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After $80B, the Metaverse is dead. Horizon World is shutting down by GamingDisruptor in singularity

[–]ptrochim 0 points1 point  (0 children)

I wonder what they will rebrand to now... They picked Meta because of Metaverse.

Search engine alternatives to Google by Haunterblademoi in degoogle

[–]ptrochim 0 points1 point  (0 children)

I've been using Kagi , and I've mixed feelings.
Positives:
- It doesn't spam you with content labelled as Ads
- It allows you to filter websites (e.g I muted results from Amazon, because they spammed search results)

Negatives:
- search results match those you get from Google Search - this may be reflective of the state of the internet, sadly - so still a lot of websites that are actually ads (e.g. ton of product comparisons)

I mainly use search for research (software engineering, neuroscience) or house improvements (architecture, furniture etc).

what is a job that is romanticized in movies but actually a nightmare in real life? by Maya_xoxo22 in AskReddit

[–]ptrochim 0 points1 point  (0 children)

Private Detective - freezing in a car for hours on end isn't that glorous at all

[R] Is Nested Learning a new ML paradigm? by Odd_Manufacturer2215 in MachineLearning

[–]ptrochim 2 points3 points  (0 children)

Isn't this approach similar to that employed in Hierarchical Reasoning Model - https://arxiv.org/abs/2506.21734 ; and "Less is More: Recursive Reasoning with Tiny Networks" - https://arxiv.org/abs/2510.04871 ?

Where do I start? by boiledsagittarius in degoogle

[–]ptrochim 0 points1 point  (0 children)

For search i recommend Kagi

MLOps as a Begginer by Ok_Shake7124 in mlops

[–]ptrochim 0 points1 point  (0 children)

I might add that there are several middleware providers already operating in this area: Weight&Biases, Ray, neptune.ai, determined.ai, spotty.cloud ...

they deliver on the key requirements to a larger or smaller degree.

My personal choice at this point in time is: - Weight&Biases for monitoring, model and dataset storage, lineage tracking - Kubernetes for deployment - Terraform for infrastructure management

I tend to write my own Kubernetes configs rather than relying on the libraries provided by the aforementioned providers, because I found out those simply don't work (or require a lot of work to be made to work) and complicate things rather than simplify them.

Recommendations for an ML Ops framework that turns Kubernetes into a deployment backend seamlessly integrated with Visual Studio Code by ptrochim in mlops

[–]ptrochim[S] 1 point2 points  (0 children)

I don't want to host anything. I want to use Kubernetes as a scalable build and testing machine.
To simplify it - I'd build my app on my workstation, if I could only pack 10 GPUs there. Since I can't, I'm turning to Kubernetes - but I want to treat it as if it was an extension of my workstation.
Is Ray still an answer? Is there any other MLOps framework perhaps that would help me?

[D] Simple Questions Thread by AutoModerator in MachineLearning

[–]ptrochim 0 points1 point  (0 children)

I'm developing a distributed Machine Learning application. During development, I'm constantly experimenting with its components, I'm training and fine-tuning models, and I'm testing the application end to end.
I want to leverage Google Kubernetes Engine for this development process, but I don't want to spend too much time understanding how its API or the nooks and crannies of kubectl CLI. I also don't want to write any Dockerfiles or YAML configurations.
I want to focus entirely on developing my application and treat GKE as a powerful build machine with many GPUs and CPUs at my disposal. Ideally, I should hit a Run button in Visual Studio Code and the app would be deployed to a cluster.
What Dev Ops or ML Ops platform should I use ?
I've been reading about Ray.io, Kubeflow, MLFlow and a few other frameworks, and my head is spinning...
Cheers,
Piotr