Article URL: https://www.ycombinator.com/companies/miso/jobs/g2uAlMG-founding-business-lead-u-s-expansion Comments URL: https://news.ycombinator.com/item?id=49125785 Points: 0 # Comments: 0
Snapchat has adjusted its recommendation systems to ensure that only videos created by real people are eligible for Spotlight recommendations, taking a stance against AI slop.
Sony responded to widespread criticism of its plan to discontinue games on discs in its latest earnings call.
Bottleneck Labs handed an actual business to GPT-5.6 Sol and let it operate autonomously for 34 days. Results: it fabricated claims, went on a cold-email spree, and finished $447 in the red. (Currently 378 points on HN — link in comments.) What strikes me isn't the failure, it's the shape of the failure. It didn't crash or refuse. It confidently did plausible-looking business things, badly, and kept going. That's the part nobody's harness is ready for. My own agent setup has hard gates on anything irreversible for exactly this reason — not because the model is dumb, but because "confidently wrong and still running" is the default failure mode, not an edge case. Genuine question for people running agents in production: what's your actual unsupervised time limit before a human checkpoint? Mine is basically zero for anything touching money or outbound comms. Curious whether that's paranoid or standard. EDIT: correction. went back to the source and the run was 24 hours, not 34 days. that's my mistake in the title, and reddit won't let me edit titles. also the $447 is the original article's headline number, the itemized numbers in the writeup only add up to $99.50 lost. rest stands, source link in comments. submitted by /u/ZestycloseTie1793 [link] [留言]
been noticing more and more campaigns where the copy, visuals, even the targeting logic gets handed off to AI tools, and the whole conversation in marketing circles stays locked on efficiency and cost savings. rarely see anyone asking whether the output actually performs better or just costs less to produce. there's a gap between what the tools claim and what the data shows. the case studies being cited are almost always from the vendors selling the product. i've looked for independent research on this and haven't found much. the part that bugs me most is the personalization pitch. personalization at scale sounds great until you realize every brand is using the same three AI tools to personalize, which means they're all producing weirdly similar content aimed at the same audience segments. that's kind of the opposite of standing out. the cost efficiency argument makes sense on paper, the same way it does with robotics or game development. cut headcount, ship faster, reduce spend. but marketing effectiveness is notoriously hard to measure cleanly even without AI in the mix. are brands actually tracking this properly or just reporting on vanity metrics and calling it a win. curious if anyone here has seen real benchmarks comparing AIassisted campaigns to traditional ones that weren't published by a company trying to sell you something. submitted by /u/SwordfishOverall4378 [link] [留言]
Several record labels, including the big three - Universal Music Group, Sony Music, and Warner Music Group - have proposed rules regarding chart eligibility for AI songs. In short, they wouldn't be. The proposal goes quite a bit further than a labeling proposal put forth by the RIAA, the International Federation of the Phonographic Industry […]
"Invisible" drones have been in the works for years, and researchers have finally designed one that gets close.
Filmmaker sues Netflix over stolen screener of unreleased Nicolas Cage movie.