In this episode, Ray Cochrane digs into “algorithmic outing,” new research showing that social feeds can infer your sexual orientation before you have consciously come out. He also covers Meta’s privacy-aware AI infrastructure, Alberta’s 466-million-line code scan with Claude, NVIDIA on reinforcement learning, and the many journeys of learning Rust. Along the way, he hits Google DeepMind’s A24 deal, WhatsApp usernames, and scuba-diving cyborg cockroaches. Finally, he looks up with Webb’s puzzling early universe, NASA’s emergency telescope rescue, and a gorgeous aurora from orbit.

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Full Summary

Cochrane opens with a quick personal update. He hopes listeners had a good holiday weekend, and he shares that he spent his time working his other job at Oregon’s Finest, chatting with people around Portland. Because his Blurbry workweek tends to be solitary, he refills his social meter on the weekends. He then recalls a Saturday night out with coworkers at the Hungry Tiger before turning to the lead story.

Algorithmic Outing: When Your Feed Knows Before You Do

Cochrane leads with new research from Australia that identifies a phenomenon called “algorithmic outing.” In short, the recommendation systems behind your social feeds can infer your sexual orientation or gender identity and start serving related content before you have worked it out yourself. Importantly, the study is small and qualitative, built on in-depth interviews with twenty LGBTQ+ adults in the Hunter region of New South Wales and published in the journal Gender, Place and Culture.

The mechanism is engagement signals: what you like, who you follow, and how long you linger on a post, a metric the industry calls dwell time. Lead researcher Dr. Justin Ellis of the University of Newcastle notes that several participants said the algorithm “knew” they were queer before they did, an experience that felt validating for some but frightening for others in public settings. For Cochrane, the deeper worry is what else that hidden pattern encodes, from upbringing to mental health, and where that data ultimately gets sold.

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Meta’s Blueprint for Privacy-Aware AI Infrastructure

Next, Cochrane turns to a sharp engineering piece from Meta on privacy-aware infrastructure. The core challenge is that a system must understand what a piece of data actually is before any privacy rule can protect it. A field named “age,” for example, might describe a person in one place and a cache setting in another. Meta’s answer deploys a large language model only on the genuinely ambiguous cases, then distills what it learns into fixed, human-reviewed rules.

The payoff is concrete. According to Meta, those deterministic rules already handle about 85 percent of the traffic, and only the last 15 percent falls back to the model, which costs roughly 400 times more compute. Cochrane loves this edge-case approach. However, he contrasts it sharply with the AI-everywhere software he wrestles with at his weekend job, which he says the heavy AI reliance genuinely makes worse and harder to audit.

Alberta Scans 466 Million Lines of Code With Claude

This one comes from Anthropic, and it ties directly to Meta’s theme. A team inside Alberta’s Ministry of Technology and Innovation used Claude to scan 466 million lines of code in about twenty hours, a review Anthropic estimates would have taken humans roughly six and a half years. Notably, they ran around fifty AI agents in parallel, essentially an automated red team and blue team probing the systems at once. For Cochrane, this is the good version of AI in production: cleaning up and locking down real systems rather than running the show unsupervised.

NVIDIA on Reinforcement Learning for AI Agents

On the AI-building side, Cochrane walks through an NVIDIA developer piece on reinforcement learning for agents. Reinforcement learning rewards a model for good behavior rather than showing it the right answer, much like training a dog with treats. Additionally, he clears up a common mix-up. NVIDIA treats RAG, retrieval-augmented generation, as a separate tool: reinforcement learning changes how a model behaves, while RAG changes what facts it can reach.

GitHub Retires Two Gemini Models

Meanwhile, GitHub is retiring Gemini 2.5 Pro and Gemini 3 Flash across all of Copilot on July 31. The migration paths are Gemini 3.1 Pro and Gemini 3.5 Flash. Cochrane flags it as a sign of the times, since tools that felt brand new a couple of years ago are already getting sunset. He also wonders how quickly today’s “AI-optimized” chips will turn over as the models keep changing.

The Many Journeys of Learning Rust

One for the programmers, and Cochrane makes no secret of loving Rust. The Rust blog’s Vision Doc series explores how people actually learn the language, which is built around memory safety and its strict borrow checker. Honest themes surface throughout, including “clone guilt,” where beginners refuse to copy anything, and “silent attrition,” the learners who quietly bounce off. His take stands: getting your brain onto a memory-safe language rewires how you approach a problem.

Google DeepMind Partners With A24

In an interesting collision of worlds, Google DeepMind is teaming up with A24, the studio behind Hereditary and Everything Everywhere All at Once. The two call it a first-of-its-kind research partnership, with DeepMind researchers and A24 building creative tools shaped by the artists who use them. Cochrane adds a detail worth noting: Google also invested in A24, so this is money on the table, not just a research handshake. For now, though, the announcement stays deliberately vague, with no named films or products.

Google’s $1 Million Africa Indie Game Fund

Another one from Google, and it is good news for developers. Google is launching an indie games fund for sub-Saharan Africa, a region whose gaming scene is growing about as fast as anywhere. The fund puts up $1 million across ten local studios, each receiving between $50,000 and $200,000 plus mentorship and hands-on support. Applications close at noon UTC on July 31.

WhatsApp Usernames Are Here to Reserve

WhatsApp is finally moving off phone numbers as your identity. With usernames, someone can start a conversation with you without ever seeing your number. Starting this week, you can reserve the name you want ahead of the full launch later this year. To claim yours, head into Settings, then Account, then Username.

Intel Sets Its Q2 Earnings Date

Cochrane flags a date worth watching for anyone tracking Intel. The company reports second-quarter results on July 23, right after market close, with an earnings call at 2 p.m. Pacific. Given recent US government investment and a shifting chip landscape, he is curious how the domestic chipmaker is holding up.

Your Smartwatch Might Spot Illness Before You Do

Shifting to health, Engadget reports that the wearables-plus-AI wave is starting to deliver. These devices excel at catching the moment your body drifts off its own baseline, often the first nudge to get checked out. A 2025 study from Texas A&M and Stanford suggests smartwatches can detect early signs of COVID or the flu within hours of infection. Additionally, Apple Watch’s irregular-rhythm alerts have flagged AFib correctly about 84 percent of the time.

Working Memory and Consciousness

Here is a heady one from Scientific American, written by philosopher Henry Taylor at the University of Birmingham. Working memory is the mental scratchpad holding whatever you are doing right now. Taylor opens with the doorway effect, that blank moment when you enter a room and forget why. Intriguingly, when information leaves working memory, it seems to leave conscious awareness at the same instant, a link drawing fresh attention across psychology, philosophy, and neuroscience.

Scuba-Diving Cyborg Cockroaches

Now for the wild one. Scientists have built tiny diving suits that let Madagascar hissing cockroaches survive underwater for up to three hours, while an unequipped roach suffocates in minutes. The 3D-printed suit feeds oxygen through tubes into the insect’s breathing holes, called spiracles, using a chemical ge...