Items tagged with “Fb”.
Meta introduces a diversity-aware notification ranking framework that layers diversity controls on top of engagement models to reduce repetition, broaden content variety, and improve click-through rates on Instagram notifications.
Meta brings Kotlin incremental compilation to Buck2, accelerating Kotlin builds with incremental actions, classpath snapshots, and careful plugin integration to deliver faster, more scalable Android toolchains.
Meta outlines an agentic data-access framework that integrates specialized AI agents to streamline access, enforce security, and reduce risk in its warehouse data ecosystem.
Meta introduces Diff Risk Score (DRS), an AI-powered tool that predicts the risk of code changes causing production incidents, enabling safer and more productive software development.
Meta’s Reality Labs is pursuing wrist-worn devices using surface electromyography (sEMG) to enable intuitive control across users. This piece covers the podcast discussion on generalization challenges and the quest for a generic neuromotor interface.
ExecuTorch is Meta’s open-source PyTorch inference framework for on-device ML across its family of apps, delivering lower latency, better privacy, and improved performance on Android and iOS.
Meta details its approach to maintaining reliability in its AI hardware at scale, covering fault categories, detection of silent data corruptions, and strategies that keep large training clusters like Llama 3 operational.
Meta developers unveil open-source AI for sustainable concrete design, optimizing strength, curing speed, and carbon footprint with Bayesian optimization and collaboration with Amrize and U of I.
Meta joins the Kotlin Foundation as a gold member to accelerate Kotlin adoption across Android. The move supports Meta’s Java-to-Kotlin migration, open source tooling, and grants to foster a robust Kotlin ecosystem.