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I think computer using agents (CUA) are highly underrated right now. Let me explain why

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Building LLM Workflows - - some observations

Is Relevance AI really as effective at building AI agents or teams as some gurus claim? What have you built so far with this platform?

jax-ml

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jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

modelscope

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modelscope

ModelScope: bring the notion of Model-as-a-Service to life.

evidentlyai

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evidently

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

tracel-ai

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burn

Burn is a next generation Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.

skypilot-org

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skypilot

SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 16+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.

microsoft

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onnxruntime

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

autogluon

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autogluon

Fast and Accurate ML in 3 Lines of Code

juliansteenbakker

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mobile_scanner

A universal scanner for Flutter based on MLKit. Uses CameraX on Android and AVFoundation on iOS.

mlfoundations

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open_clip

An open source implementation of CLIP.