Agentic AI

Desk of contents The Problem with “Thinking Longer” The Agentic Approach Infrastructure Challenges and Solutions GRPO-RoC: Learning from High-Quality Examples Training Strategy: From Simple to Complex Breakthrough Results Understanding the Mechanisms Summary The Downside with “Pondering Longer” Giant language fashions have made spectacular strides in mathematical reasoning by extending their Chain-of-Thought (CoT) processes—primarily “pondering longer”…

OpenAI has formally launched Realtime API and gpt-realtime, its most superior speech-to-speech mannequin, shifting the Realtime API out of beta with a collection of enterprise-focused options. Whereas the announcement marks actual progress in voice AI know-how, a better examination reveals each significant enhancements and protracted challenges that mood any revolutionary claims. Technical Structure and Efficiency…

We start this tutorial by designing a modular deep analysis system that runs instantly on Google Colab. We configure Gemini because the core reasoning engine, combine DuckDuckGo’s Instantaneous Reply API for light-weight internet search, and orchestrate multi-round querying with deduplication and delay dealing with. We emphasize effectivity by limiting API calls, parsing concise snippets, and…

On this tutorial, we construct a sophisticated AI agent utilizing Semantic Kernel mixed with Google’s Gemini free mannequin, and we run it seamlessly on Google Colab. We begin by wiring Semantic Kernel plugins as instruments, like internet search, math analysis, file I/O, and note-taking, after which let Gemini orchestrate them by way of structured JSON…

We start this tutorial by displaying how we are able to mix MLE-Agent with Ollama to create a completely native, API-free machine studying workflow. We arrange a reproducible atmosphere in Google Colab, generate a small artificial dataset, after which information the agent to draft a coaching script. To make it strong, we sanitize widespread errors,…