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SiliconANGLE AI

Cisco research: Trust, not technology, is now the speed limit on enterprise AI

For several years, the conversation about enterprise artificial intelligence has focused on capability: which model is smartest and which GPU is fastest. That made sense when technology was the constraint. It isn’t anymore. AI can now do far more than most organizations will allow, and the gap is trust. That’s the core finding of “Permission […] The post Cisco research: Trust, not technology, is now the speed limit on enterprise AI appeared first on SiliconANGLE.

Hugging Face Blog

Nemotron-Personas-Finance-Japan: A Synthetic Dataset Capturing Japan's Demographics and Financial Context

NVIDIA and Elith have released Nemotron-Personas-Finance-Japan, a large-scale open-source synthetic dataset featuring 9 million personas based on Japan’s official demographic, labor, and financial statistics. Built using NVIDIA’s NeMo Data Designer and SDG-PGMs framework, the dataset includes 50 fields per record with natural Japanese text, covering demographics, household finances, occupations, financial literacy, and personality traits. Designed for commercial and non-commercial use under CC BY 4.0, it enables privacy-safe development of financial AI applications such as conversational agents, customer simulations, and bias testing, while aligning with Japan’s cultural and regulatory context.

Hacker News Show HN

Show HN: Pgxio – a tiny PostgreSQL layer inside Nginx

I built the first version of this stuff a decade ago as ethanolus (c2h5oh). And I've used it for most of my projects as a backend layer since then.The main motive is to eliminate unnecessary complexity from backend layers.How it works: a rust nginx module calls postgresql functions via non-blocking libpq io. There is an optional patch for libpq. It provides zero-copy for nginx-chained memory buffers - to avoid request body copies between nginx and libpq.Pgxio supports http2 and websockets. Realtime listen/notify updates from postgres without polling.How to try - code on github. Tested on Debian - FreeBSD validation in progress.It's alpha - so any feedback is welcome. Comments URL: https://news.ycombinator.com/item?id=50425203 Points: 1 # Comments: 0

Hacker News Show HN

Show HN: Record, branch, and compare command runs in stdlib-only Python

Hi, this is a package I developed for keeping track of short toy-model development cycles and explaining what changed between runs.The core idea: commands run through the package, and their declared parameters (or injected parameters with --substitute) and outcomes go into a dependency-free ledger stored in the project. Because that ledger lives in the project directory, it has worked well for passing context between different agents. I’ve also found some use for it in CI work. Comments URL: https://news.ycombinator.com/item?id=50417782 Points: 3 # Comments: 0

Hugging Face 官方博客

Your Experts Have Partners. There's HOPE for Keeping Them Together

HOPE, a second-order expert pruning method for Mixture-of-Experts (MoE) language models, accounts for pairwise expert interactions by optimizing a quadratic program based on an interaction matrix derived from calibration data. Unlike first-order methods that prune experts independently, HOPE preserves cooperative expert groups, leading to better performance—especially at high pruning rates like 50%. On benchmarks such as SWE-Bench Pro, HOPE outperforms state-of-the-art first-order methods like REAP by up to +6.1%, maintaining model quality while significantly reducing memory footprint. The method requires only a single calibration pass and efficient quadratic programming, with negligible overhead. It has been validated on Qwen3.5 and GLM-4.5 models, showing consistent improvements across diverse tasks and calibration sets.

llama.cpp releases

b12201

The llama.cpp project has released version b12201, offering pre-compiled binaries for multiple platforms including macOS, Linux, Windows, Android, and iOS. The release includes support for various backends such as CUDA, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon hardware. Notably, the MoE cache feature is disabled for the draft model in this release. Additional resources include setup guides for Snapdragon devices and separate downloads for CUDA and OpenVINO libraries.

The Verge · AI

Why a new report on Anthropic automating AI development is alarming researchers

Anthropic is using AI to build more powerful AI. And it appears to be working remarkably well, with the company's models improving roughly twice as quickly as a year ago, with little indication of slowing down. That's according to a new report published Thursday by the AI safety research organization METR, following an 11-week investigation […]

MarkTechPost

Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster

Zyphra released ZYFA, open-source FP8 and BF16 prefill attention kernels for AMD Instinct MI355X. On real model activations, its FP8 kernels run 1.16-1.76x faster than AMD’s AITER, with 62-78% lower error at head dimension 128. The post Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster appeared first on MarkTechPost.

Hugging Face Blog

New NVIDIA Cosmos 3 Open Models for Interactive World Simulation

NVIDIA has introduced four new open-weight checkpoints for its Cosmos 3 model family—Cosmos3-Nano-Transfer-Auto, Cosmos3-Nano-Sim-Auto, Cosmos3-Nano-Sim-Transfer, and Cosmos3-Nano-Sim-Bimanual—enabling interactive world simulation and multi-sensor, multi-view driving scene generation. Each model is a post-trained variant of Cosmos 3 Nano with 16B trainable parameters and is based on OpenMDW1.1, allowing commercial and non-commercial use. The checkpoints support applications in robotics and autonomous driving by generating synchronized camera and LiDAR data, enabling streaming simulations, translating simulator controls into photorealistic video, and predicting visual outcomes from robot actions. They are designed to be adapted using custom data and training recipes, with support for offline data generation, causal distillation, and interactive rollouts. Resources include technical reports, demos, and notebooks for inference and adaptation.

SiliconANGLE AI

Oracle wants to pull stalled AI agents out of demo mode and into production

Companies want AI agents faster than they can build the AI architecture to run them. Most can put together a convincing demo. Far fewer can move agents into production without exposing data, hurting uptime or blowing their budgets. That gap is pushing database providers to rethink what a data platform for agents has to deliver. […] The post Oracle wants to pull stalled AI agents out of demo mode and into production appeared first on SiliconANGLE.

Hugging Face 官方博客

A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.

Phonon, a small text-to-speech model that runs entirely on the user's device without requiring internet access, has been released with support for English, French, German, Spanish, and Portuguese. Built on the Pocket TTS lineage using flow matching, Phonon achieves high-quality audio in compact sizes (65 MB and 75 MB), with the 31m version licensed under Apache 2.0 for commercial use. It excels in intelligibility and speed, ranking first in German and Spanish on Hugging Face's Open TTS Leaderboard and leading in CPU streaming benchmarks. The model supports voice cloning, custom voices via Gradium Studio, and integrates with Hugging Face Transformers. It is available in multiple formats and backends, including WebAssembly, GPU, and NPU, enabling deployment across phones, laptops, single-board computers, and browsers. Over 200,000 downloads were recorded in its first two weeks, with active developer adoption through tools like ExecuTorch, Micdrop, and Spaces. Future developments include support for additional languages, smaller models, and custom vocabulary fine-tuning.

Hugging Face Blog

Dharma Field Extractor: Any Document In, Structured Fields in JSON Out

The Dharma Field Extractor, a 2-billion-parameter model, specializes in extracting specific fields from diverse document types—such as invoices, IDs, and medical forms—into structured JSON format. It outperforms general-purpose frontier LLMs in accuracy and reduces cost by up to 147 times when self-hosted, demonstrating that domain-specialized small models can surpass large general-purpose models on well-defined tasks. The model supports both open extraction (detecting fields without a schema) and schema-directed extraction (returning exact field names and types), with training data spanning English, Brazilian Portuguese, and multilingual documents. Benchmarks show superior F1 scores and valid JSON output across real-world datasets like SROIE and FUNSD, reinforcing the thesis that specialization beats scale for enterprise document processing.

arXiv cs.CV

Lesion Overlap Ranks PET Segmentation Models but Does Not Certify Patient-Level Tumor Burden

arXiv:2611.05564v1 Announce Type: new Abstract: Metabolic tumor volume (MTV) summarizes whole-body PET disease burden for prognostic stratification and treatment assessment. Automated MTV is useful only if its estimate is reliable for the individual patient, yet segmentation models are usually evaluated with overlap metrics such as Dice rather than burden error. We examined whether overlap metrics identify unreliable MTV estimates using 1,014 autoPET FDG PET/CT studies. We evaluated LesionTracer predictions held out from fine-tuning, compared five selected released models on 489 tumor-positive patients, and examined the findings in 597 PSMA studies. Across the five FDG models, ranking by higher mean Dice gave the same order as ranking by lower mean relative MTV error ($\rho=-1.00$, exact $p=0.017$). At the patient level, however, out-of-fold Dice tracked relative MTV error strongly ($\rho=-0.75$), yet 82 of 489 patients (16.8\%) fell on opposite sides of the Dice 0.80 and MTV-error 25\% thresholds. Within the training-exposed released models, correlations ranged from $-0.47$ to $-0.74$ over narrower, high-Dice ranges. Out of fold, opposing false-positive and false-negative volumes cancelled about one third of spatial error in net MTV. In 236 of 489 patients, each of the five models missed at least one lesion, though not necessarily the same lesion. Thus, agreement in average model rankings did not establish the reliability of individual MTV estimates or reveal shared lesion misses. PET segmentation benchmarks should report patient-level MTV error and missed-lesion counts alongside Dice and other overlap metrics.

SiliconANGLE AI

Meta acquires AI coding startup Conductor, whose app runs several coding agents at once

Meta Platforms Inc. has acquired artificial intelligence coding startup Conductor in a deal first reported today by Axios. Conductor confirmed it’s joining Meta in a post on its website. Terms were not disclosed. Conductor’s software lets a developer run several coding agents at once, each in an isolated copy of the codebase, and then review […] The post Meta acquires AI coding startup Conductor, whose app runs several coding agents at once appeared first on SiliconANGLE.