The AI landscape is shifting again, beyond brittle prompts and toward deeper intelligence, richer context, and creator-grade multimodality. Google is pushing memory, learning, and image generation to new heights, while the industry moves from prompt hacks to full-stack context engineering.

🧠 Context Engineering > Prompt Engineering – Prompting hit its ceiling. The next wave is context engineering: designing and managing the information AI needs to understand intent, powered by advances in memory, context selection, and lifelong learning.

🎨 Google Nano Banana 2 – The leaked GEMPIX2 model delivers pro-level image generation with 1K–4K output, smarter aspect ratios, iterative refinement, and layer-aware editing for precise, high-fidelity visuals.

🧩 Google Nested Learning – Google’s HOPE model shows how multi-level learning can help AI retain knowledge over time, reducing catastrophic forgetting and enabling more adaptive, human-like intelligence.

From the shift beyond prompts to advanced multimodal creation and architectures that learn continuously, the next phase of AI is not just smarter, it’s contextual, creative, and increasingly human-like in how it understands, remembers, and collaborates.

Why Prompt Engineering Didn’t Scale (And What’s Next)

Context engineering is the systematic process of designing, collecting, managing, and using contextual information to help machines better understand and respond to human intentions, especially in AI and agent systems. It has evolved from early human-computer interaction frameworks, where context was limited to structured inputs, to modern intelligent agents that can interpret natural language, multimodal signals, and even infer user needs proactively. The core challenge is reducing the gap between human and machine understanding by transforming high-entropy, ambiguous human contexts into low-entropy, machine-processable forms. This field is crucial for improving the efficiency and naturalness of human-machine collaboration, with ongoing advances in memory management, context selection, and lifelong context preservation shaping the future of AI systems.​

Google Nano Banana 2 Leaked: Smarter, Faster, Pro-Level AI Images

Google's Nano Banana 2, also known as GEMPIX2 and built on the Gemini 3 Pro Image model, is a next-generation AI image generator and editor designed for professional creators and developers. It enhances the original Nano Banana with higher resolution outputs, including 1K to 4K modes, and supports a wider variety of aspect ratios such as 1:1, 2:3, and 21:9. The model features a new multi-step generation process where it plans, generates, reviews, and iteratively refines images to improve accuracy, especially in text rendering, character consistency, and fine detail. Nano Banana 2 also offers advanced editing controls with layer-aware precision, allowing users to make targeted changes while preserving textures and lighting. It functions as a truly multimodal system that fuses vision and language understanding for seamless, conversational image manipulation. These improvements position Nano Banana 2 as a powerful tool for marketing, design, media production, and creative workflows, promising faster render times and high-fidelity outputs suited for professional-grade use.​

Google’s “Nested Learning” AI That Never Forgets

Google Research has introduced Nested Learning, a new machine learning paradigm that treats a complex model not as a single process but as a system of interconnected, multi-level optimization problems running simultaneously. This approach aims to overcome the major challenge of catastrophic forgetting, where AI models lose prior knowledge when learning new tasks. Nested Learning models different components of a system with their own context and update frequencies, mimicking the human brain's ability to learn continually and retain information over time. Google’s proof-of-concept model called HOPE demonstrates improved long-context memory management and higher accuracy than existing models, marking an important step toward more adaptable AI systems capable of continual learning and closer to human-like intelligence.

Hand Picked Video

In this video, we’ll look at how BG Blur has completely transformed over the past few months — from a simple blur tool to a powerful AI video enhancer. You’ll see the new minimal design, smarter background blur, privacy upgrades, and live preview features in action. Watch till the end to see how this update changes everything for creators!

Top AI Products from this week

• Talo - Talo offers real-time voice translation across video calls, live events, offline presentations and streaming broadcasts. Use bots or the desktop app to translate meetings, or integrate via our API. Make every conversation accessible globally.

• Emma - Emma is AI that understands food globally. It reads any label in any language, uncovers hidden sugars, harmful additives, toxins and allergens, and tells you what’s safe. No databases. No guessing. Just personal health clarity, anywhere.

• YouArt - YouArt is a creative agent that orchestrates models, tools, and prompts into lightweight creative workflows. A Creative Studio built for vibe-driven designers and creators. Build reusable workflows, iterate fast, and export production-ready assets.

• C1 Artifacts API - Let your users generate interactive slides and reports right within your AI app, fully on-brand. Easy to create and edit with prompts, and share with a link. Integrate in just 3 steps or try it as an MCP in your favorite AI app.

• MindPal - Your expertise is your moat. MindPal codifies your decision-making, frameworks, and knowledge into proprietary AI agents and multi-agent workflows that deliver your genius 24/7—to your team and clients. While competitors use personal AI assistants, you're building scalable assets. Join 50k+ experts, coaches, and agencies today.

• Graphis - Graphis is a new kind of platform for creators and agencies to manage AI content projects. Built by creatives, for creatives — it combines an infinity canvas for organizing models and assets with real-time client communication in one seamless flow. No nodes, no chaos — just a clear, human-first way to create, manage, and scale AI-powered work.

This week in AI

• Google Vertex AI Agent Builder - Google Vertex AI Agent Builder enables no-code creation of AI agents with natural language goals, integration with enterprise data, and seamless scaling on Google Cloud.

• Benchmarking World-Model Learning - WorldTest evaluates AI world models in goal-free interaction and challenge phases with AutumnBench, revealing humans outperform current models in prediction, planning, and change detection tasks.

• McKinsey’s report on State of AI in 2025 - Most organizations experiment with AI, but scaling is limited; AI agents gain traction, driving innovation and workflow redesign; impact on enterprise EBIT still growing.

• Grounding Google Maps in Gemini API - Google Maps integrates with Gemini API, letting developers build AI apps grounded in real-time data from 250M+ places for accurate, location-aware, interactive experiences.

• OpenAI GPT-5-Codex Mini Update - OpenAI's GPT-5-Codex-Mini offers 4x more usage with slight performance tradeoff, 50% higher rate limits for major plans, and priority processing for Pro and Enterprise users.

Paper of The Day

The paper "Two Heads are Better than One: Distilling Large Language Model Features Into Small Models with Feature Fusion" proposes a novel method to distill knowledge from large language models into smaller models. By using a feature fusion technique combining multiple layers from the large teacher model, the approach helps the smaller student model retain rich semantic features and improve performance on natural language processing tasks. This technique reduces model size and computational cost while maintaining high accuracy, enabling efficient deployment of language models in resource-constrained environments. The study demonstrates the method’s effectiveness across several benchmark datasets, showing that small models can approach large model capabilities when guided with multi-level feature fusion.

To read the whole paper 👉️ here