If neural networks are now making decisions everywhere from code editors to safety systems, how can we actually see the specific circuits inside that drive each behavior? OpenAI has introduced a new mechanistic interpretability research study that trains language models to use sparse internal wiring, so that model behavior can be explained using small, explicit…
As AI agents move from single-app copilots to autonomous systems that browse, transact, and coordinate with each other, a new infrastructure layer is emerging underneath them. This article compares six key “agent-native rails” — MCP, A2A, AP2, ACP, x402, and Kite — focusing on how they standardize tool access, inter-agent communication, payment authorization, and settlement,…
In this tutorial, we build an advanced Agentic AI system using spaCy, designed to allow multiple intelligent agents to reason, collaborate, reflect, and learn from experience. We work through the entire pipeline step by step, observing how each agent processes tasks using planning, memory, communication, and semantic reasoning. By the end, we see how the…
How far can we push large language model speed by reusing “free” GPU compute, without giving up autoregressive level output quality? NVIDIA researchers propose TiDAR, a sequence level hybrid language model that drafts tokens with diffusion and samples them autoregressively in a single forward pass. The main goal of this research is to reach autoregressive…
How can developers reliably generate, control, and inspect large volumes of realistic dialogue data without building a custom simulation stack every time? Meet SDialog, an open sourced Python toolkit for synthetic dialogue generation, evaluation, and interpretability that targets the full conversational pipeline from agent definition to analysis. It standardizes how a Dialog is represented and…
How powerful are Diffusion LLMs compared to classic autoregressive LLMs, once you treat generation as an algorithm with time and space complexity, not just as a decoding trick? A new research paper from a team researchers from Toyota Technological Institute at Chicago and MIT gives a formal answer. This new research compares Auto-Regressive Models (ARM),…
In this tutorial, we build a self-verifying DataOps AIAgent that can plan, execute, and test data operations automatically using local Hugging Face models. We design the agent with three intelligent roles: a Planner that creates an execution strategy, an Executor that writes and runs code using pandas, and a Tester that validates the results for…
OpenAI has released GPT-5.1 as the next iteration in the GPT-5 family, with 2 core variants, GPT-5.1 Instant and GPT-5.1 Thinking. The update focuses on 3 axes, adaptive reasoning behavior, clearer explanations, and stronger control over tone and safety. Model Lineup And Positioning GPT-5.1 Instant is the default conversational model in ChatGPT. OpenAI describes it…
In this tutorial, we build our own custom GPT-style chat system from scratch using a local Hugging Face model. We start by loading a lightweight instruction-tuned model that understands conversational prompts, then wrap it inside a structured chat framework that includes a system role, user memory, and assistant responses. We define how the agent interprets…
Maya Research has released Maya1, a 3B parameter text to speech model that turns text plus a short description into controllable, expressive speech while running in real time on a single GPU. What Maya1 Actually Does? Maya1 is a state of the art speech model for expressive voice generation. It is built to capture real…
