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Quantum computing has spent years living in the future tense. Hardware has improved, research has compounded, and venture dollars have followed — but the gap between a quantum processor running in a lab and one running a real-world application remains stubbornly wide. NVIDIA moved to close that gap with the launch of NVIDIA Ising, the […]
Anthropic has launched Claude Opus 4.7, it’s latest frontier model and a direct successor to Claude Opus 4.6. The release is positioned as a focused improvement rather than a full generational leap, but the gains it delivers are substantial in the areas that matter most to developers building real-world AI-powered applications: agentic software engineering, multimodal
If you have ever stared at thousands of lines of integration test logs wondering which of the sixteen log files actually contains your bug, you are not alone — and Google now has data to prove it. A team of Google researchers introduced Auto-Diagnose, an LLM-powered tool that automatically reads the failure logs from a
In this tutorial, we explore how to run OpenAI’s open-weight GPT-OSS models in Google Colab with a strong focus on their technical behavior, deployment requirements, and practical inference workflows. We begin by setting up the exact dependencies needed for Transformers-based execution, verifying GPU availability, and loading openai/gpt-oss-20b with the correct configuration using native MXFP4 quantization,
Table of contentsWhat Is AI Red Teaming?Top 19 AI Red Teaming Tools (2026)Conclusion What Is AI Red Teaming? AI Red Teaming is the process of systematically testing artificial intelligence systems—especially generative AI and machine learning models—against adversarial attacks and security stress scenarios. Red teaming goes beyond classic penetration testing; while penetration testing targets known software
Top 19 AI Red Teaming Tools (2026): Secure Your ML Models Read More »
In this tutorial, we explore how to build a fully functional background task processing system using Huey directly, without relying on Redis. We configure a SQLite-backed Huey instance, start a real consumer in the notebook, and implement advanced task patterns, including retries, priorities, scheduling, pipelines, locking, and monitoring via signals. As we move step by
The open-source AI landscape has a new entry worth paying attention to. The Qwen team at Alibaba has released Qwen3.6-35B-A3B, the first open-weight model from the Qwen3.6 generation, and it is making a compelling argument that parameter efficiency matters far more than raw model size. With 35 billion total parameters but only 3 billion activated
Drug discovery is one of the most expensive and time-consuming endeavors in human history. It takes roughly 10 to 15 years to go from target discovery to regulatory approval for a new drug in the United States. Most of that time is spent not in breakthrough moments, but in painstaking analytical work — sifting through
The intersection of many-body physics and deep learning has opened a new frontier: Neural Quantum States (NQS). While traditional methods struggle with high-dimensional frustrated systems, the global attention mechanism of Transformers provides a powerful tool for capturing complex quantum correlations. In this tutorial, we implement a research-grade Variational Monte Carlo (VMC) pipeline using NetKet and
Building Transformer-Based NQS for Frustrated Spin Systems with NetKet Read More »
The dominant recipe for building better language models has not changed much since the Chinchilla era: spend more FLOPs, add more parameters, train on more tokens. But as inference deployments consume an ever-growing share of compute and model deployments push toward the edge, researchers are increasingly asking a harder question — can you scale quality
