DeepLearning.AI

Stop just asking AI for basic advice. It’s time to actually solve your problems. 💡 We recently kicked off a 7-Day AI Prompting Challenge to go along with our new AI Prompting for Everyone course — and there’s still time to join before the May 5th deadline! Your mission: Pick a real-world task that normally takes 5+ hours (like fixing a messy process, making a big decision, or building a work deliverable) and use expert prompting to actually get it done. Plus, standout submissions will earn special recognition and prizes! 🏆 Ready to level up your AI skills and build something awesome? Jump in below! 👇 🎓 Check out the AI Prompting for Everyone course: deeplearning.ai/courses/ai-pro… 🚀Join the Challenge here: community.deeplearning.ai/t/7-day-challe…

At the AI Dev 26 stage, Anush Elangovan from @AMD shared how engineering is shifting from writing code to steering intent, and why leaning into agentic AI now matters even more as the pace accelerates. The change is real and it's already here!

At AI Dev 26, Carter Rabasa (@crtr0) from @Box showed how file systems can act as the foundation for agent memory, state, and collaboration, leveraging what LLMs already do exceptionally well. Learn more at hubs.la/Q04dSlp90

From the stage at AI Dev 26: Dan Maloney, CEO of @LandingAI, on the Future of Software Engineering: AI is compressing skill gaps. Whether you’re a generalist or a specialist, you can fill gaps faster and move across domains more easily. The bar for building is shifting, and that's a good thing. Stay tuned for more AI Dev highlights!

Day 1!! @DeepLearningAI

Eli Schilling's workshop at AI Dev 26 focused on Memory Engineering and Context Engineering, and shared a working mental model of both disciplines and hands-on exposure to building a memory-first agent harness using @Oracle AI Database, LangChain, and Tavily. "An AI Agent without memory is just autocomplete with ambition. Give your agents the memory they deserve so they can stop forgetting and start learning." Explore the session's repo: github.com/oracle-devrel/…

Make sure to drop by the @Oracle booth at AI Dev 26 to learn more about the unified memory core for AI agents!

Emma McGrattan, CTO of @ActianCorp, explored at AI Dev 26 how the shift toward distributed AI is changing the role of vector databases. Her session highlighted how deployment topology is becoming a crucial design decision for modern AI architectures. Stay tuned for more AI Dev highlights!

@AndrewYNg Put what you learn into practice with a hands-on challenge! Find all the details here: community.deeplearning.ai/t/7-day-challe… Join us on Discord: discord.com/invite/z4FNBCF…

We're opening the doors at AI Dev 26 today, and celebrating with a little something for everyone: AI Prompting for Everyone, a new course taught by @AndrewYNg, is now live! 🎉 Learn how to get accurate answers, better writing, and more useful outputs from the AI tools you already use — whether you're an engineer, a marketer, a salesperson, in finance, or anything in between. Enroll today: hubs.la/Q04dFX7B0

Stop by @spice_ai 's booth live at AI Dev tomorrow and Wednesday! Spice makes it easy to securely serve enterprise data to AI agents, federated SQL across any data source, sub-second acceleration, and built-in hybrid search and AI inference, all without pipelines. Get a demo and enter to win a custom mechanical keyboard, and be sure to check out Spice AI's Founder & CEO @LukeKim’s presentation at 3:30 pm on Wednesday at Stage 3: “The Agent Data Stack: Why Every Agent Needs its Own Data Stack."

Researchers introduced Walrus, a transformer model that can predict what fluids (liquids, gases, and plasmas) will do across many physical domains. The model achieves higher accuracy and more stable long-term predictions than prior models, thanks in part to a "jitter" technique that reduces error accumulation during repeated simulations. Read our summary of the paper in The Batch 👇 hubs.la/Q04dfSWr0

Meet the Box team at AI Dev 26! @Box helps devs unlock their unstructured data and build production-grade AI-powered applications using APIs, MCP and Agent Skills. Learn more at hubs.la/Q04c_WN50, stop by their booth and don't miss Carter Rabasa's talk on Filesystems as the New Primitive for AI Agents on April 28. 🎫 Last tickets available: hubs.la/Q04c_J1x0

Images, audio, and video are everywhere in modern orgs but most data pipelines weren't built for any of them. We just launched a new short course with Snowflake on building multimodal data pipelines. You’ll build systems that: - Convert images and audio into structured text (OCR, ASR) - Generate timestamped descriptions from video with Vision Language Models - Retrieve across slides, audio, and video with a multimodal RAG pipeline Taught by Gilberto Hernandez. Enroll in "Building Multimodal Data Pipelines:" hubs.la/Q04d0QzW0

AlphaGenome is an open-weights model from Google that interprets non-coding DNA, helping identify how variations affect gene behavior and expressions in disease. By predicting gene properties and mutation impacts with high accuracy, it offers a powerful tool for biomedical research and treatment development. Learn more in The Batch ⬇️ hubs.la/Q04cYlj90

At AI Dev 26, @LandingAI will showcase Agentic Document Extraction (ADE), an API-first platform that turns messy, multi-modal documents and dark data into structured, auditable intelligence. Meet their team at booth 107 on April 28 and 29! Try your document at hubs.la/Q04cZQqR0

We’re building a new course with @CopilotKit! It focuses on turning agents into real applications, so instead of plain text, they can return forms, charts, and interactive UI. Join the waitlist to be the first to know when it’s live: hubs.la/Q04c-kzg0

Low- or no-vision users rely on AI tools like Be My Eyes, which can increase independence by helping assess appearance and surroundings. However, the apps' subjective and sometimes critical judgments about beauty can create confusion, insecurity, and potential psychological risks. Learn more in The Batch: hubs.la/Q04cBRqX0

Anthropic introduced Claude Mythos Preview, a highly capable model that can autonomously identify and exploit serious software vulnerabilities. Because of its inherent risk, the company has not released the model publicly. Instead it is working with industry partners to find and fix critical flaws before broader deployment. Learn more in The Batch: hubs.la/Q04cBS340

Shoutout to our top mentors 👏 They help shape our courses and support learners every day, all as volunteers. We’re incredibly grateful for everything you do. Here’s to more collaboration ahead!