Caleb Dunn @calebdunn___

This was 2 years ago, still maybe one of the wildest new features added to the iPhone?

No one cares about ballet and opera. Everyone has a take on something.

AI got great at reading documents. But reading was never the hard part. The hard part: turning extracted data into a running operation. Today we shipped Forge. It closes that gap. Describe your operation. Drop your data. Deploy. 🧵

ring ring…new J’adore coming soon📞✨ #JadoreDior

new IT girl alert 🚨 Fine Linez eyeliner just dropped 👁️ she’s waterproof, comes in 10 shades for all skintones, and lasts ALLL day long!!! #FineLinez available now @Sephora + fentybeauty.com

Tesla owners have now driven >8 billion miles on FSD Supervised tesla.com/fsd/safety?red…

If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI. In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can apply to build a wide variety of applications. If you want to try vibe coding, this will be the best place to start! Further, you'll be able to use these techniques with whatever tool you're most comfortable with (like ChatGPT, Gemini, Claude, or others) -- we're vendor neutral. Skills you'll gain: - How to build web apps with AI - zero coding skills needed - How to fix and improve your creations by chatting with AI - A simple process you can use to build other things you can dream up Building with AI is one of the most fun things in the world. Please join me and take your first step! I think you will be surprised at what you can build. And if you're an experienced engineer, please share this with someone in your life who's been curious about building with AI. Come build with me! deeplearning.ai/courses/build-…

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it’s good 🎈

Autonomy is inevitable

Camp Mode turns your Tesla into a hotel on wheels

Every day, we're working towards making sure everyone can get any good or service they want without sacrificing our resources. This is sustainable abundance. To achieve this, our products must be accessible to all Tesla has all the pieces: AI tech + electrical-mechanical engineering + scalable production lines. No one else has that.

I just dumped the latest NVIDIA 10-Q earnings report, released an hour ago, into Agentic Document Extraction, and the results are really accurate! Left side of the image shows the original PDF; right side shows the extracted info, including e.g. the $57.01B revenue in the most recent quarter. This is powered by the DPT (document pre-trained transformer) model. Check it out!

Really proud of the DeepLearningAI team. When Cloudflare went down, our engineers used AI coding to quickly implement a clone of basic Cloudflare capabilities to run our site on. So we came back up long before even major websites!

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. He is worried that by the time he graduates, AI will be so good there’s no meaningful work left for him to do to contribute to humanity, and he will just live on Universal Basic Income (UBI). I wrote back to reassure him that there will still be plenty of work he can do for decades hence, and encouraged him to work hard and learn to build with AI. But this conversation struck me as an example of how harmful hype about AI is. Yes, AI is amazingly intelligent, and I’m thrilled to be using it every day to build things I couldn’t have built a year ago. At the same time, AI is still incredibly dumb, and I would not trust a frontier LLM by itself to prioritize my calendar, carry out resumé screening, or choose what to order for lunch — tasks that businesses routinely ask junior personnel to do. Yes, we can build AI software to do these tasks. For example, after a lot of customization work, one of my teams now has a decent AI resumé screening assistant. But the point is it took a lot of customization. Even though LLMs can handle a much more general set of tasks than previous iterations of AI technology, compared to what humans can do, they are still highly specialized. They’re much better at working with text than other modalities, still require lots of custom engineering to get it the right context for a particular application, and we have few tools — and only inefficient ones — for getting our systems to learn from feedback and repeated exposure to a specific task (such as screening resumés for a particular role). AI has stark limitations, and despite rapid improvements, it will remain limited compared to humans for a long time. AI is amazing, but it has unfortunately been hyped up to be even more amazing than it is. A pernicious aspect of hype is that it often contains an element of truth, but not to the degree of the hype. This makes it difficult for nontechnical people to discern where the truth really is. Modern AI is a general purpose technology that is enabling many applications, but AI that can do any intellectual tasks that a human can (a popular definition for AGI) is still decades away or longer. This nuanced message that AI is general, but not that general, often is lost in the noise of today's media environment. Similarly, the progress of frontier models is amazing! But not so amazing that they’ll be able to do everything under the sun without a lot of customization. I know VC investors who are scared to invest in application-layer startups because they are worried that frontier AI model companies will quickly wipe out all of these businesses by improving their models. While some thin wrappers around LLMs no doubt will be replaced, there also remains a huge set of valuable applications that the current trajectory of progress of frontier models won’t displace for a long time. Without accurate information about the current state of AI and how it is likely to progress, some young people will decide not to enter AI because think think AGI leaves them no meaningful role, or decide not to learn how to code because they fear AI will automate it — right when it is the best time ever to join our field. Let us all keep working to get to a precise understanding of what’s actually possible, and keep building! [Original text: deeplearning.ai/the-batch/issu… ]

FSD

We’re saying goodbye to the Recode branding today, but you can still find all the same great work from the reporters you know and love, plus an expanded focus on tech and innovation, over at vox.com/technology. vox.com/technology/236…

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. This is why I increasingly try to select software that lets me control my own data, so I can make it available to my AI agents. Because of AI’s growing capabilities, the value you can now create from “connecting the dots” between different pieces of data is higher than ever. For example, if an email click is logged in one vendor’s system and a subsequent online purchase is logged in a different one, then it is valuable to build agents that can access both of these data sources to see how they correlate to make better decisions. Unfortunately, many SaaS vendors try to create a data silo in their customer’s business. By making it hard for you to extract your data, they create high switching costs. This also allows them to steer you to buy their AI agent services — sometimes at high expense and/or of low quality — rather than build your own or buy from a different vendor. Unfortunately, some SaaS vendors are seeing AI agents coming for this data and working to make it harder for you (and your AI agents) to efficiently access it. One of my teams just told me that a SaaS vendor we have been using to store our customer data wants to charge over $20,000 for an API key to get at our data. This high cost — no doubt intentionally designed to make it hard for customers to get their data out — is adding a barrier to implementing agentic workflows that take advantage of that data. Through AI Aspire (an AI advisory firm), I advise a number of businesses on their AI strategies. When it comes to buying SaaS, I often advise them to try to control their own data (which, sadly, some vendors mightily resist). This way, you can hire a SaaS vendor to record and operate on your data, but ultimately you decide how to route it to the appropriate human or AI system for processing. Over the past decade, a lot of work has gone into organizing businesses’ structured data. Because AI can now process unstructured data much better than before, the value of organizing your unstructured data (including PDF files, which LandingAI’s Agentic Document Extraction specializes in!) is higher than ever before. In the era of generative AI, businesses and individuals have important work ahead to organize their data to be AI-ready. P.S. As an individual, my favorite note-taking app is Obsidian. I am happy to “hire” Obsidian to operate on my notes files. And, all my notes are saved as Markdown files in my file system, and I have built AI agents that read from or write to my Obsidian files. This is a small example of how controlling my own notes data lets me do more with AI agents! [Original text: deeplearning.ai/the-batch/issu… ]

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.@roelofbotha is one of the investors I've come to most respect, and I learned something new every time I spoke with him. Even beyond his leadership of @sequoia, his influence on how investors think is hard to overstate. His passing on the baton is the end of an era!