标签：#AR

Hi folks, Deciding between these two Mac options has been a challenge for me, so pls help. I know mac is not even necessary for this but just help me to decide between these two options. For the reference, Im a swe student and looking forward to go deep into ml and data science in the near future… EDIT: mac book pro m5 ( base chip) that I’m referring here. submitted by /u/Both-Hovercraft3161 [link] [留言]

I was just thinking about the difference between an LLMs capacity for theory of mind and a human's capacity for theory of mind, and I realize it gets at the heart of what differentiates an LLM from human, and that's the method of how we gather information. LLMs are based on objective data, e.g. text, numbers, pixels, etc. Whereas we as humans, use subjective information, e.g., feelings, sensations, experiences; as well as objective data. Within cognitive science, this would be described as affective empathy vs cognitive empathy. Or in other words, LLMs simply possess a cognitive theory of mind, whereas we have both a cognitive *and* affective theory of mind. The problem I have with figures like Hinton, who claim that AI is already conscious, is that his whole framework is based on the idea that consciousness (subjective experience) is just an artifact of computation (an illusion), and therefore there is no recognition of subjective measure - that reality is only defined by what we can measure objectively (with fixed metrics). I think what this fails to recognize is that in pursuit of reproducible results, which requires fixed metrics, we've thrown out a whole set of other measurements, which is subjective (variable). submitted by /u/flasticpeet [link] [留言]

Hi everyone, I'm an undergraduate student and ML researcher at UC Berkeley. My colleagues and I are working on a project that hopes to fix some of the problems users face with Colab. What are the features you wish it had as an ML professional, researcher, or enthusiast? What're the biggest problems you've faced while using it? Some of the issues that everyone feels (including us) is environment management and kernel persistence. But we would love to hear more from the community. submitted by /u/myplstn [link] [留言]

Clean Architecture on the Frontend: Beyond Smart and Dumb Components djblackett djblackett djblackett Follow Jun 7 Clean Architecture on the Frontend: Beyond Smart and Dumb Components # react # frontend # architecture # typescript Comments Add Comment 14 min read

Including, me. There’s no question the world has experienced significant change, over just the past 10 years. You’ve got standard concerns like AI; questions around whether tech is helping or hurting us - but I want to present another side to the story: one that I’ve got direct experience in. In a new “series” I’ll be rolling out, I’ll be deep diving into my prior experience as a software engineer in this new world where everything is a metric to be improved. Laying out the problem Not to be vague, I’ve referenced the problem in the title. Companies, and by proxy, engineers at the companies, are being driven (by shareholders), to ensure you maximise the time spent on their platforms. Perhaps, that much is apparent and obvious. But, have you stopped to think recently how vehement and aggressive these practices are getting? How many services do you use or rely on that are (subtly or not), making decisions purely just to increase the time customers spend with the software open ? I’m a big believer that software can make lives better . I’ll be quick to announce, though, that as of 2025, there’s a lot of secrecy behind the scenes around why companies are striving harder to hit engagement metrics. It’s a race to the bottom, and as we continue seeing software take up more and more of our time spent, it’s worth exploring just how bad things can get when you’re laser focused on app enegagement. Making something good, isn’t good enough I worked at Flux Finance for 3 and a half years, before unfortunately being made redundant. Flux went on, only months later, to be bought out by Networth. Not terrible; acquisition complete, and everybody’s clapping. As a refresher, Flux was an app designed to make your money journey easier, and make you more financial literate, in fun ways. The motto: “helping 400k+ Aussies win at money ” - with gamification being a major aspect. For Australians, it features: a way to check your credit score articles released regularly to learn about money new

I got fired from my first job, took down a database server with a badly written query, and was...

If someone asked one of the know AI chats for feedback on body, would it be honest or be supportive only submitted by /u/thowing_away48494578 [link] [留言]

Head's true genius—and that of his character, Giles—lay in quietly filling in the gaps in every scene

submitted by /u/ThereWas [link] [留言]

Been experimenting with AI-generated UGC. This whole clip — the face, the voice, the walk — is generated (I used omnigems.ai). No camera, no actor. What surprised me is the "tells" are mostly gone now if you keep the lighting candid (no studio polish), add real skin texture, and let there be natural micro-motion. Studio-perfect is what reads as fake; messy/handheld reads as real. Posting because I'm curious where this community draws the line: is AI UGC fair game for ads, or does \ undisclosed** AI cross into sketchy territory? Happy to share the exact workflow if it's useful to anyone. submitted by /u/New_Measurement_6962 [link] [留言]

I finished building my first portfolio. I want everyone to give their honest opinion on it. No need to hold back. waliimran.vercel.app Thanks you!

I've been spending a lot of time experimenting with AI filmmaking tools lately, and I've noticed something that feels a bit odd. Most AI video tools seem to be built around generating clips: Text → Video Image → Video Start Frame → End Frame But when I think about how films are actually made, the process usually starts with: Screenplay → Characters → Locations → Storyboard → Shots → Film It feels like there's a gap between how filmmakers think about projects and how AI video tools are currently designed. For example, while working on my ai video, I don't really think in terms of generating isolated clips. I'm thinking about scenes, character continuity, locations, visual references, storyboards, and how everything fits together. Maybe I'm wrong, but it sometimes feels like AI tools are optimizing for clip generation while filmmakers are optimizing for story development and visual planning. Do others here feel the same way? How are you currently bridging the gap between screenplay and AI-generated video? submitted by /u/data-gig [link] [留言]

I too have a different personality when you're paying for me submitted by /u/Complete-Sea6655 [link] [留言]

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

Most merchants know they have unused files. Far fewer realize they're accumulating AI-generated media they never intended to keep. There's a problem quietly growing inside thousands of Shopify stores right now. It's not abandoned carts. It's not slow page speeds. It's not even the 400 unused product images you already know you should deal with. It's something newer, and most merchants have no idea it's happening. The Rise of AI-Generated Commerce Content Over the past two years, AI image tools have gone from novelty to routine. Shopify Magic. Canva AI. Midjourney. ChatGPT image generation. Adobe Firefly. Background removers. Lifestyle photo generators. Product shot enhancers. Merchants are using these tools constantly — to mock up new products, test background options, generate seasonal variants, create ad creatives, experiment with lifestyle photography. The workflow feels clean: generate a few options, pick the best one, move on. Here's what's actually happening on the backend. Every time you use Shopify's native AI tools to generate, edit, or enhance an image, Shopify quietly deposits files into your media library. Not just the one you kept. All of them. The rejected generations. The experimental edits. The "let me try one more variant" files. The abandoned attempts from six months ago when you were testing a new product that never launched. Every. Single. One. Most merchants assume the files they don't choose disappear. They don't. The lifecycle looks something like this: ┌─────────────────────┐ │ AI Image Generation │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Rejected Variants │ │ • Drafts │ │ • Test Images │ │ • AI Edits │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Hidden Media Files │ │ Accumulate Over Time│ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ AI Clutter │ │ Invisible Technical │ │ Debt │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Reduced Media │ │ Governance │ │ • More Noise │ │ • Less Visibility │ │ • Hard

For enterprise AI/security teams, what would make AI SPM useful rather than just another category label? Is the important part AI asset inventory data access mapping, agent permissions, prompt/data monitoring, policy enforcement or something else? submitted by /u/Efficient_Western609 [link] [留言]

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If you’ve been following my previous posts, you know I’m a big advocate for Trunk-Based Development and shrinking your pull requests until they almost feel too small. In a perfect world, developers merge code directly into the main branch multiple times a day, everything flows smoothly, and production remains rock solid. But let’s be honest. When you actually try to pitch this to a backend team working on a core system, you almost always hit the exact same wall of resistance. Someone in the back of the room will inevitably raise their hand and ask: “That sounds great in theory, but I’m currently refactoring our legacy checkout service. It’s going to take me four days of deep architectural changes. Are you seriously telling me I should merge half-baked, broken code into the main trunk and push it straight to production where real customers are buying our products?” It’s a completely valid objection. If your only tool for hiding uncompleted work is holding onto a massive, long-lived feature branch, then trunk-based development breaks down immediately. You end up with the exact nightmare we talked about earlier: huge code reviews, painful merge conflicts, and code that rots before it ever sees a live environment. To make continuous delivery actually work without causing catastrophic production outages every single afternoon, you need to decouple two concepts that most engineering teams mistakenly treat as the exact same thing: Deployment and Release . Last article in this category is focused on Trunk-Based Development: https://codecraftdiary.com/2026/05/18/trunk-based-development-roadmap/ The Core Concept: Shifting Left by Decoupling In traditional development setups, deploying code and releasing a feature happen simultaneously. You merge your giant feature branch, the CI/CD pipeline runs, the code hits the live servers, and boom—your users immediately see the new functionality. This model is incredibly high-stakes. If something goes wrong, your only options are rollin

it turns into some unfinished project lying in some GitHub private repo lol submitted by /u/Complete-Sea6655 [link] [留言]