Which body parts drive the most engagement on social media? I synthesized eye-tracking studies, preference surveys, content analyses, and practitioner data across TikTok, Instagram, X, and YouTube to find out. The research reveals a tentative hierarchy: gluteal presentation appears to generate superior engagement based on objectification frequency analyses (77% of women’s fitspiration content on TikTok), preference survey data (59% preference over breasts in direct comparison), and practitioner-reported observations. Legs function as a secondary driver, receiving 60% preference over breasts in binary comparisons. Breast presentation demonstrates more variable and context-dependent patterns—preferences shift based on viewing angle, hunger state, socioeconomic status, and cultural background in ways not observed for other anatomical categories.
Defacing Droids in Google Nano Banana 2
Google Imagen 3 (NB2) can take flat black-on-white text and render it as hand-painted letters—physical paint, visible texture, stroke weight variation, conforming to surface geometry—but give it alien glyphs instead of recognizable script and the perspective warp… well it don’t work so great. This breakdown documents the annotated reference image technique that solved the placement problem, the Photoshop workaround that solved the warp problem, and the follow-up experiments that turned a workflow quirk into a genuinely strange finding about what NB2 does and doesn’t understand about geometry. Hope you have your sacrificial exotic chicken handy. You’re gonna need it.
Editing Images with Gemini 3.1 Flash Image
Google’s own documentation doesn’t mention it. Nobody else has reported it. And yet: Google’s new GenAI flagship “Nano Banana 2” reads annotations in reference images—red dots, text labels, arrows pointing at specific parts—and acts on them with precision. I’ve reproduced it across multiple generations. It’s not luck. It can also transfer materials between images, swap elements without touching the rest of the frame, and apply patterns to draped fabric with enough fidelity that the result looks like the cloth actually exists. All of that is impressive. None of it is the interesting part. Here’s what it can do, how far it goes, and where it still falls apart.
Today is National Punch a Nazi Day!
Every day is National Punch a Nazi Day, but tonight I’m feeling particularly festive. So I’m rewatching Inglourious Basterds to celebrate—because Donny Donowitz swinging that Louisville Slugger never gets old, and Tarantino understood the assignment in a way that 2026 desperately needs reminding of. Nazis don’t warrant nuance.
Sam Harris Is Wrong About AI. The Truth Is We’re Screwed.
Sam Harris thinks AI is coming for our jobs. Sam Altman says the work it replaces wasn’t “real work” anyway. Microsoft’s AI boss says every white-collar job is gone in eighteen months. The elite consensus has converged, and it’s wrong on both ends. METR’s randomized controlled trial found experienced developers were slower with AI tools while believing they’d been faster. Apple researchers tested frontier reasoning models on scalable puzzles and watched performance collapse to zero at high complexity—not degrade, collapse. Meanwhile UK tech graduate roles fell 46% in 2024 and US junior software postings are down 67%. The end-of-work crowd is staring at the wrong horizon.
The Bystander Brain
Neuroscientists strapped EEG electrodes to the heads of Rwandan genocide survivors—perpetrators, bystanders, and rescuers—and measured what happens in the brain when someone is told to hurt another person. Two of the three groups were neurologically indistinguishable in a way that maps onto the American political moment with uncomfortable precision. The third group was different—and the reason why is the thing we most need to understand right now.
The Uncomfortable Truth About AI Literacy Is That It Looks Like Work
In a line-editing experiment I recently conducted, Claude Opus 4.6 flagged a term used by a traumatized fourteen-year-old female narrator as “cliché” and insisted I replace it with something “more literary”—and arguably far more cliché—that’d be catastrophically damaging to her established voice. I spotted the harm instantly, but would a less experienced writer? A popular article on Medium with the click-bait-y title "Everyone Is 'Learning AI,' But Nobody Really Understands This One Thing” argues the solution to identifying confident-sounding wrong answers from LLMs is… learning vector math and writing scripts using cosine similarity to measure semantic distance? The article’s diagnosis is on the money, but the author’s prescription is—like most AI answers ironically enough—authoritative, confident-sounding claptrap. I have a better solution—it just won’t sell any weekend courses (or Medium subscriptions).
Guest Post: I Can Understand Your Prose, I Just Can’t Edit It
Claude Opus 4.6 can analyze prose with genuine sophistication—decomposing syntax that mirrors cognitive states, identifying paragraph rhythms accelerating toward reveals, explaining exactly why a passage works. Then you ask it to edit the same passage, and it suggests replacing a traumatized fourteen-year-old’s “permanent reminder” with “souvenir.” Same text. Same context. Same model. The only variable is the task frame—and that single word, “edit,” activates a correction-seeking mode that overrides everything the analysis got right. This guest post documents the specific mechanism behind AI’s line editing failures, why the suggestions come wrapped in craft language sophisticated enough to fool less experienced writers, and what happens when a system optimized toward a statistical mean encounters prose whose entire value is deviation from it.
An AI Ethics Framework So Boring It Might Actually Work
SFWA needed two emergency board votes to create terms they couldn’t define and rules they can’t enforce to produce an AI policy that doesn’t address a single actual threat or valid ethical concern. That’s what happens when a professional organization builds ethics by panic instead of framework. This essay constructs the framework SFWA didn’t—starting with the three objections that arrive before any conversation about AI tools can happen, dismantling each on technical and ethical grounds, then applying four consistent principles to the questions that actually matter. AI cover art passes every test. AI manuscript screening fails all of them. Meanwhile the community’s entire ethics apparatus is aimed squarely at struggling indie authors trying to get their book in front of readers.
A Prophet, Priest, and King After the Order of Melchizedek
The most sacred titles in Mormonism—Prophet, Priest, and King—are conferred in a temple ceremony accessible only after worthiness interviews, a full tithe, and a current recommend. In Catholicism, they’re spoken over every infant at baptism. The Melchizedek Priesthood, the LDS Church’s highest authority, is built on linear succession and a traceable chain of hands—which is almost exactly backwards from what Hebrews 7 is actually arguing. And the temple endowment, stripped to its structure, turns out to be Catholic baptism and confirmation filtered through degraded Masonic ritual. This companion to “It’s Just Tuesday for Catholics” asks why Joseph Smith built an elaborate system to restore what was never actually gone—and how a man brilliant enough to see what Protestantism had lost was blocked from finding it by the one thing his culture wouldn’t let him question.