Perplexity AI Introduces Hybrid Local-Server Inference Orchestrator for Personal Computer: Automatic On-Device and Cloud Task Routing

by
Perplexity AI

Perplexity AI announces a hybrid local-server inference orchestrator for Personal Computer, automatically routing AI tasks between on-device and cloud models.

The post Perplexity AI Introduces Hybrid Local-Server Inference Orchestrator for Personal Computer: Automatic On-Device and Cloud Task Routing appeared first on MarkTechPost.

Google SensorFM: The Wearable Health AI Trained on One Trillion Minutes of Data

by

SensorFM, a wearable health foundation model from Google Research, Google DeepMind, and university collaborators. We walk through its ViT-1D masked-autoencoder backbone, pretrained on more than one trillion minutes of unlabeled sensor signals from 5,000,000 consented participants. We examine the co-scaling results across four model sizes and four data volumes, including the case where capacity outruns data. We show how frozen embeddings plus a PCA-50 linear probe beat feature-engineered baselines on 34 of 35 tasks. We also review the agentic classroom that searched 30,516 prediction heads, and the clinician evaluation grounding a Personal Health Agent.

The post Google Research Introduces SensorFM: A Wearable Health Foundation Model Pretrained on One Trillion Minutes of Sensor Data appeared first on MarkTechPost.

Mira Murati’s Thinking Machines Lab Makes the Case for Human-Owned AI

by
Mira Maruti's Thinking Machines

Thinking Machines Lab published “The Future Worth Building Is Human.” The essay frames human participation, model ownership, and decentralized alignment as technical challenges. It ties them to interaction models and Tinker’s LoRA fine-tuning, where teams train and keep their own model weights.

The post Mira Murati’s Thinking Machines Lab Makes The Technical Case For Human-Centered AI Built On Customizable Model Weights appeared first on MarkTechPost.

How Netflix Slashed Database Read Times from Seconds to Milliseconds

by
Netflix Casandra

Netflix engineers detailed how they handle wide partitions in Apache Cassandra for the TimeSeries Abstraction. Two approaches work together: Time Slice re-partitioning tunes future partitions at the table level, while dynamic partitioning detects and splits oversized partitions per TimeSeries ID on the read path. Detection runs via byte counting and Kafka, splits are checksum-validated, and Bloom filters route reads to parallel child partitions. Average read latency dropped from seconds to low double-digit milliseconds, with 500MB+ partitions staying available.

The post Netflix AI Team Cuts Wide-Partition Read Latency from Seconds to Milliseconds by Splitting Cassandra Partitions Per ID appeared first on MarkTechPost.