标签：#AR

“I don't want recommendations! I know what I want to watch."

https://gemini.google.com/share/1b2ff803d882 I'm sorry earlier today i made a post comparing ChatGPT and Gemini . I asked Gemini to build a prompt and gave it to him in another chat and i got this... submitted by /u/ObjectiveOrchid5344 [link] [留言]

submitted by /u/andix3 [link] [留言]

Demis Hassabis has tightened his AGI timeline to 2029, making him the most aggressive sitting frontier-lab CEO on record with a public forecast. In an Axios interview, Hassabis named one or two remaining technical breakthroughs DeepMind needs to clear within three years. DeepMind's Co-Scientist multi-agent system is already live across all 17 DOE national labs, providing the kind of real-world deployment data that likely informed the revised estimate. Open questions Which specific technical breakthroughs Hassabis identified as remaining: the Axios interview did not name them publicly. Whether Co-Scientist's DOE deployment includes autonomous decision-making capabilities or operates under strict human oversight protocols. How other frontier lab CEOs (Sam Altman, Dario Amodei) will respond publicly to the 2029 anchor, given no comparable on-record forecast exists as of May 2026. source : https://aiweekly.co/alerts/deepmind-ceo-hassabis-moves-agi-deadline-to-2029 submitted by /u/Justgototheeffinmoon [link] [留言]

The Chinese company is adapting to the demise of Moore’s Law, which guides chip production. It could complicate US chip dominance.

AI tools are getting better quickly and many technical skills are becoming easier to automate. I often think about this: What AI-related skill will still be truly valuable in 5 years? Not using ChatGPT more effectively " but actual long-term skills that will still matter even as AI models get better. My thoughts are: * problem solving * really understanding systems * checking AI outputs * good communication and setting context I'm interested, in hearing your thoughts. What skill do you think will remain important despite AI advancements? submitted by /u/FollowingSuitable941 [link] [留言]

AI videos that are animated, unrealistic, or only have a little AI may still hide their origins, though.

I hate the way AI talks back to me. Its so proper, so robotic, every response feels like a help article. I wanted something that actually knew who i am, my beliefs, my history, what shaped me, the positions i hold and why. Not a generic assistant that treats every question like it came from nobody. So i got to thinking, who better to talk to than myself? So i built it over a weekend. Heres what I did and how you can do it too. Step 1: Export your Reddit data Go to reddit.com and click your profile icon in the top right, then hit Settings. Scroll down to the bottom of the page and youll see a section called "Data Request." Click "Request Data Export" and Reddit will email you a download link within a few hours, sometimes longer depending on how much history you have. The zip file will contain your posts and comments going back to when you created your account. Mine was about 21,000 comments over two years. Once you have it, open the CSVs in excel or just upload them directly into Claude and ask it to help you make sense of the structure. The raw data is ugly but everything is there, the text of every comment, the subreddit it was posted in, the date, all of it. One thing worth knowing: you can go way deeper than just Reddit. I looked into Google Takeout while i was doing this and it was honestly a little scary how much data they have on you. If you want to go deeper Google Takeout is wild, i didnt realize how much data they actually have on you until i went through it. Search history, location history, YouTube, Gmail, its all there and its all exportable. I thought about pulling my SMS history too but that felt wrong, those conversations are with real people who didnt agree to any of this so i left it alone. Reddit was enough for me and honestly if youve been on here for years and actually say what you think in the comments, you probably have more to work with than you realize. Step 2: Build the personality document and this is where the real work is Dont just tell t

I recently wanted to see whether an AI agent could self-improve a harness to solve terminal bench tasks. It’s possible for an AI agent to propose a meaningful one-time change to the harness, but after experimenting with this for a couple of weeks, I think the continuous self-improvement is mostly an experiment-systems problem. The system needs a way to decide what kind of improvements can safely compound. Turns out there's a lot of parallels to coding-agent customization (e.g. SKILLS.md etc..) too. I wrote my experience of building such system here, including the successful and failure attempts during the process, and how I approached the self-improvement loop. It's not intended as a benchmark claim but more of a systems/research writeup. https://www.henrypan.com/blog/2026-05-25-self-improvement-harness/ submitted by /u/Megadragon9 [link] [留言]

PR exec says Govee "did not meet the standard required."

Last month NVIDIA released SOL-ExecBench , a new benchmark of 235 production CUDA kernels lifted from DeepSeek, Qwen, Gemma, and Kimi. We took several top-ranked AI-generated submissions and tried using them in production workloads. Many of them broke, sometimes in surprising ways. One of those kernels is the fused embedding-gradient + RMSNorm backward pass, which runs at the end of every transformer training step. We took the fastest submission on the benchmark for it, and dropped it into the training loop of a small transformer. The kernel had passed the benchmark's verifier with room to spare. But in our training run, the loss diverged and never recovered. We started debugging. Replace the dataset distribution with uniformly sampled tokens, the divergence vanishes. Swap SGD for AdamW, also vanishes. This is the worst kind of bug for research. Symptoms and masks both look exactly like "the idea didn't work". It's the type of bug that can make researchers spend a long time debugging without knowing what's at fault: the dataset? the research idea? the architecture? or the implementation itself? Turns out, the actual bug is that the embedding-gradient half of the kernel accumulates in bf16 instead of fp32. Embedding backward sums many small gradient contributions into each token's row of the embedding matrix. With uniform random tokens the contributions spread evenly and bf16 precision is enough. In real text, a handful of token IDs end up with thousands of contributions: the small ones round to zero against the growing accumulator, and the high-frequency rows drift. AdamW's per-parameter normalization absorbs the resulting multiplicative bias, so under AdamW the same drift is invisible in the loss. The other broken submissions had different bug shapes (all interesting). More examples in our blogpost . submitted by /u/laginimaineb [link] [留言]

I am wondering how are businesses integrating AI while protecting their data? submitted by /u/pappugulal [link] [留言]

These WIRED-tested computer speakers, from stereo speakers to surround sound, will suit any budget.

submitted by /u/Alone-Competition-77 [link] [留言]

As Cognition reaches $492 million in annualized revenue run rate, it more than doubled its valuation in eight months, it says.

AI can now realistically simulate massive crowds and public events. The scary part isn’t the quality anymore. It’s how quickly people are discovering creative ways to use it. Reality online is about to get very confusing. 💀 submitted by /u/Old_Establishment287 [link] [留言]

I'm working on a project that translates any language into English. So far, I've tried NMT models like NLLB, MADLAD, and SeamlessM4T v2. The main issue is that they struggle with proper nouns such as: - names - places - dates - organizations I also tried LLMs like Gemma 4, Qwen 3 4B, and Aya Tiny Global, but the issue still persists. The LLMs sometimes partially translate or modify entity names as well. I even tried NER masking / placeholder replacement before translation, but multilingual NER itself becomes a bottleneck. Most NER models only work reliably for a limited set of languages, while my dataset contains 100+ languages, including many low-resource ones. How do production systems usually handle this problem? Are there better multilingual translation models, multilingual NER approaches, or decoding techniques for preserving entities properly? Requirements: - Support for 100+ languages - Runs locally on an RTX GPU - Model size under 7B - English is always the target language. submitted by /u/Illustrious_Age_2792 [link] [留言]

Designed by Jony Ive and a host of ex-Cupertino colleagues, the Luce shows us what might have been had Apple made good on its $10 billion bet.

Obviously there are like hundreds of image gen websites and apps now that AI has become widespread. ChatGPT - not bad but looking for something more robust Midjourney - works well but kind of burns through money quickly Looking for suggestions. submitted by /u/jimmy-got-paid [link] [留言]

I built a PINN implementation in Python to solve two problems as part of a physics exam project: the damped harmonic oscillator (2nd-order ODE) and the 1D viscid Burgers' equation (nonlinear PDE). Both forward and inverse problems (to estimate unknown equation parameters from data) are implemented for each problem. The repo includes source code, sample outputs, and the written exam report (PDF). Beyond the standard PINN training setup, I ran a comparison against non-physics-informed baselines and specifically investigated extrapolation behavior, i.e. how well the models generalize outside the training domain, and finally made statistical analyses of the parameter estimation performance. GitHub: https://github.com/desdb6/pinn-dho-burgers Ready-to-run demo scripts are included, and the modules are structured to be importable so you can write your own training scripts for more customization. This is not novel research, just a clean student implementation, but hopefully useful to others learning about PINNs. Happy to answer questions or receive feedback in the comments. submitted by /u/Reversed456 [link] [留言]