A detailed review of GPT-5.2, covering its Thinking and Pro modes, code generation, vision, long-context capabilities, speed, and comparisons to Claude Opus 4.5 and Gemini 3 Pro.
Over the last year, MCP accomplished a rapid rise to popularity that few other standards or technologies have achieved so quickly. This article details the unlikely rise of the Model Context Protocol (MCP) and its journey to becoming a generally accepted standard for AI connectivity.
LLM Council works together to answer your hardest questions. A local web app that uses OpenRouter to send queries to multiple LLMs, have them review/rank each other's work, and finally a Chairman LLM produces the final response.
OpenAI releases GPT-5.1 Instant and GPT-5.1 Thinking, upgrades to the GPT-5 series focusing on improved intelligence, conversational style, and customization options for ChatGPT. Includes new tone presets and the ability to fine-tune characteristics.
OpenAI releases gpt-oss-safeguard, an open-source AI model for content moderation that allows developers to define their own safety policies instead of relying on pre-trained models. It operates by reasoning about content based on custom policies, offering a more flexible and nuanced approach to moderation.
An in-depth look at the architecture of OpenAI's GPT-OSS models, detailing tokenization, embeddings, transformer blocks, Mixture of Experts, attention mechanisms (GQA and RoPE), and quantization techniques.
This paper explains that hallucinations in large language models (LLMs) aren’t due to flawed data, but to the way these models are trained and evaluated. LLMs are incentivized to guess rather than admit uncertainty, leading to errors that are statistically predictable. The authors frame this as a binary classification problem – correctly identifying valid outputs – and demonstrate a link between misclassification rate and hallucination rate. They argue that fixing this requires a shift in evaluation metrics, moving away from rewarding overconfidence and towards accepting uncertainty, to build more trustworthy models.
This article provides a practical guide to JSON prompting for Large Language Models (LLMs), demonstrating how structuring prompts with JSON improves consistency, accuracy, and scalability. It includes Python coding examples comparing free-form and JSON prompts, and provides access to full code notebooks.
This tutorial explores implementing the LLM Arena-as-a-Judge approach to evaluate large language model outputs using head-to-head comparisons. It demonstrates using OpenAI’s GPT-4.1 and Gemini 2.5 Pro, judged by GPT-5, in a customer support scenario.
OpenAI's release of GPT-OSS marks their first major open source LLM since GPT-2, featuring improvements in reasoning, tool usage, and problem-solving capabilities. The article explores its architecture, message formatting, reasoning modes, and tokenizer details.
