We're launching Claude Code, our agentic coding tool, in a limited research preview. Claude Code lets developers delegate substantial engineering tasks to Claude directly from their terminal.

Claude Code has already become indispensable for our team. In early testing, Claude completed tasks in one pass that would normally take 45+ minutes of manual work.

With Claude Code, our goal is to better understand how developers use Claude to help improve our models for all.

In this demo, Claude Code completes complex coding tasks that would ordinarily take a significant amount of manual work. It explains an unfamiliar Next.js project, adds new functionality, creates tests, fixes build errors, and explains its changes.

Read more and join the preview: http://www.anthropic.com/news/claude-3-7-sonnet

Learn how to design with Claude Code from Anthropic in real product workflows with Figma MCP.

I cover what Claude Code is, how designers can use it, and how to turn it into a serious productivity multiplier for UX exploration, documentation, prototyping, and shipping faster.

If you’re a UI, UX, or Product Designer looking to build better products, work smarter with AI, and stay competitive in a rapidly changing industry, this guide is for you.

🔗 KEY LINKS
📣 JOIN THE COMMUNITY: https://uicollective.co/
❎ Follow me on X: https://x.com/KirkMDesign

Why Join UI Collective Academy? Get access to premium courses, premium downloads, and so much more on the way (I am largely building this solo...trying to make design education available for all, support goes a long way!)

↪️ Need a design system? (also included in the academy): https://collectivekit.co/

🔗 VIDEOS TO WATCH
Build a Design System: https://youtu.be/opTANvl9G1g
Complex Design System Setup: https://youtu.be/L-tpK7Eeuow
AI & Design Systems: https://youtu.be/XfezMs8B-O8

🔗 MORE LINKS
Let us build or fix your design system: https://designsystemlabs.co/
📣 Save 20% on the Annual Mobbin plan: http://mobbin.com/uicollective
[email protected]

VIDEO LINKS & MORE:
Installation: https://code.claude.com/docs/en/overview#terminal
For installation: /plugin install figma@claude-plugins-official
MCP documentation: https://code.claude.com/docs/en/mcp
https://www.pencil.dev/

0:00 An Introduction
0:53 Introduction to Claude Code
3:23 Claude Code & Opus 4.6
4:26 Setting Up Claude Code
6:07 Connecting Figma MCP
7:35 Building a Figma Design with Claude Code
11:59 Claude Code Desktop App
13:32 Pencil App Demo
14:31 Building a Design with Claude Code & Pencil

Presented at Code w/ Claude by @anthropic-ai on May 22, 2025 in San Francisco, CA, USA.

Speakers:
Cal Rueb, Member of Technical Staff at @anthropic-ai

MIT 18.156 Projection Theory, Spring 2025
Instructor: Lawrence D Guth
View the complete course: https://ocw.mit.edu/courses/18....-156-projection-theo
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

We formulate the main questions and goals of the class, especially the exceptional set problem.

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ

We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

MIT 14.01 Principles of Microeconomics, Fall 2023
Instructor: Prof. Jonathan Gruber
View the complete course: https://ocw.mit.edu/14-01F23
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

In this lecture, Prof. Gruber talks about how consumers make decisions with budget constraints and constrained choice. How do consumers make decisions when they face a limit on their resources? Keywords: constrained choice, budget constraints, consumer preference, SNAP

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ

We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

In an effort to spread knowledge and promote life-long learning, Stanford University has put extensive efforts into offering free online courses available to anyone, anywhere.

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

August 7, 2025

Guest Lecture:
Gabor Angeli
AI Research Engineer, Resolve AI

Bharat Khandelwal
AI Research Engineer, Resolve AI

Spiros Xanthos
Founder & CEO, Resolve AI

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

January 9, 2026
This lecture covers:
• How the ongoing integration of LLMs into our social worlds is creating new risks and opportunities
• Best practices for designing rehabilitative bots

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Jeremy Foote is an Assistant Professor in the Brian Lamb School of Communication at Purdue University and a faculty member in the Community Data Science Collective.

For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai

This lecture covers:
1. The course (10 mins)
2. Human language and word meaning (15 mins)
3. Word2vec introduction (15 mins)
4. Word2vec objective function gradients (25 mins)
5. Optimization basics (5 mins)
6. Looking at word vectors (10 mins or less)

Key learning: The (astounding!) result that word meaning can be represented rather
well by a (high-dimensional) vector of real numbers

To learn more about enrolling in this course visit: https://online.stanford.edu/co....urses/cs224n-natural

To follow along with the course schedule and syllabus visit: hhttps://web.stanford.edu/class..../archive/cs/cs224n/c

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2025 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

For all lectures, slides, and lab materials: http://introtodeeplearning.com/

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Deep learning has shown remarkable success in processing and understanding unstructured data like text and images. In this module, we will explore how deep neural networks can be leveraged to build intelligent systems in the domains of natural language processing and computer vision. Get ready to dive into the exciting world of deep learning on text and images!

We will start with the agenda and course overview. Then, we will review some key machine learning concepts, including regression, optimization, regularization, and text and data representation.

--------------------

This video is part of Machine Learning University’s open-source "Application of Deep Learning to Text and Image Data" series, part of our Fundamentals of Machine Learning content. Access the full set of lessons, hands-on labs, and Jupyter Notebooks here:

🔗 GitHub Repository: https://github.com/aws-mlu/aws....-mlu-eep-traditional
🔗 Watch More MLU Videos: https://www.youtube.com/@machinelearninguniversity

Machine Learning University provides free, open-source AI/ML educational content for educators. New content is released regularly — subscribe to stay updated.

We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :)

Code for this video:
https://github.com/llSourcell/....how_to_generate_imag

Mike's Winning Code:
https://github.com/xkortex/how...._to_win_slot_machine

SG's Runner up Code:
https://github.com/esha-sg/Int....ro-DeepLearning-Sira

Please subscribe! And like. And comment. That's what keeps me going.

2 things
-The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5.
-The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that.

More Learning resources:
https://jaan.io/what-is-variat....ional-autoencoder-va
http://kvfrans.com/variational....-autoencoders-explai
http://blog.fastforwardlabs.co....m/2016/08/12/introdu
http://blog.fastforwardlabs.co....m/2016/08/22/under-t
http://blog.evjang.com/2016/11..../tutorial-categorica
https://jmetzen.github.io/2015-11-27/vae.html

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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Provides an intuitive explanation for what deep learning models are doing when they find an 'efficient feature representation'
Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

Xiaoqing Ge, Senior Innovation Engineer at Baker Hughes, discusses the following:
• Overview of computer vision
• How deep learning works in computer vision?
• Traditional methods vs deep learning
• Survey of Baker Hughes applications
• Looking ahead

A lot of people think an AI agent is just a large language model wrapped in a chat interface. But when you move from a local demo to a real application, that assumption breaks down completely. A true agent is a production software system that can reason over a goal, retrieve context, maintain state, and trigger real actions through APIs.

In this video, I break down Agent AI System Design from a builder's perspective. We cover the exact architecture required to make single and multi-agent systems reliable, cost-aware, and safe enough to connect to real external tools. You will learn how to properly route models to save costs, structure your tool contracts, separate workflow state from long-term memory, and implement critical approval gates. By the end, you will have a practical mental model for building production-grade systems that users can actually rely on.

👉 Mastering Agentic AI Certification
Our August cohort now has fewer than 10 seats remaining, and we are currently offering 15% off with the code FLASHSALE15.
Register here: https://maven.com/aishwarya-sr....inivasan/mastering-a

👉 AI for Forward-Deployed Engineers Workshop
Our workshop is filling up very quickly. We currently have a limited-time 50% discount, so if you have been wanting to understand one of the fastest-growing roles in AI and learn how to build customer-facing AI solutions, now is the best time to register.

Register here: https://maven.com/aishwarya-sr....inivasan/ai-for-forw

👉 You can also check out our one-hour-long FREE Agentic AI System Design masterclass: https://maven.com/p/4c948b/age....ntic-ai-system-desig

CHAPTERS
0:00 The Demo vs. Production Reality
1:49 What is an Agent AI System?
2:12 Single Agent vs. Multi-Agent Systems
3:57 Building Block 1: The Model Layer and Routing
5:39 Building Block 2: Tool Contracts and Boundaries
7:37 Building Block 3: Memory vs. Workflow State
9:57 Building Block 4: Orchestration and Control Flow
12:23 Building Block 5: Trace-Level Evaluations
15:16 Building Block 6: Approval Gates and Policy Controls
17:03 Production Principle 1: Reliability and Fallbacks
18:42 Production Principle 2: Cost and Latency
20:42 Production Principle 3: Context and Retrieval Design
22:20 Production Principle 4: Observability, Security, and Privacy
25:33 Go Deeper with Gen Academy

📚 Resources to Watch Next
* AI Agents Explained: https://youtu.be/TZMdEg1ZoIo?si=xolifmjybYclpNZ-
* Single Agent vs. Multi-Agent Systems: https://youtu.be/-zBbij9rrEI?si=n0B9kG0XH9U0CQeV
* Deep Dive on AI Evaluations: https://youtu.be/_Er8Hao_gmQ?si=KNKjGEJrS7dkU6RF

Subscribe for deep dives into production-grade AI engineering, architecture breakdowns, and practical career strategies. Drop your system design questions in the comments below. I read and answer as many as I can!