Articles about Large Language Models in Technology & AI
OpenAI just rolled out GPT-5.2, a powerful new model designed for serious professional work. We break down what makes its 'Thinking' and 'Pro' variants so capable at coding, long-running tasks, and complex reasoning.
Google just dropped a bombshell benchmark called FACTS, and the results are a wake-up call. Even top models like Gemini and GPT are struggling to crack 70% accuracy, revealing a critical "factuality wall" for enterprise AI.
Ever feel like AI models have the memory of a goldfish? Google Research is tackling this with Titans and MIRAS, new approaches that give AI a persistent, long-term memory, allowing them to process millions of tokens without forgetting key details.
Ever wonder how an AI model with 47 billion parameters can run faster than one with 13 billion? It's not magic—it's an architecture called Mixture of Experts (MoE). Let's break down how MoE is changing the game.
Chinese AI startup DeepSeek just released two incredibly powerful AI models for free. We break down how their new tech slashes costs and why it could seriously shake up the AI race between the US and China.
We're deploying powerful LLMs without knowing how they truly operate. Learn why AI observability is the crucial SRE layer that turns these black boxes into reliable, trustworthy systems you can actually manage.
Anthropic just dropped Claude Opus 4.5, and it's a big deal. The new AI model is way cheaper, smarter, and somehow managed to outperform every human candidate on the company's own tough engineering test.
Remember the AI model everyone fell in love with? OpenAI is officially retiring the API for GPT-4o in February 2026. We break down why this is happening, the wild story behind its user backlash, and what it means for you.
Ever feel like AI models are getting too big and expensive? Cerebras just released a new model that's 30% smaller but performs almost identically to its massive predecessor. Let's break down how they did it.