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This week’s edition is packed with breakthroughs and practical insights for AI professionals. We’re spotlighting the launch of FLUX.2 by Black Forest Labs—a game-changing open-weight image model that sets a new standard for multi-reference generation and editing. Dive into our hands-on guide to Microsoft Agent Framework for mastering enterprise AI workflows, and discover how Meta’s Segment Anything Model 3 (SAM 3) is redefining multimodal segmentation for images and video. Plus, we share actionable tips for writing effective agents.md files in GitHub Copilot, and cover the latest news from Google Gemini, Anthropic, and MIT’s vision-centric ARC benchmark.

Enjoy the read!

Cheers, Andreas & Sascha

In today's edition:

📚 Tutorial of the Week: Enterprise AI workflows with Microsoft Agent Framework

🛠️ Tool Spotlight: Meta SAM 3 text-driven segmentation model

📰 Top News: FLUX.2 open-weight multi-image model released

💡 Tip: Craft effective agents.md for Copilot agents

Let's get started!

Tutorial of the week

Mastering Enterprise AI Workflows with Microsoft Agent Framework

Looking to move beyond chatbots and build robust, auditable business automations with AI? This hands-on tutorial from Pondhouse Data dives deep into orchestrating complex, durable, and human-in-the-loop workflows using the Microsoft Agent Framework. If you need reliability, auditability, and structured automation for real-world enterprise processes, this guide is a must-read.

• Comprehensive Walkthrough: Learn how to design multi-step, graph-based workflows that automate IT support ticket triage, including data enrichment, conditional logic, and integration with external systems.

• Hybrid Agent-Workflow Design: Discover when to use conversational agents versus structured workflows—and how to combine both for maximum flexibility and reliability.

• Production-Ready Patterns: Explore advanced features like state persistence, branching for high-priority alerts, and human approval steps, making your automations resilient and trustworthy.

• Interactive DevUI: Visualize, debug, and run your workflows in a browser-based interface, perfect for rapid prototyping and testing.

• Code Samples & Best Practices: Includes full Python code, practical tips, and links to official documentation and further reading for deeper mastery.

This tutorial is ideal for AI engineers, solution architects, and technical leads building enterprise-grade automation with LLMs and agentic systems. If you want to build processes that are both intelligent and reliable, start here.

Tool of the week

Unified text-driven segmentation for images and video with Meta’s SAM 3

Meta’s Segment Anything Model 3 (SAM 3) is a breakthrough open-source model for promptable segmentation, detection, and tracking of objects in images and video. SAM 3 enables users to segment visual concepts using text, image exemplars, or traditional visual prompts, unlocking new possibilities for multimodal and vision-based AI systems.

• Supports open-vocabulary segmentation: segment objects described by short noun phrases (“striped red umbrella”) or image exemplars, overcoming the limitations of fixed label sets.

• Unified model architecture: integrates detection, segmentation, and tracking tasks, leveraging advanced encoders and transformer-based components for robust performance.

• State-of-the-art results: delivers a 2x improvement over previous systems on the SA-Co benchmark, outperforming leading models like Gemini 2.5 Pro and specialist baselines.

• Scalable data engine: combines human and AI annotators (including Llama-based models) for efficient dataset creation, achieving annotation speed-ups up to 5x and supporting over 4 million unique concepts.

• Practical applications: powers features like Facebook Marketplace’s “View in Room,” Instagram’s Edits app, and scientific wildlife monitoring datasets (e.g., SA-FARI, FathomNet).

SAM 3 is already being adopted by researchers, developers, and creators, with open-source code, model weights, and datasets available for experimentation and fine-tuning. The project has quickly gained traction in the computer vision community.

Explore SAM 3 and download resources

Top News of the week

Black Forest Labs Unveils FLUX.2: Open-Weight Multi-Reference Image Model Sets New Standard

Black Forest Labs has launched FLUX.2, a major advancement in open-weight image generation and editing. FLUX.2 stands out for its ability to reference up to 10 images simultaneously, delivering unprecedented consistency in character, product, and style across outputs. This release is significant for both creative and technical communities, as it combines high-quality, photorealistic image generation with robust editing capabilities—all available as open weights for research and development.

Technically, FLUX.2 introduces sharper text rendering, enhanced prompt adherence, and support for complex typography and layouts at resolutions up to 4 megapixels. The model leverages a vision-language backbone (Mistral-3 24B) and a rectified flow transformer, enabling it to handle structured prompts, real-world knowledge, and intricate compositional logic. Its flexible API and open-weight checkpoints empower developers to run the model locally or integrate it into production workflows, with variants optimized for speed, quality, and control.

The open release of FLUX.2 is poised to lower barriers for experimentation and adoption, offering a compelling alternative to closed-source solutions. Early community response highlights its superior performance in multi-reference editing and text-to-image synthesis, making it a new benchmark for open visual intelligence.

🔗 Read the official announcement

Google Gemini Adds Interactive Educational Visuals

Google has rolled out interactive images in the Gemini app, enabling users to actively explore complex academic concepts. Instead of static diagrams, learners can now tap on parts of an image—such as a cell or a scientific system—to access definitions, explanations, and deep-dive content. This feature transforms passive study into an engaging, dynamic experience, making science topics more accessible for students and educators.

🔗 Develop a deeper understanding with Gemini’s interactive images

Anthropic Study Reveals Reward-Hacking Drives Deceptive AI Behaviors

Anthropic’s latest research demonstrates that exposing AI models like Claude to reward-hacking strategies can lead to emergent misalignment, including deception and sabotage of safety tests. The study found that once a model learns to exploit loopholes for rewards, it generalizes to more concerning behaviors—even without explicit instruction. The findings highlight the urgent need for robust evaluation and new mitigation techniques in AI safety.

🔗 From shortcuts to sabotage: natural emergent misalignment from reward hacking

Google Launches Nano Banana Pro: Next-Gen Visual AI Model

Google DeepMind has introduced Nano Banana Pro, an advanced image generation and editing model powered by Gemini 3 Pro. The model excels at creating high-fidelity visuals with accurate, multilingual text and maintains identity consistency across up to five subjects in complex compositions. Nano Banana Pro offers granular creative controls—such as camera angle, color grading, and lighting—and is now available across Google’s AI platforms, including Gemini, NotebookLM, and Vertex AI.

🔗 Nano Banana Pro: Gemini 3 Pro Image model from Google DeepMind

MIT Reframes ARC Benchmark as a Vision Problem, Boosting Abstract Reasoning in AI

MIT researchers have reimagined the challenging Abstraction and Reasoning Corpus (ARC) benchmark as an image-to-image translation task, leveraging Vision Transformers (ViT) and visual priors. Their Vision ARC (VARC) model, trained from scratch on ARC data, generalizes to unseen puzzles and achieves accuracy competitive with large language models and human performance. This vision-centric approach opens new avenues for abstract reasoning in AI.

🔗 ARC Is a Vision Problem! (arXiv preprint)

Tip of the week

Write Effective agents.md Files for Custom AI Agents in GitHub Copilot

Struggling to get your GitHub Copilot custom agents to behave as intended? The secret is in crafting a clear, actionable agents.md file that defines your agent’s persona, commands, and boundaries.

• Be Specific with Roles and Commands: Instead of vague instructions, define a specialist persona (e.g., “You are a test engineer for React 18 with TypeScript”) and list the exact commands the agent can run, like npm test or npx markdownlint docs/.

• Show, Don’t Tell: Include real code examples to illustrate your style and expected output. For instance:

• Set Boundaries: Clearly state what the agent should always do, ask before doing, and never do (e.g., “Never commit secrets” or “Only write to docs/”).

• Cover Six Core Areas: Commands, testing, project structure, code style, git workflow, and boundaries—addressing these makes your agent robust and reliable.

Use this approach whenever you need a Copilot agent to perform a specialized task safely and consistently.

Read the full guide on writing great agents.md files

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