How Boston became central to Takeda’s data strategy For Takeda’s teams in Boston, the push toward healthcare AI started with a simple realization: most innovation fails long before the model stage. It fails in disconnected systems, inconsistent data, and unclear ownership. In our case study, we explore how Takeda is modernizing fragmented systems so teams…
NVIDIA has released VIBETENSOR, an open-source research system software stack for deep learning. VIBETENSOR is generated by LLM-powered coding agents under high-level human guidance. The system asks a concrete question: can coding agents generate a coherent deep learning runtime that spans Python and JavaScript APIs down to C++ runtime components and CUDA memory management and…
In this tutorial, we implement an agentic chain-of-thought pruning framework that generates multiple reasoning paths in parallel and dynamically reduces them using consensus signals and early stopping. We focus on improving reasoning efficiency by reducing unnecessary token usage while preserving answer correctness, demonstrating that self-consistency and lightweight graph-based agreement can serve as effective proxies for…
The digital fortress: How Citi is engineering the future from Austin As we head into early 2026, the intersection of high finance and high tech has found a permanent home in Austin’s “Silicon Hills.” While many global institutions are still untangling the realities of post-pandemic work, Citi has taken a different approach. It has doubled…
If you have ever tried to understand how the mind works, you know it rarely behaves as neatly as we imagine. Thoughts do not arrive in tidy rows. Memories can drift, bend, or quietly change shape. A scent can pull a forgotten childhood moment into focus. A sentence we only half-heard can emerge altered by…
Qwen team has just released Qwen3-Coder-Next, an open-weight language model designed for coding agents and local development. It sits on top of the Qwen3-Next-80B-A3B backbone. The model uses a sparse Mixture-of-Experts (MoE) architecture with hybrid attention. It has 80B total parameters, but only 3B parameters are activated per token. The goal is to match the…
In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a behavior dataset from a constrained policy, and then train both a Behavior Cloning baseline and a Conservative Q-Learning agent using d3rlpy. By structuring the workflow around offline…
Frontier multimodal models usually process an image in a single pass. If they miss a serial number on a chip or a small symbol on a building plan, they often guess. Google’s new Agentic Vision capability in Gemini 3 Flash changes this by turning image understanding into an active, tool using loop grounded in visual…
Google has introduced Conductor, an open source preview extension for Gemini CLI that turns AI code generation into a structured, context driven workflow. Conductor stores product knowledge, technical decisions, and work plans as versioned Markdown inside the repository, then drives Gemini agents from those files instead of ad hoc chat prompts. From chat based coding…
NVIDIA has released Nemotron-Nano-3-30B-A3B-NVFP4, a production checkpoint that runs a 30B parameter reasoning model in 4 bit NVFP4 format while keeping accuracy close to its BF16 baseline. The model combines a hybrid Mamba2 Transformer Mixture of Experts architecture with a Quantization Aware Distillation (QAD) recipe designed specifically for NVFP4 deployment. Overall, it is an ultra-efficient…