DeepLearning.AI

How to Gain New Skills and Sharpen Old Ones: Give yourself a gift this holiday season by taking courses, building projects, and (maybe) reading papers.

Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software.

Letters

Large Language Models Are General — But Not _That_ General: Current progress in AI is piecemeal and laborious. Unforeseen breakthroughs may drive future progress, but the trend of improvement is incremental.

As amazing as LLMs are, improving their knowledge today involves a more piecemeal process than is widely appreciated.

Letters

Build an Autonomous Agent Using This Simple Recipe!: The ability of large language models to carry out multiple steps autonomously makes it possible to build a capable agent in a few lines of code.

If you have not yet built an agentic workflow, I encourage you to try doing so, using the simple recipe I’ll share here!

Letters

Why People Don’t Trust AI and What To Do About It: Recent surveys by Edelman and Pew Research show that Americans distrust AI. The AI community should take this seriously and work to regain public trust.

Separate reports by the publicity firm Edelman and Pew Research show that Americans, and more broadly large parts of Europe and the western world, do not trust AI and are not excited about it.

Letters

Understanding the AI Bubble — If There Is One

Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenAI’s $1.4 trillion plan and Nvidia briefly reaching a $5 trillion market cap, many have asked if speculation and hype have driven the values of AI investments above sustainable values.

Letters

What We Learned at AI Dev x NYC 2025: DeepLearning.AI’s sold-out AI Dev x NYC 2025 conference revealed widespread optimism, excitement, and technical depth among AI developers.

I just got back from AI Dev x NYC, the AI developer conference where our community gathers for a day of coding, learning, and connecting.

Letters

Don't Believe The Hype!: AGI is not just around the corner. People who enter AI today have huge opportunities to contribute to the field.

I recently received an email titled “An 18-year-old’s dilemma: Too late to contribute to AI?” Its author, who gave me permission to share this, is preparing for college.

Letters

Tear Down Data Silos!: Many software-as-a-service vendors aim to hold their customers' data in silos. Their customers would do well to open the silos so AI agents can use the data.

AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful.

Letters

Announcing the DeepLearning.AI Pro Membership!: The time to build with AI is now! One membership gives you all DeepLearning.AI courses, labs, practice sessions, and certificates for completed courses.

Today I’m launching DeepLearning.AI Pro — the one membership that keeps you at the forefront of AI. Please join!

Letters

Improve Agentic Performance with Evals and Error Analysis, Part 2: Best practices for error analysis in agentic AI development, and how LLMs make them easier

In last week’s letter, I explained how effective agentic AI development needs a disciplined evals and error analysis process, and described an approach to performing evals.

Letters

Improve Agentic Performance with Evals and Error Analysis, Part 1: When AI agentic systems go astray, it’s tempting to shortcut evals and error analysis. But these processes cas lead to much faster progress.

Readers responded with both surprise and agreement last week when I wrote that the single biggest predictor of how rapidly a team makes progress building an AI agent lay in their ability to drive a disciplined process for evals...

Letters

Check Out Our Course on How to Build AI Agents!: Andrew Ng teaches design patterns and best practices for building autonomous agents in a new course available exclusively from DeepLearning.AI.

I’m thrilled to announce my latest course: Agentic AI! This course will get you up to speed building cutting-edge agentic workflows.

Letters

How to Liberate Data From Large, Complex PDFs: LandingAI’s Agentic Document Extraction accurately extracts data from PDFs for processing by LLMs in as few as 3 lines of code.

LandingAI’s Agentic Document Extraction (ADE) turns PDF files into LLM-ready markdown text.

Letters

High Stakes in the U.S.-China AI Chip Race: China’s decision to use domestic AI chips instead of buying from Nvidia signals progress — and newfound confidence — in its own semiconductor industry.

Last week, China barred its major tech companies from buying Nvidia chips.

Letters

Agentic Coding and Agentic Software Testing Go Together: Agentic coding can make mistakes, but agentic testing can find and fix them.

Automated software testing is growing in importance in the era of AI-assisted coding.

How to Gain New Skills and Sharpen Old Ones: Give yourself a gift this holiday season by taking courses, building projects, and (maybe) reading papers.

Large Language Models Are General — But Not _That_ General: Current progress in AI is piecemeal and laborious. Unforeseen breakthroughs may drive future progress, but the trend of improvement is incremental.

Build an Autonomous Agent Using This Simple Recipe!: The ability of large language models to carry out multiple steps autonomously makes it possible to build a capable agent in a few lines of code.

Why People Don’t Trust AI and What To Do About It: Recent surveys by Edelman and Pew Research show that Americans distrust AI. The AI community should take this seriously and work to regain public trust.

Understanding the AI Bubble — If There Is One

What We Learned at AI Dev x NYC 2025: DeepLearning.AI’s sold-out AI Dev x NYC 2025 conference revealed widespread optimism, excitement, and technical depth among AI developers.

Don't Believe The Hype!: AGI is not just around the corner. People who enter AI today have huge opportunities to contribute to the field.

Tear Down Data Silos!: Many software-as-a-service vendors aim to hold their customers' data in silos. Their customers would do well to open the silos so AI agents can use the data.

Announcing the DeepLearning.AI Pro Membership!: The time to build with AI is now! One membership gives you all DeepLearning.AI courses, labs, practice sessions, and certificates for completed courses.

Improve Agentic Performance with Evals and Error Analysis, Part 2: Best practices for error analysis in agentic AI development, and how LLMs make them easier

Improve Agentic Performance with Evals and Error Analysis, Part 1: When AI agentic systems go astray, it’s tempting to shortcut evals and error analysis. But these processes cas lead to much faster progress.

Check Out Our Course on How to Build AI Agents!: Andrew Ng teaches design patterns and best practices for building autonomous agents in a new course available exclusively from DeepLearning.AI.

How to Liberate Data From Large, Complex PDFs: LandingAI’s Agentic Document Extraction accurately extracts data from PDFs for processing by LLMs in as few as 3 lines of code.

High Stakes in the U.S.-China AI Chip Race: China’s decision to use domestic AI chips instead of buying from Nvidia signals progress — and newfound confidence — in its own semiconductor industry.

Agentic Coding and Agentic Software Testing Go Together: Agentic coding can make mistakes, but agentic testing can find and fix them.

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