标签：#AR

Hey! I'm a CS student and I got tired of not being able to compare MLX inference engines properly — every benchmark out there is either made by the engine's own developers, runs on an M3 Ultra nobody has, or just shows tok/s with zero context. So I built mlx-Chronos — a small open source CLI tool that runs a standardized benchmark protocol on your Mac and lets you submit your results to a shared community leaderboard. What it measures: Cold and cached TTFT (Time to First Token), with a proper methodology — unique prompts per trial, cache priming, no interleaved phases Throughput (tok/s), with mean/stddev/min/max across repeated trials Engine process RSS and system RAM peak, sampled continuously during inference Thermal state and hardware info Supported engines: oMLX, Rapid-MLX, mlx-lm, Ollama (MLX backend) The leaderboard is basically empty right now since I only have an M2 8GB. Would love results from M3 Max, M4, M4 Ultra, or anything with more RAM — that's where things get actually interesting. → Leaderboard: https://igurss.github.io/mlx-chronos → GitHub: https://github.com/igurss/mlx-chronos → Install: pip install mlx-chronos It's early, the methodology is documented (there's a methodology.md if you want to pick it apart), and I'm 100% open to feedback, contributions, and getting told what I'm doing wrong. The goal is just to have one place where you can compare engines on your specific hardware instead of trusting someone else's numbers. submitted by /u/igor__004 [link] [留言]

submitted by /u/Early_Mail9268 [link] [留言]

I used these two prompts on all AI apps to figure out who to vote for in the CA primaries: If you were running for governor of California, what will your big policies be ⁠Out of the candidates that are running in June election, who aligns closest to those policies Gemini, claude, chatgpt all ranked Matt Mahan (Democrat) as #1 Grok chose Steve Hilton (Republican) thoughts on AI use for voting decisions? submitted by /u/No_Mall_7299 [link] [留言]

Most robot foundation model demos are hard to interpret because the impressive number usually comes after task-specific fine tuning. Wall-OSS-0.5, a new open-source VLA release from X Square Robot, is interesting because the report tries to measure what the pretrained checkpoint can do before that extra adaptation step. The setup is a 4B vision-language-action model built around a 3B VLM backbone plus action-generation components. According to the report, the pretrained checkpoint was evaluated on a 17-task real-robot suite without task-specific fine tuning. Four tasks crossed 80 task progress: block sorting, fruit sorting, ring stacking, and a held-out deformable task, rope tightening. The part that seems more important than the raw score is the framing. In language models, nobody would accept only a fine-tuned downstream score as evidence that pretraining worked. With robots, that has been much harder because the evaluation is physical, slow, embodiment-dependent, and expensive. A real-robot zero-shot suite is a useful step toward asking the same question directly: does pretraining itself produce executable behavior, or is it mostly a better initialization? The method is also trying to solve a specific training problem. Continuous action losses are useful for execution, but the paper argues they do not send a strong enough learning signal into the VLM backbone by themselves. Their recipe combines action-token cross entropy, multimodal cross entropy, and flow matching in one stage, using the discrete action-token path as a gradient bridge into the backbone while flow matching handles continuous actions at deployment time. For reference, the code is at https://github.com/X-Square-Robot/wall-x , the paper is at https://x2robot.com/api/files/file/wall_oss_05.pdf , the project page is https://x2robot.com/oss#resources , and the Hugging Face org is https://huggingface.co/x-square-robot . The caveat is obvious but important. Zero-shot still does not solve the hardest man

174 million smart TVs, most of which run Linux. 3.9 billion Android phones. Zero marketing. Tonight, somewhere around the world, a person will press the power button on their Samsung TV. A proprietary Samsung logo will appear. A polished menu will load. They will open Netflix, scroll through recommendations, and pick a movie. They will never know that every frame they see is being scheduled, managed, and rendered by a Linux kernel, the invisible engine that sits between apps and hardware. They will then reach for their Android phone to check something on social media. Another Linux kernel. If they are sitting in a Tesla, the touchscreen showing their charging status is running yet another Linux kernel. The “year of the Linux desktop” debate has been running for two decades. Entire forums exist to argue about whether 2025, 2026, or 2027 will finally be the year Linux takes over the PC market.

The Principle of Least Privilege: Operational Speed's Security Cost While developing a production ERP, delayed shipment reports were always a headache. One of the main reasons behind incomplete reports was the complexity of privilege layers in the system and, often, excessive permissions granted. In this post, I will delve into the costs we pay when we stretch security boundaries in an effort to gain operational speed. The principle of least privilege is more than just a security concept; it's critically important for operational efficiency and system stability. In this article, I will explain the impact of the principle of least privilege on operational speed, the security risks it entails, and how I've tried to strike this balance with concrete examples from my practical experience. My goal is to move beyond superficial definitions and dive deep into this topic based on my real-world field experiences, providing actionable insights to readers. Why Does the Principle of Least Privilege Seem to Hinder Operational Speed? The general tendency is to provide instant access to all relevant tools and data to speed up a task. This can be appealing, especially in an emergency or before a critical delivery. However, the Principle of Least Privilege (PoLP) advocates the opposite: a user or system component should have the absolute minimum privileges required to perform its task. This might initially seem to slow down operational processes. For example, a development team having unlimited SELECT rights to a production database might facilitate running an urgent query. However, the same developer could accidentally run UPDATE or DELETE commands, causing serious damage to the system. Such an incident, instead of speeding up a query in the short term, could lead to hours of downtime and data loss. This is where the long-term risk posed by operational speed, which PoLP is thought to hinder, becomes apparent. Another example is a system administrator frequently using the sudo su co

Intel Foundry's Rio Rancho Facility Moves Toward Glass Substrate Volume Production Reports from Wccftech and Forbes (May 26, 2026) indicate that Intel Foundry's facility in Rio Rancho, New Mexico, is advancing toward becoming the world's first factory to achieve mass production of glass substrates — a next-generation chip packaging technology considered critical for scaling AI hardware beyond current organic substrate limitations. The facility has already begun manufacturing silicon photonics products for external customers and is expected to play a central role in Intel's advanced packaging strategy. Why Glass Substrates Matter for AI Glass substrates address fundamental limitations of current organic (ABF) substrates that are becoming bottlenecks for AI chip scaling: Extreme flatness (<1 μm warpage) enables larger die and chiplet assemblies Low CTE (3-8 ppm/°C) closely matches silicon (2.6 ppm/°C), reducing thermal stress Higher interconnect density due to dimensional stability Better high-frequency performance with low dielectric loss Larger format supporting bigger interposers than organic substrates For AI accelerators that already push CoWoS substrate limits at 5,500+ mm², glass substrates could enable even larger multi-chiplet assemblies. Intel's Advanced Packaging Ecosystem Intel has been building an advanced packaging portfolio: EMIB (Embedded Multi-die Interconnect Bridge): High-density die-to-die connections Foveros : 3D stacking for logic-on-logic packaging Co-Packaged Optics (CPO) : Recently demonstrated glass-core substrate prototypes with CPO Customer Base According to Forbes: Existing customers : AWS, Cisco Reportedly in discussion : Apple, Google, Microsoft, Nvidia, Tesla Commercial Timeline Milestone Timeline Glass substrate R&D announcement 2023 Pilot line (Chandler, AZ) 2024-2025 Silicon photonics production (Rio Rancho) 2026 (active) Glass substrate volume production ~2028-2030 Global Competition Intensifying SKC/Absolics (Korea): Operating pilo

For those who cant afford claude models and wanna use claude code, deepseek v4 pro is closest best and cheapest option. How to use deepseek API inside claude code (easist way ever): We will use AI to replace AI. Just feed your existing claude code this prompt "Yo Claude, you’re expensive af 💀 Do everything needed to fully switch Claude Code to DeepSeek API automatically. Set up the complete settings.json config, API integration, model selection, base URL, env variables, testing, debugging, and optimization for low cost + strong coding performance. Use this DeepSeek API key: "sh......................" Make it fully working, minimal, and production ready." Thats it! Thank me later! submitted by /u/Agreeable-Pen-9763 [link] [留言]

Since AI is a top-grossing buzzword for all students , employers , HRs , managers , scientists , engineers and analysts and all other people working in IT industry and other enterprises accross the Earth from early 2024 to till date. What do you all think about this digital transition of the human world present right now and what are the expectations of tommorow ? submitted by /u/Hzrshx [link] [留言]

Hai Guyz, Ive used many apps and it seems the market is flooded with crappy ones. So far my favorites are HiWaifu and Privee. HiWaifu is by far the best text, it doesn't allow sexy pics, just pg-13 ones. Privee has sexy pics but both the text and pics are limited. Another thing Ive noticed with a lot of AI generators is that by default everyone seems to be east Asian. Why is this? submitted by /u/Jiggalopuffii [link] [留言]

I randomly came across this and honestly I can’t tell if it’s real or one of those AI demos that looks impressive but doesn’t actually work. From what I understand, it’s claiming you can fine-tune models, do image training, test them in a playground, and deploy them as an API from a phone. That sounds a little too convenient, which is why I’m skeptical. I haven’t tried it myself yet, but I’m curious if anyone here has. submitted by /u/Raman606surrey [link] [留言]

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There's a question that keeps surfacing across engineering teams — sometimes in architecture reviews,...

Hi, Relatively new to deep learning. I wanted some opinions on which of these approaches might be best for time series data and spectral analysis. I currently use a GP and it works pretty well, but I’m wondering what the computational tradeoffs and so forth might be. Any ideas? submitted by /u/InevitableCut1243 [link] [留言]

Lightweight local coding agent with emphasis on subagenting rather than stuffing everything into one giant context. The idea is to reduce context rot and kv cache size so as to scale to larger coding tasks using focused parallel workers. submitted by /u/Turbulent-Guest154 [link] [留言]

Sequel to: Learning to Skip Blocks: Self-Discovered Ultrametric Routing for Hardware-Accelerated Sparse Attention Abstract We present Llama Surgery , a method for injecting learned block-sparse attention topologies into pre-trained dense language models without retraining from scratch, distillation, or post-hoc pruning. Starting from a frozen Llama 3.1 8B, we surgically replace each attention layer with a Dynamic Topology Router that maps token embeddings onto the branches of a Bruhat-Tits p-adic tree via factorized Gumbel-Softmax routing. A Continuous Logit Homotopy guarantees that at initialization the injected topology bias is identically zero, preserving the pre-trained manifold exactly. Over training, temperature annealing polarizes the soft routing assignments into hard binary masks, and a Switch Transformer-style load-balancing loss prevents routing collapse. We identify and resolve two critical failure modes: (1) gradient collapse through discrete masking operations, solved by a Straight-Through Estimator bridge that decouples the hard forward mask from the soft backward gradient; and (2) Attention Sink instability, where hard-masking the initial token causes softmax entropy collapse and syntactic degeneration, solved by permanently anchoring Token 0 in the visibility set. The resulting architecture is validated on Llama 3.1 8B fine-tuned on WikiText-2, achieving stable convergence and producing coherent, mathematically sophisticated text while maintaining dynamic block-sparse routing across all 32 transformer layers. A custom Triton forward kernel with Attention Sink and Local Window support, pipelined for Ampere and Hopper architectures ( num_warps=4 , num_stages=3 ), executes the block-sparse prefill phase at O(N) theoretical complexity. To our knowledge, this is the first demonstration of differentiable ultrametric topology injection into a production-scale pre-trained LLM. https://github.com/sneed-and-feed/adelic-spectral-zeta/blob/main/papers/llama_sur

My understanding is that AI won’t do anything if we don’t ask him something, so i was wondering what will happen to AI if no one ask him to do anything. submitted by /u/mansithole6 [link] [留言]

I once submitted an essay with three citations that I hadn't personally verified. The AI had suggested them, and they sounded right. None of them existed. That's not a quirk or a bug — it's exactly how LLMs work. And once you understand why, a technique called RAG starts to make a lot of sense. AI assistants are remarkably good at sounding right. The model isn't lying — it's doing its best with what it knows. The problem is that what it knows has limits, and it doesn't always know where those limits are. Ask one about a recent event, a niche regulation, or anything from a source it's never seen — and it fills the gap anyway. Confidently. That's the gap RAG was built to close. Once you understand how it works, you'll have a much clearer picture of why some AI tools are genuinely reliable and others are just very convincing guessers. Here's what's actually going on. First, What's the Problem? Large language models (LLMs)—the technology powering AI assistants like ChatGPT and Claude—are trained on vast amounts of data from across the internet. That training gives them a remarkable ability to reason, summarize, and generate content. But it also comes with some real limitations: They have a knowledge cutoff. An LLM trained last year doesn't know what happened last month. They can hallucinate. When they don't know something, they don't say "I don't know"—they generate a confident-sounding answer anyway. Wrong facts, fake statistics, invented sources. All delivered with a straight face. They don't know your specific sources. Think of a software engineer asking an AI assistant about their company's internal API documentation, deployment runbooks, or architecture decisions. None of that is in the training data. The model has never seen it — and it will still try to answer. The model isn't lying — it's generating the most plausible answer it can. It just has no way to know when it's wrong. So, what do you do when you need an AI that's accurate, current, and knows your specifi

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

one of the most annoying problems when building AI agents: fix a failure, change something, same failure comes back quietly. built replayd for this. captures failed runs as regression tests and replays them before you ship. catches the failure if it returns after a prompt, model, or tool change. v0.1.2, pip installable, open source. pip install replayd star it if you want to follow progress. submitted by /u/taimoorkhan10 [link] [留言]