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Before GenAI, prototyping an app, especially one powered by data or AI, meant hours of setup and boilerplate code before you could even test your idea. In this clip from the course Fast Prototyping of GenAI Apps with Streamlit, you’ll see how that changes. Watch how GenAI flips the... show more

DeepLearning.AI

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12,373 views • 9 months ago •via X (Twitter)

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🚨 New course alert! Fast Prototyping of GenAI Apps with Streamlit, built in partnership with Snowflake, is live. Traditional, months-long planning cycles don’t fit the software development landscape of today. New capabilities surface every week, and ideas lose momentum unless you ship a demo quickly. This course, led by Chanin Nantasenamat (Chanin Nantasenamat), gives you a repeatable workflow to go from concept to share-ready demo in days, not months. What you’ll do: - Build an interactive Streamlit app on a dataset inside your own Snowflake account (120-day trial available) - Improve response quality with prompt engineering and RAG, grounded in your data - Run your prototype in Snowflake or publish to Streamlit Community Cloud to collect feedback and iterate with an MVP workflow Get all the details and enroll now:

🚨 New course alert! Fast Prototyping of GenAI Apps with Streamlit, built in partnership with Snowflake, is live. Traditional, months-long planning cycles don’t fit the software development landscape of today. New capabilities surface every week, and ideas lose momentum unless you ship a demo quickly. This course, led by Chanin Nantasenamat (Chanin Nantasenamat), gives you a repeatable workflow to go from concept to share-ready demo in days, not months. What you’ll do: - Build an interactive Streamlit app on a dataset inside your own Snowflake account (120-day trial available) - Improve response quality with prompt engineering and RAG, grounded in your data - Run your prototype in Snowflake or publish to Streamlit Community Cloud to collect feedback and iterate with an MVP workflow Get all the details and enroll now:

DeepLearning.AI

45,584 views • 10 months ago

Discover how success in #GenAI hinges on #Data and #Cloud. Watch HCLTech experts Alan Flower and Siki Giunta discuss the rise of GenAI, the purpose of continuous modernization in cloud and the data journey. Learn more. #SuperchargingProgress

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New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

139,763 views • 1 year ago

"Do this first before asking AI to code." Here's my new episode with Colin Matthews (top AI instructor), where we walk through how to pick the right AI prototyping tool and build a habit-tracking app in just 20 minutes with Bolt. Colin shared some incredible advice for AI prototyping, including: - Which tools beginners should start with - Why you should get the data model right first - How to avoid struggling with AI on bugs 📌 Watch now:

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51,265 views • 1 year ago

Day three of coding with Cursor. Created a personal assistant powered by n8n.io, Supabase and Streamlit. From concept to functional app in a day, complete with user auth, data management, and an intuitive interface. Without writing a line of code.

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Supercharge your personal investing ✨Building advanced trading strategies used to take hours on end to code, test, and deploy. That changes with Composer: The AI-powered trading platform that lets you build trading strategies, test on historical data, and invest in a click. No coding needed. Learn more:

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Composer by SoFi

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Learn to build and deploy GenAI pipelines in "Orchestrating Workflows for GenAI Applications", built in partnership with Astronomer and taught by Kenten Danas, the company's DevRel Senior Manager, and Tamara Fingerlin, developer advocate. Many GenAI applications require executing a pipeline comprising many steps. For example, a RAG app for recommending books might ingest and embed book descriptions, store the embeddings in a vector database, and later use the database to retrieve and recommend specific books based on a user query. After having prototyped this -- maybe in a Jupyter notebook -- how do you turn this into a reliable, repeatable workflow to run in production? In this short course, you’ll learn to build reliable GenAI pipelines and orchestrate them using the popular open-source tool Airflow 3.0. You’ll learn to break down a workflow into discrete tasks so that an orchestration framework can schedule tasks to run in the right order at the right time (using time-based or data-aware triggers), and execute tasks in parallel when possible. It can also use retries to recover gracefully from failure (such as transient API rate limits) and provide observability (using Airflow UI) to help you track the status of the pipeline. You'll do this by using Airflow dags, which helps sequence tasks that need to run in a specific order, with clear task dependencies. By the end of this course, you’ll know how to turn your prototype Jupyter notebook or Python script into production-ready workflow. Please sign up here:

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73,584 views • 1 year ago

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New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

Andrew Ng

752,188 views • 1 year ago

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

Matt Dancho (Business Science)

12,736 views • 1 year ago

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

Andrew Ng

126,390 views • 1 year ago

In large-scale clusters, there are two types of people: the FP4/BF16 GenAI zoomers and the traditional FP64 boomers. It's important that low-precision GenAI engineers learn from the FP64 boomers, who have extensive experience with large-scale supercomputing. Equally important is for FP64 engineers to learn from the GenAI zoomers about incorporating GenAI and lower precision into their legacy HPC workloads. One crucial note: FP64 boomers don't use Discord or Slack, the only way to communicate with them is through Microsoft Teams or WebEx.

In large-scale clusters, there are two types of people: the FP4/BF16 GenAI zoomers and the traditional FP64 boomers. It's important that low-precision GenAI engineers learn from the FP64 boomers, who have extensive experience with large-scale supercomputing. Equally important is for FP64 engineers to learn from the GenAI zoomers about incorporating GenAI and lower precision into their legacy HPC workloads. One crucial note: FP64 boomers don't use Discord or Slack, the only way to communicate with them is through Microsoft Teams or WebEx.

SemiAnalysis

23,618 views • 7 months ago

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

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DeepLearning.AI

32,513 views • 10 months ago

"If you're in tech, run in one of these directions." In this clip, Amjad Masad (CEO Replit ⠕) shares two paths to future-proof your career in the AI era: 1. Get as close to the metal as possible (e.g., NASA won't use GPT-Javascript to run rockets) OR 2. Become a generalist who can go from idea to end product with AI end to end. Amjad and I had a great chat covering: • Why now's the best time to learn to code • A live demo of building a nutrition tracker app • The rise of personal apps and the future of work Some quotes from Amjad: "The return on learning to code doubles every 6 months. AI automates many of the boring parts. What's left is you and your creativity — the most exciting part of coding." "The ultimate test for an AI coding agent is if you can make an app faster than you can Google for it. I think we've done it." "I’ll be honest with you. I think roadmaps are dead. Here's why..." 📌 Watch now:

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Peter Yang

740,088 views • 1 year ago

I tried @ReelQuestAI recently, and it honestly feels like turning imagination directly into a playable game. Create your own playable world here: You describe the world, characters, gameplay, story, enemies, quests and even the vibe, AI builds a playable experience from it without needing to code. What normally takes weeks of planning, designing, and prototyping can now happen in minutes. It’s especially useful for creators who want to test ideas fast, experiment with different concepts, or bring cinematic worlds to life without a full dev team. Feels less like a game tool and more like an AI-powered game studio for creators.

I tried @ReelQuestAI recently, and it honestly feels like turning imagination directly into a playable game. Create your own playable world here: You describe the world, characters, gameplay, story, enemies, quests and even the vibe, AI builds a playable experience from it without needing to code. What normally takes weeks of planning, designing, and prototyping can now happen in minutes. It’s especially useful for creators who want to test ideas fast, experiment with different concepts, or bring cinematic worlds to life without a full dev team. Feels less like a game tool and more like an AI-powered game studio for creators.

Sharon Riley

32,261 views • 1 month ago

Announcing Generative AI for Software Development, a new specialization on Coursera! Taught by my friend and longtime instructor Laurence Moroney 🇺🇸🇮🇪 🏴󠁧󠁢󠁷󠁬󠁳󠁿. Using GenAI for software development goes well beyond using chatbots for code generation. This 3-course series shares current best practices for AI use through the entire software development lifecycle: From design and architecture to coding, testing, deployment, and maintenance. You'll learn to use LLMs as your thought partner, pair programmer, documentation specialist, security analyst, and performance optimization expert. There's a lot that anyone that writes software can gain from using GenAI, and this will show you how! Please sign up here to get started!

Announcing Generative AI for Software Development, a new specialization on Coursera! Taught by my friend and longtime instructor Laurence Moroney 🇺🇸🇮🇪 🏴󠁧󠁢󠁷󠁬󠁳󠁿. Using GenAI for software development goes well beyond using chatbots for code generation. This 3-course series shares current best practices for AI use through the entire software development lifecycle: From design and architecture to coding, testing, deployment, and maintenance. You'll learn to use LLMs as your thought partner, pair programmer, documentation specialist, security analyst, and performance optimization expert. There's a lot that anyone that writes software can gain from using GenAI, and this will show you how! Please sign up here to get started!

Andrew Ng

69,454 views • 1 year ago

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

Amazon

47,421 views • 2 years ago

We're excited to announce the LLM Apps: Evaluation course is now LIVE! 🚀 Created in collaboration with guest experts 👩‍💻 Paige Bailey and Graham Neubig, this course equips you with the skills needed to build trustworthy evaluations for your GenAI apps. Ready to skill up? 👇

We're excited to announce the LLM Apps: Evaluation course is now LIVE! 🚀 Created in collaboration with guest experts 👩‍💻 Paige Bailey and Graham Neubig, this course equips you with the skills needed to build trustworthy evaluations for your GenAI apps. Ready to skill up? 👇

Weights & Biases

16,757 views • 1 year ago

Update: You can now modify your created apps easily. Once you create an app, you'll see a popup in the bottom right corner. Simply tell the AI about your requested changes and it whip together the code additions Watch how I add a timer and gamification for each task to a basic todo app 👇 The possibilities are now truly limitless - instead of just making the app and getting it right once, you can now keep iterating until you get what you want. One step closer to the vision of turning 2077 into the most powerful - and fun - coding engine in crypto!

Update: You can now modify your created apps easily. Once you create an app, you'll see a popup in the bottom right corner. Simply tell the AI about your requested changes and it whip together the code additions Watch how I add a timer and gamification for each task to a basic todo app 👇 The possibilities are now truly limitless - instead of just making the app and getting it right once, you can now keep iterating until you get what you want. One step closer to the vision of turning 2077 into the most powerful - and fun - coding engine in crypto!

2077: A Code Quest

20,810 views • 1 year ago