AI Chronicle

  The Chinese AI company, MiniMaxAI, has just launched a large-scale open-source reasoning model, named MiniMax-M1. The model, released on Day 1 of the 5-day MiniMaxWeek event, seems to give a good competition to OpenAI o3, Claude 4, DeepSeke-R1, and other contemporaries. Along with the chatbot, MiniMax has also released an agent in beta version, capable of running code, building apps, creating presentations, and more. In this article, we’ll explore the key features of MiniMax-M1, learn how to access it, and test it out on a few tasks. We’ll also be exploring the MiniMax Agent, so read till the end to watch the agent in action! What is MiniMax-M1? MiniMax‑M1 is an open‑source, large‑scale, hybrid‑attention reasoning model, developed by Shanghai‑based AI startup MiniMax. The thinking model comes with a web search feature and can handle multimodal input in the form of text, images, presentations, and more across various formats. Built on a Mixture of experts (MOE) architecture, the m...

  we implement the Agent2Agent collaborative framework built atop Google’s Gemini models. The guide walks through the creation of specialized AI personas, ranging from data scientists and product strategists to risk analysts and creative innovators. It demonstrates how these agents can exchange structured messages to tackle complex, real-world challenges. By defining clear roles, personalities, and communication protocols, the tutorial highlights how to orchestrate multi-agent problem solving in three phases: individual analysis, cross-agent critique, and synthesis of solutions. import google.generativeai as genai import json import time from dataclasses import data class from typing import Dict , List , Any from enum import Enum import random import re API_KEY = "Use Your Own API Key" genai.configure(api_key=API_KEY) We import the core libraries for building your Agent2Agent system, handling JSON, timing, data structures, and regex utilities. Then, we set your G...