Trajectory is betting the rapid iteration cycle that supercharged vibe-coding can help all kinds of companies build AI products that learn continuously.
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]
China's AI boom is producing world-class talent, and Beijing is increasingly reluctant to let them go elsewhere.
The office was created a year ago and seemingly named for a far right European plan to expel minorities and immigrants from Western nations. It now works, a source says, with little to no oversight.
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
The database provider is eyeing a public debut within the next few years.
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]
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Stay on top of what’s going on in AI this summer Here at MIT Technology Review, we understand exactly how relentless the pace of news from the world of artificial intelligence…