/templates/tree/master/workspac...

[3] https://github.com/onepanelio/core#contributing

Link to the HuggingFace model contributed by Microsoft: https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-u...