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Most AI systems today work in turns. You type or speak, the model waits, processes your input, and then responds. That’s the entire interaction loop. Thinking Machines Lab, an AI research lab, is arguing that this model of interaction is a fundamental bottleneck. Thinking Machines Lab team introduced a research preview of a new class […]
The mouse pointer has sat at the center of personal computing for more than half a century. It tracks cursor position. It registers clicks. Beyond that, it does almost nothing. Google DeepMind researchers outlined a set of experimental principles and demos for an AI-enabled pointer that goes considerably further: one that understands not just where
Researchers at Tilde Research have released Aurora, a new optimizer for training neural networks that addresses a structural flaw in the widely-used Muon optimizer. The flaw quietly kills off a significant fraction of MLP neurons during training and keeps them permanently dead. Aurora comes with a 1.1B parameter pretraining experiment, a new state-of-the-art result on
A team of researchers from Meta, Stanford University, and the University of Washington have introduced three new methods that substantially accelerate generation in the Byte Latent Transformer (BLT) — a language model architecture that operates directly on raw bytes instead of tokens. Byte-Level Models Are Slow at Inference To understand what this new research solves,
Scaling large language models (LLMs) is expensive. Every token processed during inference and every gradient computed during training flows through feedforward layers that account for over two-thirds of model parameters and more than 80% of total FLOPs in larger models. A team researchers from Sakana AI and NVIDIA have worked on a new research that
Vector databases have graduated from experimental tooling to mission-critical infrastructure. In 2026, vector databases serve as the core retrieval layer for RAG pipelines, semantic search systems, and agentic AI workflows — and choosing the wrong one has real cost and performance consequences. This guide breaks down the top vector databases available today, covering architecture, performance,
NVIDIA AI researchers recently released cuda-oxide, an experimental compiler that allows developers to write CUDA SIMT (Single Instruction, Multiple Threads) GPU kernels in standard Rust code. The project compiles Rust directly to PTX (Parallel Thread Execution) — the assembly-like intermediate representation that CUDA uses to target NVIDIA GPUs — without requiring domain-specific languages, foreign function
Training a family of large language models (LLMs) has always come with a painful multiplier: every model variant in the family—whether 8B, 30B, or 70B—typically requires its own full training run, its own storage, and its own deployment stack. For a dev team running inference at scale, this means multiplying compute costs by the number
If you have spent time using AI coding agents — GitHub Copilot, Claude Code, Gemini CLI — you have probably run into this situation: you describe what you want, the agent generates a block of code that looks correct, compiles, and then subtly misses the actual intent. This “vibe-coding” approach can work for quick prototypes
OpenAI has launched a Codex Chrome extension for Mac and PC to streamline browser-based workflows that were previously difficult to handle via APIs or plugins. This release follows a trend where most users preferred working in a browser after the launch of “Computer Use,” allowing Codex to operate more effectively across various web-based tasks. What
