The Batch
Newsletter (Digital)
Stay up to date with nuanced takes on high-impact business trends, practical research papers, and industry-shaping applications with The Batch, the DeepLearning.AI newsletter.
Every week, the Batch presents the most important AI events and perspective in a curated, easy-to-read report for engineers, enthusiasts, and business leaders as well as a personal note from Andrew Ng. Source
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Recent Articles
Search ArticlesAnthropic’s Watermarks, Grok 4.6 Surges, Qwen’s Open Weights, Better Corrections for Speech Recognition
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, I previously wrote about our AI Engineering Skills Map, with the highest level skills being (i) Building and deploying AI applications, (ii) Software engineering fundamentals, (iii) Using coding agents, and (iv) Shaping the build. In this letter, I will flesh out the first of them.
The AI Engineering Skills Map from Andrew Ng
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, I am delighted to present The AI Engineering Skills Map. AI allows us to build software very differently today than in 2022, and everyone with the skills to take advantage of this shift has numerous exciting project and job opportunities. But with the noisy, hype-filled, information environment around AI, what are the most valuable skills for you to learn?
DeepSeek-V4-Flash Outshines Pro, The Biggest GitHub Crawl Yet, Engineering System Prompts for Safer Code
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, I’m glad the idea of “tokenmaxxing” — that individuals and companies should use as many tokens as possible to boost productivity — is finally dying out. As much as I encourage everyone to make ample use of AI, the practical reality is that increasing token usage beyond a certain point gives diminishing returns because there are still bottlenecks in organizations that burning more tokens alone cannot resolve.
Opus Outshines Even Fable, Inside the Hugging Face Hack, AI Companies Spend Big for Compute
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, My team recently had our own version of Hugging Face’s experience when closed models failed to defend the company following an accidental cyberattack from OpenAI, leading Hugging Face to use the open weight GLM 5.2 instead.
AI Overviews Land Google In Hot Water, GPT-Live Puts Reasoning in the Background, How to Tell If Your Model is Manipulative
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, As AI increasingly automates coding, it frees up developers to spend time on high-level software development tasks traditionally reserved for senior engineers, like deciding on technical architecture and participating in scoping product requirements. Demand for this type of work is growing, since it is an economic complement to coding, which is becoming cheaper.
A New Generation Studies AI, Apple's Recipe for On-Device Models, GLM5.2 Tackles Open-Ended Problems
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, “Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products.
GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 Leads Open LLMs, AI Strains Climate Pledges, and more...
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. Some people are still using LLMs primarily by asking them short questions. But the models can do much more, like think for minutes, ingest many documents as context, and use web search and other tools.
AI Prompting for Everyone
What Learners From Previous Courses Say About DeepLearning.AI “Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.” “The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it.
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, and more...
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Meta Pivots From Open Weights, Big Pharma Bets On AI, Regulatory Patchwork, Simulating Human Cohorts
Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers.