“Elon Musk wants Tesla to iterate new AI accelerators faster than AMD and Nvidia.”

“The article highlights an instance of 12,000 lines of refactoring using 10 Claude instances running in parallel.”

“Llama-3.2-1B-4bit → 464 tok/s”

“Compared with the kinetic Langevin sampling algorithm, the proposed algorithm exhibits a higher contraction rate in the asymptotic time regime.”

“Once connected, the Raspberry Pi 5 will use the AI HAT+ 2 to handle AI-related workloads while leaving the main board's Arm CPU available to complete other tasks.”

“The article's target audience includes those familiar with Python, AI accelerators, and Intel processor internals, suggesting a technical deep dive.”

“This article is for those who do not understand the difference between CUDA cores and Tensor Cores.”

“OpenAI will add Cerebras' chips to its computing infrastructure to improve the response speed of AI.”

“The challenge is no longer whether AI can help, but how tightly it needs to be built into research and clinical work to improve decisions around trials and treatment.”

“The new AI supercomputing platform aims to accelerate the adoption of LLMs among the public.”

“Will AI Max Plus chips make seriously powerful handhelds more affordable?”

“AMD announced the latest version of its AI-powered PC chips designed for a variety of tasks from gaming to content creation and multitasking.”

“As AI factories scale, the next generation of enterprise AI depends on infrastructure that can efficiently manage data, secure every stage of the pipeline and accelerate the core services that move, protect and process information alongside AI workloads.”

“As Samsung announces new products for 2026, a key exec talks about how it’s prepared for the next 20 years in TV.”

“"I'm not joking and this isn't funny. ... I gave Claude a description of the problem, it generated what we built last year in an hour."”

“submitted by /u/simpleuserhere”

“The South Korean government funded the Sovereign AI Foundation Model Project, and the five selected teams released their initial models and presented on December 30, 2025. ... all 5 teams "presented robust open-source policies so that foundation models they develop and release can also be used commercially by other companies, thereby contributing in many ways to expansion of the domestic AI ecosystem, to the acceleration of diverse AI services, and to improved public access to AI."”

“Enterprise agent adoption feels like the obvious near-term shift, but the second part is more interesting to me: scientific acceleration. If agents meaningfully speed up research, especially in materials, biology and compute efficiency, the downstream effects could matter more than consumer AI gains.”

“The framework reduces runtime from 84 to 48 hr on the same CPU platform and to 7 hr on an NVIDIA A100 GPU, while producing results consistent with those from the original pipeline.”

“The model successfully predicted drag reduction rates ranging from $-1\%$ to $86\%$, with a mean absolute error of 9.2.”

“FlowBlending achieves up to 1.65x faster inference with 57.35% fewer FLOPs, while maintaining the visual fidelity, temporal coherence, and semantic alignment of the large models.”

“Electron beams with an energy spread of 1% can be generated with the energy transfer efficiency of 10% to 30% in a large parameter space.”

“The spectral evolution shows a transition from thermal (single BB) to hybrid (PL+BB), and finally to non-thermal (Band and CPL) emissions.”

“The discovered accelerator, featuring second differences and cross-product terms, achieved over 75 percent success rate in improving convergence compared to raw sequences.”

“The resulting tracking performance, evaluated on the Open Data Detector, is comparable with the full simulation.”

“CorGi and CorGi+ achieve up to 2.0x speedup on average, while preserving high generation quality.”

“”

“HERO Sign achieves throughput improvements of 1.28-3.13, 1.28-2.92, and 1.24-2.60 under the SPHINCS+ 128f, 192f, and 256f parameter sets on RTX 4090.”

“The survey reviews the technology landscape for hardware acceleration of deep learning, spanning GPUs and tensor-core architectures; domain-specific accelerators (e.g., TPUs/NPUs); FPGA-based designs; ASIC inference engines; and emerging LLM-serving accelerators such as LPUs (language processing units), alongside in-/near-memory computing and neuromorphic/analog approaches.”

“The paper demonstrates that the decoherence decay rates differ between inertial and accelerated frames and that the characteristic exponential decay associated with the Unruh effect can be observed at lower accelerations.”

“The paper estimates the current value of the Hubble parameter as $H_0 = 66.945 \pm 1.094$ using the latest datasets, which is compatible with observations.”

“Achieved over 95% accuracy for road condition classification using deep learning.”

“The paper suggests a novel interpretation of the currently observed acceleration of the expansion of the Universe in terms of such a phase at large (cosmological) scales.”

“CEM significantly improves generation fidelity of existing acceleration models, and outperforms the original generation performance on FLUX.1-dev, PixArt-$α$, StableDiffusion1.5 and Hunyuan.”

“The MoraNet preserved better structural details with lower RMSE and higher SSIM values at acceleration factor of 4, and meanwhile took ten-fold faster inference time.”

“SE-MLP achieves superior prediction accuracy, generalization, and stability.”

“TYTAN achieves ~2 times performance improvement, with ~56% power reduction and ~35 times lower area compared to the baseline open-source NVIDIA Deep Learning Accelerator (NVDLA) implementation.”

“"Now with this changing dark energy going up and then down, again, we need a new mechanism. And this could be a shake up for the whole of physics,"”

“Specializing a small model for a single task often yields better results than using a massive, general-purpose one.”

“The paper highlights the use of a GPU-based EDT and SMPC for high-frequency replanning and reactive manipulation.”

“I decided to build my own solution that runs 100% locally on-device.”

“The paper predicts that gamma-rays are detectable across all facilities for the external shock model, while the neutrino detection prospect is poor. In contrast, both IceCube and KM3NeT have significantly better prospects for detecting neutrinos in the magnetic reconnection scenario.”

“A Bayesian model comparison indicates that the entropic models are statistically preferred over the conventional $Λ$CDM scenario.”

“The framework comprises three core components: (1) a long-video generation framework integrating unified context compression with linear attention; (2) a real-time streaming acceleration strategy powered by bidirectional attention distillation and an enhanced text embedding scheme; (3) a text-controlled method for generating world events.”

“Optimal hardware configuration: high operating frequencies (1200MHz-1400MHz) and a small local buffer size of 32KB to 64KB achieves the best energy-delay product.”

“The paper proposes a two-stage autoregressive adaptation and acceleration framework to adapt a high-fidelity human video diffusion model for real-time, interactive streaming.”

“The article is sourced from ArXiv.”

“BLEST leverages tensor cores for efficient BFS.”

“ADT-Tree achieves speedups of 3.13x and 3.05x, respectively, on MS-COCO 2017 and PartiPrompts.”

“N/A (Article is too brief to extract a meaningful quote)”