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A model can behave perfectly one moment and degrade the next—without any change to your data, pipeline, or logic. The root cause often lies in something far more subtle: how your input is tokenized. Before a model processes text, it converts it into token IDs, and even minor formatting differences—like spacing, line breaks, or punctuation—can […]
What is Tokenization Drift and How to Fix It? Read More »
Mistral AI has been quietly building one of the more practical coding agent ecosystems in the open-source/weights AI space, and they are shipping its most significant infrastructure upgrade yet. Mistral team announced remote agents in Vibe, its coding agent platform, alongside the public preview of Mistral Medium 3.5 — a new 128B dense model that
In this tutorial, we build a multi-agent workflow for biological systems modeling and explore how different computational components work together inside one unified systems biology pipeline. We generate synthetic biological data, analyze gene regulatory structure, predict protein-protein interactions, optimize metabolic pathway activity, and simulate a dynamic cell signaling cascade, all within a Colab environment that
If you have been running reinforcement learning (RL) post-training on a language model for math reasoning, code generation, or any verifiable task, you have almost certainly stared at a progress bar while your GPU cluster burns through rollout generation. A team of researchers from NVIDIA proposes a precise fix by integrating speculative decoding into the
The bottleneck in building better AI models has never been compute alone — it has always been data quality. Meta AI’s RAM (Reasoning, Alignment, and Memory) team is now addressing that bottleneck directly. Meta researchers have introduced Autodata, a framework that deploys AI agents in the role of an autonomous data scientist, tasked with iteratively
Large language models are remarkably capable, yet frustratingly opaque. When a model misbehaves — generating responses in the wrong language, repeating itself endlessly, or refusing safe requests — AI devs have very few tools to diagnose why it happened at the level of internal computations. That’s the problem Qwen-Scope is built to solve. Qwen Team
In this tutorial, we build the entire Agentic UI stack from the ground up using plain Python, without relying on external frameworks to abstract away the core ideas. We implement the AG-UI event stream to make agent behavior observable in real time, and we bring in A2UI as a declarative layer that allows interfaces to
The team behind Kimi.ai (Moonshot AI) just made a significant contribution to the open-source AI infrastructure space. The research team has made a significant contribution to the open-source AI infrastructure space. They released FlashKDA (Flash Kimi Delta Attention), a high-performance CUTLASS-based kernel implementation of the Kimi Delta Attention (KDA) mechanism. The FlashKDA library is available
Video foundation models can paint a beautiful frame. They are still notoriously bad at remembering it. Push the camera through a corridor in Wan 2.1 or CogVideoX and walls warp, objects morph, and details vanish — the giveaway that these models are fitting 2D pixel correlations rather than simulating a coherent 3D scene. A team
The race to make large language models faster and cheaper to run has largely been fought at two levels: the model architecture and the hardware. But there is a third, often underappreciated frontier — the GPU kernel. A kernel is the low-level computational routine that actually executes a mathematical operation on the GPU. Writing a
