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Google ADK Multi-Agent Pipeline Tutorial: Data Loading, Statistical Testing, Visualization, and Report Generation in Python

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In this tutorial, we build an advanced data analysis pipeline using Google ADK and organize it as a practical multi-agent system for real analytical work. We set up the environment, configure secure API access, create a centralized data store, and define specialized tools for loading data, exploring datasets, running statistical tests, transforming tables, generating visualizations, […]

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Google AI Research Proposes Vantage: An LLM-Based Protocol for Measuring Collaboration, Creativity, and Critical Thinking

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Standardized tests can tell you whether a student knows calculus or can parse a passage of text. What they cannot reliably tell you is whether that student can resolve a disagreement with a teammate, generate genuinely original ideas under pressure, or critically dismantle a flawed argument. These are the so-called durable skills — collaboration, creativity,

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A Step-by-Step Coding Tutorial on NVIDIA PhysicsNeMo: Darcy Flow, FNOs, PINNs, Surrogate Models, and Inference Benchmarking

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In this tutorial, we implement NVIDIA PhysicsNeMo on Colab and build a practical workflow for physics-informed machine learning. We start by setting up the environment, generating data for the 2D Darcy Flow problem, and visualizing the physical fields to clearly understand the learning task. From there, we implement and train powerful models such as the

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An Implementation Guide to Building a DuckDB-Python Analytics Pipeline with SQL, DataFrames, Parquet, UDFs, and Performance Profiling

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In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with the fundamentals of connection management and data generation, then move into real analytical workflows, including querying Pandas, Polars, and Arrow objects without manual loading, transforming results across multiple formats, and writing

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MiniMax Releases MMX-CLI: A Command-Line Interface That Gives AI Agents Native Access to Image, Video, Speech, Music, Vision, and Search

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MiniMax, the AI research company behind the MiniMax omni-modal model stack, has released MMX-CLI — Node.js-based command-line interface that exposes the MiniMax AI platform’s full suite of generative capabilities, both to human developers working in a terminal and to AI agents running in tools like Cursor, Claude Code, and OpenCode. What Problem Is MMX-CLI Solving?

MiniMax Releases MMX-CLI: A Command-Line Interface That Gives AI Agents Native Access to Image, Video, Speech, Music, Vision, and Search Read More »

A Hands-On Coding Tutorial for Microsoft VibeVoice Covering Speaker-Aware ASR, Real-Time TTS, and Speech-to-Speech Pipelines

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In this tutorial, we explore Microsoft VibeVoice in Colab and build a complete hands-on workflow for both speech recognition and real-time speech synthesis. We set up the environment from scratch, install the required dependencies, verify support for the latest VibeVoice models, and then walk through advanced capabilities such as speaker-aware transcription, context-guided ASR, batch audio

A Hands-On Coding Tutorial for Microsoft VibeVoice Covering Speaker-Aware ASR, Real-Time TTS, and Speech-to-Speech Pipelines Read More »

Meta AI and KAUST Researchers Propose Neural Computers That Fold Computation, Memory, and I/O Into One Learned Model

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Researchers from Meta AI and the King Abdullah University of Science and Technology (KAUST) have introduced Neural Computers (NCs) — a proposed machine form in which a neural network itself acts as the running computer, rather than as a layer sitting on top of one. The research team presents both a theoretical framework and two

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A Coding Implementation of MolmoAct for Depth-Aware Spatial Reasoning, Visual Trajectory Tracing, and Robotic Action Prediction

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In this tutorial, we walk through MolmoAct step by step and build a practical understanding of how action-reasoning models can reason in space from visual observations. We set up the environment, load the model, prepare multi-view image inputs, and explore how MolmoAct produces depth-aware reasoning, visual traces, and actionable robot outputs from natural language instructions.

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MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2

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MiniMax has officially open-sourced MiniMax M2.7, making the model weights publicly available on Hugging Face. Originally announced on March 18, 2026, MiniMax M2.7 is the MiniMax’s most capable open-source model to date — and its first model to actively participate in its own development cycle, a meaningful shift in how large language models are built

MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 Read More »

Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference

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Liquid AI just released LFM2.5-VL-450M, an updated version of its earlier LFM2-VL-450M vision-language model. The new release introduces bounding box prediction, improved instruction following, expanded multilingual understanding, and function calling support — all within a 450M-parameter footprint designed to run directly on edge hardware ranging from embedded AI modules like NVIDIA Jetson Orin, to mini-PC

Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference Read More »