Pope Leo XIV may not be able to disarm AI, but he’s got the attention of the industry.

This week, the Steam Deck OLED with 512GB of storage went from $549 to $789, putting it even further out of reach for those who were considering getting one as they came back in stock after months of unavailability. I consider it a tiny consolation that there’s a decent PC gaming handheld that is currently […]

I'm creating a story for my cousin. I think it will be very interesting if this story’s main character can be a 3D character.My project is still in planning stage. I’m writing character descriptions, collecting references from Pinterest and testing some complex shapes using Tripo AI. I plan to continuously improve all the content over time. After I get a version that I like I will put it into Blender for editing and final touches.There is no final version yet but I just want to share this process with the community! I find it is so interesting to watch a story’s concept gradually become concrete lol!! submitted by /u/Final_Floor_789 [link] [留言]

Of course the Hitman devs made a great James Bond game.

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Hey everyone! I'm currently working on a landing page project with quite a few GSAP animations, some ThreeJS, all built with SvelteKit and hosted on Cloudflare. I'm working on this project with a designer friend who has some development knowledge. He can do HTML/CSS/JS and use GSAP. We often work together — he finds clients, handles project management, UI and UX, while I take care of the development side. Since I don't really have a strong "graphic eye", our usual workflow was: I lay the foundations, handle the animations, and let him come back over everything for easing and fine-tuning. Since AI came along, things have gotten a lot more complicated. Don't get me wrong — I use Claude myself to get oriented, review my code, analyse a project, or generate boilerplate code. But I always review the output. My friend, on the other hand, uses it in full vibe coding mode. I just got the latest version of this landing page where he'd pushed his changes, and I was pretty shocked. The code is massively over-engineered for no good reason, hard to follow, and a genuine nightmare to debug. Claude made everything far more complex than it needed to be. To avoid this kind of situation going forward, we've started putting a few ground rules in place: a mandatory review before any merge, and certain core files that only I touch. Not a perfect solution, but it helps keep things from spiraling. Has anyone else had the same kind of experience with vibe coding ? I mean, it does work, but the output feels bloated. Edit : As some people noticed it, i've used an AI to translate that text into english and re-phrase it, since i'm not really fluent in english (isn't that ironic lol). submitted by /u/frenchy_mustache [link] [留言]

Build employee training from team knowledge fast Discussion | Link

submitted by /u/Hot-Upstairs9603 [link] [留言]

This is a genuine technical question here. I've been looking at systems that use an ensemble of ai models to generate probability estimates for open ended real world events. The claim is that consensus across multiple models produces more calibrated estimates than any single model. this makes sense intuitively and has parallels to ensemble methods in traditional ml. But I'm wondering about the theoretical underpinnings more carefully. The standard ensemble argument relies on errors being somewhat uncorrelated across models. but if all the models are trained on similar data distributions and share architectural similarities, how independent are their errors really? are we just getting false confidence from models that all have the same blind spots? also curious about how these systems handle events that are outside the distribution of their training data. novel events are exactly where you'd want good probability estimates and also exactly where you'd expect the most unreliable performance. submitted by /u/onlyJayal [link] [留言]

Read this release today. Some crazy numbers. The tau2-bench number is 98% across all difficulty levels. That is the one that got me because usually these releases post a strong easy score and then quietly die at hard difficulty. This one... claims it holds. For multi-step agent work that actually matters more than most benchmarks. A model that drifts on step 4 of a 6 step chain is a debugging nightmare regardless of what its SWE score looks like. Raw capability is mid, Toolathlon at 49.5, GDPval at 45.8. So this is clearly a reliability play, not a frontier capability play. Depending on your use case that is either fine or a dealbreaker. 198B sparse MoE 11B activ 400 TPS 256K context Apache 2.0 runs locally on M4 Max and DGX Spark. Has anyone actually put this through agent evals or am I just reading the release card. submitted by /u/Skid_gates_99 [link] [留言]

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