• A Coding Guide to Build an Autonomous Multi-Agent Logistics System with Route Planning, Dynamic Auctions, and Real-Time Visualization Using Graph-Based Simulation

    December 29, 2025

    In this tutorial, we build an advanced, fully autonomous logistics simulation in which multiple smart delivery trucks operate within a dynamic city-wide road network. We design the system so that each truck behaves as an agent capable of bidding on delivery orders, planning optimal routes, managing battery levels, seeking charging stations, and maximizing profit through self-interested decision-making. Through each code snippet, we explore how agentic behaviors emerge from simple rules, how competition shapes order allocation, and how a graph-based world enables realistic movement, routing, and resource constraints. Check out the FULL CODES here. import networkx as nx import matplotlib.pyplot as plt…

  • MiniMax Releases M2.1: An Enhanced M2 Version with Features like Multi-Coding Language Support, API Integration, and Improved Tools for Structured Coding

    December 29, 2025

    Just months after releasing M2—a fast, low-cost model designed for agents and code—MiniMax has introduced an enhanced version: MiniMax M2.1. M2 already stood out for its efficiency, running at roughly 8% of the cost of Claude Sonnet while delivering significantly higher speed. More importantly, it introduced a different computational and reasoning pattern, particularly in how the model structures and executes its thinking during complex code and tool-driven workflows. M2.1 builds on this foundation, bringing tangible improvements across key areas: better code quality, smarter instruction following, cleaner reasoning, and stronger performance across multiple programming languages. These upgrades extend the original strengths…

  • A Coding Implementation on Building Self-Organizing Zettelkasten Knowledge Graphs and Sleep-Consolidation Mechanisms

    December 29, 2025

    In this tutorial, we dive into the cutting edge of Agentic AI by building a “Zettelkasten” memory system, a “living” architecture that organizes information much like the human brain. We move beyond standard retrieval methods to construct a dynamic knowledge graph where an agent autonomously decomposes inputs into atomic facts, links them semantically, and even “sleeps” to consolidate memories into higher-order insights. Using Google’s Gemini, we implement a robust solution that addresses real-world API constraints, ensuring our agent stores data and also actively understands the evolving context of our projects. Check out the FULL CODES here. !pip install -q -U google-generativeai…

  • From Gemma 3 270M to FunctionGemma, How Google AI Built a Compact Function Calling Specialist for Edge Workloads

    December 29, 2025

    Google has released FunctionGemma, a specialized version of the Gemma 3 270M model that is trained specifically for function calling and designed to run as an edge agent that maps natural language to executable API actions. But, What is FunctionGemma? FunctionGemma is a 270M parameter text only transformer based on Gemma 3 270M. It keeps the same architecture as Gemma 3 and is released as an open model under the Gemma license, but the training objective and chat format are dedicated to function calling rather than free form dialogue. The model is intended to be fine tuned for specific function…

  • How to Build Production-Grade Agentic Workflows with GraphBit Using Deterministic Tools, Validated Execution Graphs, and Optional LLM Orchestration

    December 29, 2025

    In this tutorial, we build an end-to-end, production-style agentic workflow using GraphBit that demonstrates how graph-structured execution, tool calling, and optional LLM-driven agents can coexist in a single system. We start by initializing and inspecting the GraphBit runtime, then define a realistic customer-support ticket domain with typed data structures and deterministic, offline-executable tools. We show how these tools can be composed into a reliable, rule-based pipeline for classification, routing, and response drafting, and then elevate that same logic into a validated GraphBit workflow in which agent nodes orchestrate tool usage via a directed graph. Throughout the tutorial, we keep the…

  • How to Build Production-Grade Agentic Workflows with GraphBit Using Deterministic Tools, Validated Execution Graphs, and Optional LLM Orchestration

    December 29, 2025

    In this tutorial, we build an end-to-end, production-style agentic workflow using GraphBit that demonstrates how graph-structured execution, tool calling, and optional LLM-driven agents can coexist in a single system. We start by initializing and inspecting the GraphBit runtime, then define a realistic customer-support ticket domain with typed data structures and deterministic, offline-executable tools. We show how these tools can be composed into a reliable, rule-based pipeline for classification, routing, and response drafting, and then elevate that same logic into a validated GraphBit workflow in which agent nodes orchestrate tool usage via a directed graph. Throughout the tutorial, we keep the…

  • Liquid AI’s LFM2-2.6B-Exp Uses Pure Reinforcement Learning RL And Dynamic Hybrid Reasoning To Tighten Small Model Behavior

    December 29, 2025

    Liquid AI has introduced LFM2-2.6B-Exp, an experimental checkpoint of its LFM2-2.6B language model that is trained with pure reinforcement learning on top of the existing LFM2 stack. The goal is simple, improve instruction following, knowledge tasks, and math for a small 3B class model that still targets on device and edge deployment. Where LFM2-2.6B-Exp Fits in the LFM2 Family? LFM2 is the second generation of Liquid Foundation Models. It is designed for efficient deployment on phones, laptops, and other edge devices. Liquid AI describes LFM2 as a hybrid model that combines short range LIV convolution blocks with grouped query attention…

  • NVIDIA AI Researchers Release NitroGen: An Open Vision Action Foundation Model For Generalist Gaming Agents

    December 29, 2025

    NVIDIA AI research team released NitroGen, an open vision action foundation model for generalist gaming agents that learns to play commercial games directly from pixels and gamepad actions using internet video at scale. NitroGen is trained on 40,000 hours of gameplay across more than 1,000 games and comes with an open dataset, a universal simulator, and a pre trained policy. https://nitrogen.minedojo.org/assets/documents/nitrogen.pdf Internet scale video action dataset The NitroGen pipeline starts from publicly available gameplay videos that include input overlays, for example gamepad visualizations that streamers place in a corner of the screen. The research team collects 71,000 hours of raw…

  • NVIDIA AI Researchers Release NitroGen: An Open Vision Action Foundation Model For Generalist Gaming Agents

    December 29, 2025

    NVIDIA AI research team released NitroGen, an open vision action foundation model for generalist gaming agents that learns to play commercial games directly from pixels and gamepad actions using internet video at scale. NitroGen is trained on 40,000 hours of gameplay across more than 1,000 games and comes with an open dataset, a universal simulator, and a pre trained policy. https://nitrogen.minedojo.org/assets/documents/nitrogen.pdf Internet scale video action dataset The NitroGen pipeline starts from publicly available gameplay videos that include input overlays, for example gamepad visualizations that streamers place in a corner of the screen. The research team collects 71,000 hours of raw…

  • How to Build Contract-First Agentic Decision Systems with PydanticAI for Risk-Aware, Policy-Compliant Enterprise AI

    December 29, 2025

    In this tutorial, we demonstrate how to design a contract-first agentic decision system using PydanticAI, treating structured schemas as non-negotiable governance contracts rather than optional output formats. We show how we define a strict decision model that encodes policy compliance, risk assessment, confidence calibration, and actionable next steps directly into the agent’s output schema. By combining Pydantic validators with PydanticAI’s retry and self-correction mechanisms, we ensure that the agent cannot produce logically inconsistent or non-compliant decisions. Throughout the workflow, we focus on building an enterprise-grade decision agent that reasons under constraints, making it suitable for real-world risk, compliance, and governance…