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How to make almost anything (2019)

My name is D. Sculley. I lead several teams at Google in Cambridge doing research in various aspects of machine learning. I'm involved in this course because many of our current projects involve the use of machine learning for design or fabrication problems of one form or another, including in the biology space and the chemistry space. I'm interested in learning more about other forms of fabrication and seeing if there are interesting cross-domain opportunities to think about. Here is my Google

How To Make (almost) Anything (2019)

My name is D. Sculley. I lead several teams at Google in Cambridge doing research in various aspects of machine learning. I'm involved in this course because many of our current projects involve the use of machine learning for design or fabrication problems of one form or another, including in the biology space and the chemistry space. I'm interested in learning more about other forms of fabrication and seeing if there are interesting cross-domain opportunities to think about. Here is my Google

Rao Reading Algorithm (2024)

October 2024 Who, What, Where, How, and Why Do I Read – Why Reading Matters Reading means my total consumption of ideas and media, learning via seeing or listening to symbols versus pure action. Reading involves books at the core, but also journal articles, news, blogs, music, video, maps, engineering and architectural drawings, code, patents, walking in cities, conversations with people, and viewing art. If it’s compressed info encoded into my brain and world models, I count it. The line betw

PHP-ORT: Machine learning inference for the web

The Problem The Solution Technical Details Addressing Critics The Future The Inevitable Transformation Software is changing faster than we've seen in 25 years. Machine learning isn't just becoming important, it's becoming essential. Every application, every website, every digital interaction will soon expect intelligent features as standard. For millions of PHP developers who power the web, this creates an existential challenge: stay relevant in an AI-first world or risk obsolescence. The St

PHP-ORT: Machine Learning Inference for the Web

The Problem The Solution Technical Details Addressing Critics The Future The Inevitable Transformation Software is changing faster than we've seen in 25 years. Machine learning isn't just becoming important, it's becoming essential. Every application, every website, every digital interaction will soon expect intelligent features as standard. For millions of PHP developers who power the web, this creates an existential challenge: stay relevant in an AI-first world or risk obsolescence. The St

AI is a floor raiser, not a ceiling raiser

AI is a Floor Raiser, not a Ceiling Raiser¶ A reshaped learning curve¶ Before AI, learners faced a matching problem: learning resources have to be created with a target audience in mind. This means as a consumer, learning resources were suboptimal fits for you: You're a newbie at $topic_of_interest , but have knowledge in related topic $related_topic . But finding learning resources that teach $topic_of_interest in terms of $related_topic is difficult. , but have knowledge in related topic .

AI Is a Floor Raiser, Not a Ceiling Raiser

AI is a Floor Raiser, not a Ceiling Raiser¶ A reshaped learning curve¶ Before AI, learners faced a matching problem: learning resources have to be created with a target audience in mind. This means as a consumer, learning resources were suboptimal fits for you: You're a newbie at $topic_of_interest , but have knowledge in related topic $related_topic . But finding learning resources that teach $topic_of_interest in terms of $related_topic is difficult. , but have knowledge in related topic .

GEPA: Reflective prompt evolution can outperform reinforcement learning

Authors: Lakshya A Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alexandros G. Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, Omar Khattab Paper: https://arxiv.org/abs/2507.19457 TL;DR What was done? The authors introduced GEPA (Genetic-Pareto), a novel algorithm for optimizing prompts in complex, multi-module AI systems. Instead of relying on traditional reinforceme

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Authors: Lakshya A Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alexandros G. Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, Omar Khattab Paper: https://arxiv.org/abs/2507.19457 TL;DR What was done? The authors introduced GEPA (Genetic-Pareto), a novel algorithm for optimizing prompts in complex, multi-module AI systems. Instead of relying on traditional reinforceme

YouTube is turning over age verification to AI

YouTube will start using machine learning to determine whether viewers should be on a teen account. The company said it plans to start using this AI application on a subset of US users in the coming weeks for a trial before rolling it out to the rest of the market. The tool will assess user behaviors including the types of videos being searched for, the categories of videos watched and how long the account has existed. When an account is deemed by machine learning to belong to a teen, YouTube wi

ChatGPT just got smarter: OpenAI’s Study Mode helps students learn step-by-step

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now OpenAI announced Study Mode for ChatGPT on Tuesday, a new feature that fundamentally changes how students interact with artificial intelligence by withholding direct answers in favor of Socratic questioning and step-by-step guidance. The launch represents OpenAI’s most significant push into the education technology market, which analysts p

OpenAI is launching a version of ChatGPT for college students

A handful of college students who were part of OpenAI’s testing cohort—hailing from Princeton, Wharton, and the University of Minnesota—shared positive reviews of Study Mode, saying it did a good job of checking their understanding and adapting to their pace. The learning approaches that OpenAI has programmed into Study Mode, which are based partially on Socratic methods, appear sound, says Christopher Harris, an educator in New York who has created a curriculum aimed at AI literacy. They might

Learning Is Slower Than You Think

It was just a question over breakfast. “What’s a metaphor?” Mira asked her father, spoon halfway to her mouth. He began to explain, but she interrupted: “So it’s when something isn’t what it is—but also is?” There was a silence at the table—not confusion, but recognition. She had already touched it, before any definition arrived. Before a lesson plan or rubric could intervene. That moment—so small, so ordinary—was also everything. Because this is how real learning often arrives: sideways, u

Teach Yourself Programming in Ten Years (1998)

Why is everyone in such a rush? The conclusion is that either people are in a big rush to learn about programming, or that programming is somehow fabulously easier to learn than anything else. Felleisen et al. give a nod to this trend in their book How to Design Programs, when they say "Bad programming is easy. Idiots can learn it in 21 days, even if they are dummies." The Abtruse Goose comic also had their take. Let's analyze what a title like Teach Yourself C++ in 24 Hours could mean: Teach

Developing our position on AI

If you’re not familiar with us, RC is a 6 or 12 week retreat for programmers, with an integrated recruiting agency. Ours is a special kind of learning environment, where programmers of all stripes grow by following their curiosity and building things that are exciting and important to them. There are no teachers or curricula. We make money by RC is a 6 or 12 week retreat for programmers, with an integrated recruiting agency. Ours is a special kind of learning environment, where programmers of al

Computing’s Top 30: Tejas Chopra

Two minutes into his TedX talk on AI and the environment, Tejas Chopra notes that training a single large language model releases roughly the same amount of carbon dioxide into Earth’s atmosphere as 125 roundtrip flights from New York to Beijing. In keeping with his ever-practical approach, Chopra goes on to suggest several concrete ways to reduce AI’s carbon footprint by optimizing resource use, energy consumption, and AI decision-making across industries. An accomplished engineer specializin

AI coding agents are removing programming language barriers

For a decade (2014-2024), I was a Ruby-only developer. I worked across the Ruby ecosystem—from Rails development to Ruby’s core tooling like IRB, RDoc, and the debug gem. But while I moved around the stack, I stayed within Ruby’s boundaries. Ruby wasn’t just my primary language; it was essentially my only language. That changed in 2025. This year, I’ve contributed to Sorbet (C++), worked on RBS’s parser (C), and am now diving into ZJIT (Rust). A combination of factors enabled this shift—someth

Kapa.ai (YC S23) is hiring a software engineers (EU remote)

As a software engineer you will work across the stack on the Kapa systems that answer thousands of developer questions per day. Check out Docker’s documentation for a live example of what kapa is. In this role, you will: Work directly with the founding team and our research engineers. Scale the infrastructure that powers the Kapa RAG engine (Python). Experiment with new features in the Kapa analytics platform (React + Python). Work on the client integrations which are used to deploy Kapa fo

OpenAI, Anthropic, Google may disrupt education market with new AI tools

AI companies could soon disrupt the education market with their new AI-based learning tools for students. BleepingComputer recently reported that OpenAI is working on a Study Together feature for ChatGPT. This would allow ChatGPT to teach students a wide range of topics and then offer quizzes. The idea is to create an engaging and interactive "study together" experience where students ask questions and ChatGPT puts in effort to teach them. But it turns out that OpenAI isn't the only AI compa

Here’s how ChatGPT’s upcoming ‘Study Together’ tool could enhance learning (APK teardown)

Calvin Wankhede / Android Authority TL;DR OpenAI is working on a dedicated “Study Together” mode to help users grasp concepts better. Study Together is likely to help break down concepts into simpler terms and follow up with quizzes for more engaged learning. It is being tested with both free and paid users, suggesting non-paying users might also have access when it launches. AI tools, such as ChatGPT, have accelerated learning by making concepts much easier to find and summarize. Now, OpenA

Apple’s machine learning framework is getting support for NVIDIA’s CUDA platform

Apple’s MLX machine learning framework, originally designed for Apple Silicon, is getting a CUDA backend, which is a pretty big deal. Here’s why. The work is being led by developer @zcbenz on GitHub (via AppleInsider), who started prototyping CUDA support a few months ago. Since then, he split the project into smaller pieces, and gradually merged them into Apple’s MLX’s main branch. The backend is still a work in progress, but several core operations, like matrix multiplication, softmax, reduc

Show HN: I built this to talk Danish to my girlfriend – works with any language

Ever wanted to text your foreign partner or chat with international friends but felt held back by missing vocabulary? Don't let language barriers stop you from connecting. No sign-ups, no subscriptions. Just open the app and start learning. Perfect for spontaneous moments when you need to express something important. Every correction is saved. Review your mistakes, practice pronunciation, and track your progress as you naturally improve. Hear how every correction should sound with high-qualit

AI text-to-speech programs could “unlearn” how to imitate certain people

AI companies generally keep a tight grip on their models to discourage misuse. For example, if you ask ChatGPT to give you someone’s phone number or instructions for doing something illegal, it will likely just tell you it cannot help. However, as many examples over time have shown, clever prompt engineering or model fine-tuning can sometimes get these models to say things they otherwise wouldn’t. The unwanted information may still be hiding somewhere inside the model so that it can be accessed

6 free Android apps I use to learn something new every day

Megan Ellis / Android Authority I’ve had a lifelong love of learning, to the point where I used to read encyclopedias and dictionaries as a child, along with a variety of non-fiction books around specific topics. This love of learning hasn’t dampened as an adult, as I frequently find myself in Wikipedia rabbit holes, and my YouTube Watch Later list is filled with topics around science, history, psychology, and other topics I want to learn about. The difference, however, is that I have a lot le

Claude can now connect to learning apps like Canvas, Panopto and Wiley

At the start of April, Anthropic released Learning mode, a feature that changed how Claude would interact with users. With the tool enabled, the chatbot would attempt to guide students to a solution rather than providing them with an answer outright. The release of Learning mode and Claude for Education was the start of a major push by Anthropic to work with universities and colleges globally. Today, the company is upgrading Claude for Education with the addition of integrations to three popula

The era of exploration

Large language models are the unintended byproduct of about three decades worth of freely accessible human text online. Ilya Sutskever compared this reservoir of information to fossil fuel, abundant but ultimately finite. Some studies suggest that, at current token‑consumption rates, frontier labs could exhaust the highest‑quality English web text well before the decade ends. Even if those projections prove overly pessimistic, one fact is clear: today’s models consume data far faster than humans

ChatGPT Glossary: 53 AI Terms Everyone Should Know

AI is everywhere. From the massive popularity of ChatGPT to Google cramming AI summaries at the top of its search results, AI is completely taking over the internet. With AI, you can get instant answers to pretty much any question. It can feel like talking to someone who has a Ph.D. in everything. But that aspect of AI chatbots is only one part of the AI landscape. Sure, having ChatGPT help do your homework or having Midjourney create fascinating images of mechs based on country of origin is co

The Era of Exploration

Large language models are the unintended byproduct of about three decades worth of freely accessible human text online. Ilya Sutskever compared this reservoir of information to fossil fuel, abundant but ultimately finite. Some studies suggest that, at current token‑consumption rates, frontier labs could exhaust the highest‑quality English web text well before the decade ends. Even if those projections prove overly pessimistic, one fact is clear: today’s models consume data far faster than humans

I don't think AGI is right around the corner

“Things take longer to happen than you think they will, and then they happen faster than you thought they could.” - Rudiger Dornbusch I’ve had a lot of discussions on my podcast where we haggle out timelines to AGI. Some guests think it’s 20 years away - others 2 years. Here’s where my thoughts stand as of June 2025. Continual learning Sometimes people say that even if all AI progress totally stopped, the systems of today would still be far more economically transformative than the internet.

Just Ask for Generalization (2021)

Generalizing to what you want may be easier than optimizing directly for what you want. We might even ask for "consciousness". This blog post outlines a key engineering principle I’ve come to believe strongly in for building general AI systems with deep learning. This principle guides my present-day research tastes and day-to-day design choices in building large-scale, general-purpose ML systems. Discoveries around Neural Scaling Laws, unsupervised pretraining on Internet-scale datasets, and o