
Building outside the box @rtalabsai
Toronto
Joined December 2011
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There’s a level of intuitive finesse required when engineering harnesses around the capabilities of models available now.
Momentum can be maintained with taste or risk being fumbled without foresight.
If you’re a physics/ML/applied math research connoisseur, like to think outside the box and if you like to venture into the unknown, shoot me a DM.
I’ve been working toward a research concept that’s been ingrained in my head for the last couple of years;
Can learned dynamical systems become the computational substrate for intelligence?
Can intuition emerge from navigating ambiguity rather than always collapsing toward a single precise representation?
Early experiments show promise.
github.com/ps2604 traces my line of thinking along the way.
🌌
Pirassena S retweeted
OMNI is now live on @googlecloud Marketplace.
Businesses can now deploy OMNI's agentic AI infrastructure directly through their existing Google Cloud account and billing.
More in our announcement:
omni.auralith.org/news/omni-…
Pirassena S retweeted
Directional intelligence > General intelligence
Why?
Less token usage
Faster performance
Consistent outputs
omni.rtalabs.org
Pirassena S retweeted
Paper: paperswithcode.co/paper/2605…
Dataset: huggingface.co/datasets/lhpk…
Project: haolpku.github.io/K12-KGraph…







