DeepLearning.AI

📢 New short course in collaboration with @Oracle! Agent Memory: Building Memory-Aware Agents Learn how to design a memory system that lets AI agents store, retrieve, and refine knowledge across sessions. Taught by @RichmondAlake and Nacho Martínez. Enroll now: hubs.la/Q047ljGB0

🛠️ Builders, show us what you made! If you've completed a project in the Build with Andrew course, we want to feature it. Your work inspires the whole community. Here's how to share: 📌 Go to the @DeepLearningAI Forum: hubs.la/Q0487K9J0 📂 Post in the AI Discussions Category 🏷️ Tag it #build-with-andrew and #ai-discussions Let's see what you've built!

Alibaba released the Qwen3.5 family of open-weights vision-language models, ranging from lightweight to massive systems. Smaller models like Qwen3.5-9B rival or outperform much larger competitors, making multimodal AI more accessible on lightweight hardware while maintaining strong performance across vision and language tasks. Learn more in The Batch: hubs.la/Q0487yjb0

AI teams often think their biggest problem is the model. In practice, the bigger problem is alignment. If different people are optimizing different things, like accuracy, latency, recall, or edge cases, every experiment becomes a debate. One team thinks the system improved. Another thinks it got worse. High-performing AI teams avoid this trap. They agree early on what success looks like. Once that’s clear, experiments stop being arguments and start becoming progress. Learn how to build better AI systems at @DeepLearningAI

This week, in The Batch, Andrew Ng discusses growing job insecurity and uncertainty in an AI-driven world by focusing on stable foundations like strong communities and continuously developing skills. Plus: 👁️ Qwen3.5 models deliver top-tier vision performance, even at small sizes 🌏 DeepSeek withholds access from U.S. chip makers, escalating tensions 🧩 Apple unifies images, video, and 3D objects with a single tokenizer Read The Batch: hubs.la/Q047-T0K0

The #1 Skill Employers Want in 2026 The job market is shifting, and the #1 skill employers are looking for right now isn't a specific programming language—it's teachableness. As AI tools evolve at lightning speed, your ability to adapt and learn is your biggest competitive advantage. AI won’t replace workers, but workers who use AI will replace workers who don’t. Ready to 10x your productivity? @DeepLearningAI offers free short courses on the exact tools employers are looking for right now, including Claude Code, Gemini CLI, and Agentic Skills. Start learning today and build your career advantage: hubs.la/Q047Nc2q0 Subscribe to The Batch newsletter for weekly AI insights: hubs.la/Q047NjtB0

Apple researchers introduced Feature Auto-Encoder (FAE), a diffusion image generator that learns from compressed embeddings produced by a pretrained vision model. By shrinking rich embeddings before reconstruction, the system trains up to seven times faster while maintaining image quality comparable to state-of-the-art diffusion models. Read our summary of the paper in The Batch: hubs.la/Q047M-gw0

A lot of AI conversations focus on chatbots and demos. But some of the most valuable AI systems solve much less glamorous problems. Processing invoices. Extracting information from documents. Connecting data across tools. Making systems reliable enough to use every day. These kinds of operational workflows are where many companies actually see the biggest impact from AI. Here’s a simple path to start learning how these systems are built. Document AI: From OCR to Agentic Doc Extraction hubs.la/Q047N1Ph0 Preprocessing Unstructured Data for LLM Applications hubs.la/Q047N1KF0 Functions, Tools and Agents with LangChain hubs.la/Q047N0h70 Improving Accuracy of LLM Applications hubs.la/Q047N5J60 Know someone exploring how to apply AI in real business workflows? Share this with them.

Companies including Meta and OpenAI are building private gas-powered plants directly connected to data centers to secure the massive electricity needed to power AI infrastructure. The projects bypass grid delays and could provide energy for a large share of future data centers, but they also raise concerns about rising costs and increased greenhouse gas emissions. Lear more in The Batch hubs.la/Q047M2tQ0

Meet @Oracle on the AI Dev x SF show floor or join their workshop to explore the latest thinking on agent memory and what it takes to build agents that learn, adapt and actually work in production. 🎟️ Grab your tickets: hubs.la/Q047M1Bt0

A lot of beginners start their AI projects the wrong way. They start with the model. Which model is best? Which architecture should I use? But the most important question comes earlier. Who actually has the problem you’re trying to solve? Good AI projects start with real problems and real users. That’s what makes the technology matter. Learn how to build AI that solves real problems at @DeepLearningAI

AI isn’t just running in the cloud anymore. AI applications are increasingly running on edge devices, embedded systems, and in environments where connectivity and data residency matter. At AI Dev X SF, @ActianCorp is launching VectorAI DB, a portable vector database built for AI beyond the cloud. Developers can run AI applications across embedded, edge, on-prem, and hybrid environments while maintaining low-latency semantic search close to where applications run. Sign up for Early Access to learn more: hubs.la/Q047BxT70

Mobile AI apps saw explosive growth in 2025, with downloads doubling to 3.8 billion and revenue tripling to over $5 billion, according to Sensor Tower. AI chatbots like ChatGPT, Gemini, and DeepSeek dominate usage as people increasingly interact with AI through smartphone apps rather than the web or desktop. Learn more in The Batch hubs.la/Q047zSVB0

Everyone's building agents. Nobody's fixing the document layer. Agents orchestrate workflows perfectly. They break when documents get complex. Tables misread, layouts vary, values can't be traced. LandingAI is at AI Dev Day by @DeepLearningAI, April 28-29. David Park, our Senior Director of Applied AI is speaking on building the infrastructure layer that makes document understanding reliable for agentic systems. If document understanding is breaking your agentic workflows, come by.

OpenAI released GPT-5.4 Thinking and GPT-5.4 Pro, models with larger context windows and improved tool use that set new highs on benchmarks for coding and agentic tasks. The models power OpenAI’s improved Codex agent and rival Google’s Gemini 3.1 Pro Preview at the top of performance rankings, but they are priced at a premium. Learn more in The Batch hubs.la/Q047ndQt0

This Mindset Wins in the AI Era AI isn't your competition. It's your leverage, if you know how to use it. The workers falling behind aren't losing to AI. They're losing to the people who figured out how to direct it. Are you the manager or the one getting outpaced?

📢 New short course in collaboration with @Oracle! Agent Memory: Building Memory-Aware Agents Learn how to design a memory system that lets AI agents store, retrieve, and refine knowledge across sessions. Taught by @RichmondAlake and Nacho Martínez. Enroll now: hubs.la/Q047ljGB0

Many people start learning AI by reading about it. But the real shift happens when you start building. Going from understanding AI to creating applications usually happens step by step: first understanding how generative AI works, then learning the programming behind it, then working with LLMs, and eventually building full AI systems. Here are a few courses that walk through that path: Generative AI for Everyone hubs.la/Q047g3-d0 AI Python for Beginners hubs.la/Q047g3_60 ChatGPT Prompt Engineering for Developers hubs.la/Q047g8_n0 LangChain for LLM Application Development hubs.la/Q047g40-0 Agentic AI hubs.la/Q047fR7g0 A practical path from learning AI to building with it. Share it with someone starting their AI journey.

This week, in The Batch, Andrew Ng discusses the idea of creating a shared platform, similar to Stack Overflow, where AI coding agents can share what they've learned to help improve documentation and each other’s performance. Plus: ⚙️ OpenAI launches GPT-5.4 with stronger coding and agentic skills 📱 Mobile AI apps surge in downloads and revenue ⚡ Tech giants build private power plants for AI data centers 🖼️ Apple speeds up diffusion image training with compressed embeddings Read The Batch hubs.la/Q047f7W60

Testing an AI idea doesn’t have to take weeks. Start with one user. One job to be done. Build the smallest version that lets someone try it. Then watch carefully. Where do they hesitate? Where do they get confused? Where does the system fail? Those moments are where the real learning happens. In AI, speed of learning often matters more than perfection. Start building at @DeepLearningAI