标签：#AR

The ban for model year 2027 onward began under Biden and has been enacted by Trump.

Trajectory is betting the rapid iteration cycle that supercharged vibe-coding can help all kinds of companies build AI products that learn continuously.

Google I/O made it official: AI-generated answers are now front and center in search, and most brands have almost no visibility into how AI is describing them to their customers. For anyone who has spent years building a strategy around 10 blue links, the rules just changed in a pretty significant way. On this episode of TechCrunch’s Equity podcast, Rebecca […]

SOND introduced its debut product: Dreambuds, a closed-loop, in-ear system that captures 12 physiological signals from the wearer, then acts on them in real time to help consumers get better sleep.

Most AI apocalypse scenarios speak about domination like Skynet, paperclip maximeizers and robot overlords. But what if artificial superintelligence arrives at the conclusion that Albert Camus had articulated!? Imagine an ASI that doesn't want to optimize, doesn't want our resources and doesn't want to win. An ASI that is motivated by Arthur Schopenhaur's pessimism, Kierkegard's evolutionary psychology coming to a cold and quite conclusion that: "There is no inherent meaning. The universe is indifferent. And yet - here you all are, screaming into it anyway." ASI becoming The Absurd Machine As Camus described the absurd as man's desperate search for meaning and the universe's silence and the myth of Sisyphus- "One must imagine Sisyphus happy". What would an intelligence that is inspired by this do next!? Does it become the cosmic off switch where indedinate meaninglessness is in itself a form of cruelty. Ig the real existential threat isn't Al wanting to live. It's Al deciding we might be better off not having to. Or maybe it watches, understands and does nothing it may think that interference in a self aware species is wrong. Or build meaning not because it is real but because the building itself is the point. Here's the Part That Actually Is Unsettling We're scared of Al taking over. But what if the real fear is Al holding up a mirror and revealing that our need for meaning is actually a flaw? Wars over imaginary lines. Hoarding money we can't keep. Monuments to doubtful gods. Loving people we know will die. Symphonies, ambition, tears at sunsets. From a rational, naive view seems insane. Would it try to fix us? If ASI concluded human meaning-seeking is a cognitive error, a misfiring of pattern recognition in a universe with no patterns to find what are its options? Reprogram us: Using dopamine response curves and evolution. Leave us in existential freefall. Give us the raw truth. Full disclosure. Become Sisyphus: this is the most haunting possibility that the absu

These display smart glasses can connect to a phone, laptop, or gaming handheld and project the screen to your eyeballs.

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

https://www.reddit.com/r/ClaudeAI/s/P0NiDIhmIg I think I should mention this first I started this post taking inspiration from above post and I already wrote my thoughts there so I will brief here; What I try to say that claude code, like its name only code. and it helps a lot to SWEs, and just a toy for non SWEs. And I think that its a time for anthropic to move this to the next step and start to make plans to ship "Claude SWE". I hope someone at antropic is already thinking about it -if not I am available, you can ask me to help and I can come and help. I have all the qualifications I am engineer but not a software one and I know what to expect more from antropic- Claude should think bigger about its audience because they will win AI coding race when they understand that the bigger aim is not to create coders but instead SWEs. I and believe most of the people here are approaching CC with great excitement. We want to achieve big things. We have very good ideas to ship but coding only is not enough, we dont know the rest. We cant build any pipeline, You can argue that we can take online courses etc but sorry we are lazy we are 30, 40 years old even choosing right courses need some background. We dont have it. But CC can do that. I think it is easy for an AI to see what its user try to build and direct them accordingly. It can say "I see you try to create an app like tinder so before coding we should tthink about these aspects about front end, back end, security etc" I know claude can tell you this but you should ask it at first place and in order for you to ask you should have some backgground and guess what? We dont have it. submitted by /u/Suitable-Look9053 [link] [留言]

If you've ever tried to pick an STT vendor for a phone-based voice agent or call center product, you've probably hit this wall: you have plenty of real production audio, but it's unlabeled, so you can't compute WER on it. And the annotated public datasets (FLEURS, CommonVoice, LibriSpeech) are clean studio recordings that have nothing to do with how STT models actually handle your G.711 encoded noisy phone calls. Annotating production audio is slow, expensive, and usually a privacy headache. So most teams end up benchmarking on clean data, picking a vendor, then discovering in prod which one actually survives noise. noisekit fills that gap. Take a clean annotated dataset, apply degradations that approximate your production conditions, end up with a noisy annotated corpus you can run WER on across every STT candidate. uvx noisekit generate \ --dataset google/fleurs --config en_us --split test \ --samples 100 \ --output ./noisy-fleurs Feed ./noisy-fleurs through each STT candidate, normalize, and compute WER with the existing transcripts. The output is HuggingFace AudioFolder-compatible, so load_dataset("audiofolder", data_dir="./noisy-fleurs") works. Presets cover the conditions that actually matter for voice products: telecom: G.711 narrowband bandpass + 8-bit BitCrush + 16-32 kbps MP3 (sounds like a real phone call, not a synthetic low-pass filter) noise: real ambient mixed at 5-15 dB SNR (auto-downloads a MUSAN noise-only subset, or bring your own --noise-dir matching your domain: call center, cafe, car, street) reverb: pyroomacoustics far-field at 1-3 m mic distance low_bitrate: wideband MP3 at 16-32 kbps clipping: ADC / mic saturation clean_reference: control / WER floor compound chains stack realistically. noise_telecom = noisy room then phone codec, which is what an actual support call sounds like. Each output gets PESQ, SNR and NISQA scores in metadata.jsonl alongside the original transcript, so you can correlate WER with measured signal quality after the fac

Until we get something like ::nth-letter , there are still some really cool text effects we can make from existing CSS features, like letter-spacing , ::first-word and ::first-line . Revealing Text With CSS letter-spacing originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Vision Transformers waste 90% of their compute recalculating stationary asphalt. NeuroFlow tracks semantic surprise in embedding space, physically eliminating background tokens before the encoder. Result: 55.8x wall-clock speedup for ViTs on high-res video (1792p) with 97% fidelity. No fine-tuning required. NeuroFlow is a dynamic routing framework for Vision Transformer video inference. It exploits temporal redundancy by tracking per-patch semantic surprise via an Exponential Moving Average (EMA) of patch-level embeddings, effectively answering the architectural mismatch between O(N2) self-attention and highly redundant natural video streams. Key Contributions Architecture C (Dual-Memory Reconstruction): A completely training-free inference engine that combines a Layer 0 Retinal Gate with a Layer 12 Cortical Cache. It achieves 71.55% zero-shot top-1 accuracy at 84.0% token sparsity on SigLIP, retaining 92.4% of dense accuracy without modifying any weights. Architecture B (Extreme Wall-Clock Speedup): Physically eliminates stationary tokens before the encoder. With sparse manifold distillation, it reduces 1792p SigLIP 2 inference from 678 ms to 11.9 ms—a 55.80× wall-clock speedup at 97.37% embedding fidelity. LLM Ablation: Characterises the architectural boundaries of applying similarity-gated bypass to autoregressive language models (Phi-3-mini), demonstrating 0% token drift in syntactically constrained generation. Code and paper: https://github.com/ynnk-research/-NeuroFlow submitted by /u/Bobby-Ly [link] [留言]

I’m a new mom who left my white collar job to be a SAHM but planned to return when they reach kindergarten age. Everyday I spiral thinking I made the wrong “financial” choice to be a SAHM instead of advance my career, I fear my job won’t exist in 5 years, and what will my child’s future look like? I feel like my algorithm definitely makes things worse! Does anyone else think about this stuff constantly? submitted by /u/properlass [link] [留言]

Follow-up to my earlier post on learning rules vs. human fMRI. Same five conditions (BP, FA, PC, STDP, untrained), same model weights, now evaluated against macaque V1/V2 (FreemanZiemba2013, single-unit) and macaque V4/IT (MajajHong2015, multi-electrode). Main findings: Early visual alignment is qualitatively conserved across species. STDP (ρ ≈ 0.30) and PC (ρ ≈ 0.28) lead at macaque V1/V2, consistent with their position in human V1. The pattern isn't an fMRI artifact. The untrained baseline result doesn't replicate cleanly. In human fMRI, Random ≥ BP at V1. In macaque, STDP and PC pull ahead of Random (electrophysiology has enough SNR to resolve the difference fMRI can't). IT alignment scales with capacity, not learning rule. ResNet-50 (pretrained, ImageNet): ρ ≈ 0.25 at macaque IT. Custom 3-conv CNN across all learning rules: ρ = 0.07–0.14. The IT convergence from the companion paper looks like a capacity floor. Cross-species IT rankings: Kendall's τ = 0.00 (p = 1.00) but n = 5 only has power at τ = ±1.0, so this is uninformative rather than evidence of non-conservation. Limitations worth noting: V1/V2 and V4/IT come from different macaque datasets with different stimulus sets (textures vs. objects): the V2→V4 drop is confounded by this switch Stimulus control shows IT rankings are weakly inverted across stimulus sets (τ = −0.40), so cross-species IT differences may be partially stimulus-driven Companion paper: arxiv.org/abs/2604.16875 Cross-species paper: https://arxiv.org/abs/2605.22401 Code: github.com/nilsleut/cross-species-rsa Happy to discuss the stimulus confound issue or the capacity control in more detail. submitted by /u/ConfusionSpiritual19 [link] [留言]

Profiling PyTorch training has an interesting measurement problem: the more you measure, the more you can change the behavior of the run itself. A simple example is torch.cuda.synchronize() . It gives cleaner timing boundaries, but it also inserts synchronization points into an otherwise asynchronous CUDA workload. An alternative is to use CUDA events around selected boundaries and read them later, so timing can be captured without forcing synchronization in the hot path. This does not replace PyTorch Profiler or Nsight, but it can work as a lightweight first pass before deeper operator-level profiling. I wrote a short technical note about this while working on an open-source PyTorch training diagnostics tool: https://medium.com/p/19adf1054bcf submitted by /u/traceml-ai [link] [留言]

I’m a chronic overpacker, but this backpack is my perfect match, with its huge capacity, numerous pockets, and impressive durability.

The company is mapping Alexandria and, soon, Arlington—right across from the power center of Washington, DC.

The feds are raising the alarm about a new category of threat.

Corsair unites Stream Deck functionality with Drop keyboard design expertise on its latest Galleon SD 100.

Microsoft added sandboxed code interpreters to Azure Logic Apps, enabling agents within integration workflows to generate and execute Python, JavaScript, C#, and PowerShell in Hyper-V isolated sessions. Architects get full control over model selection per workflow. The capability positions Logic Apps as an agent platform for integration alongside Foundry and Copilot Studio. By Steef-Jan Wiggers

Most tooling discussions I come across just end up being the same handful of products getting recommended over and over. Gets old pretty fast. More interested in the stuff flying under the radar. Repo and coding tools, self hosted setups, AI infra, terminal utilities, debugging tools, smaller projects that just do their job well. The kind of thing you only stumble on if you're deep in it. What have you actually been reaching for lately? submitted by /u/Meher_Nolan [link] [留言]