In today’s agentic AI environments, the network has a new set of responsibilities. In a traditional application stack, the network mainly moves requests between services. But as discussed in a recent white paper, Cloud Infrastructure in the Agent-Native Era, in an agentic system the network sits in the middle of model calls, tool invocations, agent-to-agent …
Read more “Envoy: A future-ready foundation for agentic AI networking”
Major AI labs are investigating a security incident that impacted Mercor, a leading data vendor. The incident could have exposed key data about how they train AI models.
A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously carried out by artificial systems. By integrating cultured neuronal networks into a machine learning framework, the team showed that these biological systems can generate complex time-series signals, …
Read more “Living brain cells enable machine learning computations”
The biggest change: we integrated model layer streaming across all local inference pipelines, cutting peak VRAM usage enough to run on 16 GB VRAM machines. This has been one of the most requested changes since launch, and it’s live now. What else is in 1.0.3: Video Editor performance: Smooth playback and responsiveness even in heavy …
Read more “LTX Desktop 1.0.3 is live! Now runs on 16 GB VRAM machines”
Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training methods, like Reinforcement Learning with Human Feedback (RLHF), optimize for a single, global objective. While Group Relative Policy Optimization (GRPO) is a widely adopted on-policy reinforcement learning framework, its group-based normalization implicitly assumes that all …
Read more “Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment”
By Renata Teixeira, Zhi Li, Reenal Mahajan, and Wei Wei On January 26, 2026, we flipped an important switch for Live at Netflix: all Live events are now encoded using VBR (Variable Bitrate) instead of CBR (Constant Bitrate). It sounds like a small configuration change, but it required us to revisit some of the foundational assumptions …
Read more “Smarter Live Streaming at Scale: Rolling Out VBR for All Netflix Live Events”
Evaluating single-turn agent interactions follows a pattern that most teams understand well. You provide an input, collect the output, and judge the result. Frameworks like Strands Evaluation SDK make this process systematic through evaluators that assess helpfulness, faithfulness, and tool usage. In a previous blog post, we covered how to build comprehensive evaluation suites for …
Read more “Simulate realistic users to evaluate multi-turn AI agents in Strands Evals”
Building the perfect bra takes thousands of data points. That’s why Honeylove isn’t just another intimates brand. We’re a technology company that happens to make exceptional bras, tops, shapewear, and bodysuits. Technology shapes everything we do, from how we iterate garments based on customer feedback to how we optimize sizing across those thousands of data …
Read more “How Honeylove boosts product quality and service efficiency with BigQuery”
In this episode, we discuss Iran’s threats to target US tech firms, gear up for the midterm elections, and get a scene report from the Polymarket pop-up bar in DC.
Anthropic CEO Dario Amodei has said that AI could surpass “almost all humans at almost everything” shortly after 2027. While AI’s capabilities are certainly improving, such rapid progress might seem at odds with findings that show AI is still failing at 95%+ of remote freelance projects, and continues to struggle with hallucination, long term planning, …