AI

If you’ve ever stared at thousands of lines of compile tests wondering which sixteen log files contain your error, you’re not alone – and Google now has the data to prove it. Google’s research team was introduced Automatic identificationa powerful LLM tool that automatically reads failure logs from broken integration tests, finds the root cause, … Read more

In this tutorial, we explore how to use the open source OpenAI GPT-OSS The models at Google Colab are focused on their technical behavior, deployment requirements, and intuitive workflows that work. We start by setting up the necessary dependencies for Transformers-based operations, verifying GPU availability, and loading openai/gpt-oss-20b with the correct configuration using the native … Read more

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 adversary attacks in security stress scenarios. The red team passes the classic entrance test; while penetration testing targets known software flaws, the team red checks for AI-specific vulnerabilities, unexpected vulnerabilities, and emerging … Read more

Artificial intelligence is developing rapidly. The minute we get used to one success, another comes along to change our expectations. The new model, the Claude Opus 4.7, introduced by Anthropic recently, is one such change. The rollout often goes beyond just AI chatbots and makes AI a trusted, independent digital partner. Even for developers and … Read more

Even though we know the benefits, many people still use Claude as a glorified Google search. Let me lead by example – “Summarize this in 3 sentences maximum.” Sound familiar? Well, there might be nothing wrong with typing such information, but truth be told, all these characters are good for an article like this, not … Read more

The open source AI space has a new entry worth paying attention to. The Qwen team at Alibaba released the Qwen3.6-35B-A3B, the first open weight model from the Qwen3.6 generation, and it makes a strong argument that parameter efficiency is more important than raw model size. With 35 billion parameters but only 3 billion activated … Read more

Drug discovery is one of the most expensive and time-consuming endeavors in human history. It takes about 10 to 15 years from target discovery to regulatory approval of a new drug in the United States. Most of that time is spent not in moments of success, but in hard analytical work – sorting through mountains … Read more

The intersection of many body physics again deep learning open a new frontier: Neural Quantum States (NQS). While traditional methods struggle with complex systems with high dimensions, the global attention method of Transformers provides a powerful tool to capture complex quantum correlations. In this lesson, we use the research grade Variational Monte Carlo (VMC) pipe … Read more

The question surrounding AI, and I mean the pinnacle of AI, not your typical “email me”, is changing. What used to mean “what can it do for me?” now it has become “who will use it?” We saw this recently with Anthropic’s Claude Mythos Preview – a prototype of AI models that will be shared … Read more

The basic recipe for building better language models hasn’t changed much since the Chinchilla era: use more FLOPs, add more parameters, train on more tokens. But as deployment of inference consumes an ever-increasing share of computing power and deployment of models approaches the edge, researchers are increasingly asking a difficult question – can you scale … Read more