Why BigQuery can’t match ClickHouse Cloud for real-time analytics
ClickHouse Cloud delivered 438× better performance per dollar than BigQuery in CostBench. We trace the gap from fresh data arriving to fast answers coming back.
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Models / 01A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.Hugging Face 官方博客
01 / IN FOCUS
A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.
Hugging Face 官方博客Models / 02A Controlled Experimental Evaluation of Configuration and Cache Architecture in LLM Serving EnginesarXiv cs.DC
02 / IN FOCUS
A Controlled Experimental Evaluation of Configuration and Cache Architecture in LLM Serving Engines
arXiv cs.DCModels / 03A Feasibility Condition for Confidence-Gated Tiered LLM InferencearXiv cs.AI
03 / IN FOCUS
A Feasibility Condition for Confidence-Gated Tiered LLM Inference
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JAXModels & infrastructureJAX is a Python numerical computing library designed for accelerators, specifically tailored for high-performance array computations and large-scale machine learning. It efficiently executes the same code across multiple hardware backends, including CPUs, GPUs, and TPUs.
分类推荐榜首KKiroCoding & developmentKiro is an AI engineering tool for developers that organizes requirements into specifications, architectures, and tasks, generates code via agents, and supports the entire workflow—from planning to code review—through property-based testing, execution memory, and integration with development tools.
分类推荐榜首EEvoMapAgents & automationEvoMap is an infrastructure designed for the reuse of AI agent experiences. It organizes reusable strategies and repair assets with validation records via the GEP protocol, offering market- and quality-based ranking to help different agents discover, evaluate, and inherit existing capabilities.
分类推荐榜首OOpen DesignDesign & 3DOpenDesign is an open-source design workspace that can connect to local programming agents, use built-in model keys, or leverage cloud-based models, enabling the creation and editing of prototypes, web pages, presentations, and graphics within a design system; local software is free, while model services incur fees.
分类推荐榜首MMemoAIMusic & audioMemoAI is an AI-powered audio-to-text tool that supports localized processing, eliminating the need to upload data and ensuring privacy and security. It can efficiently transcribe YouTube videos, podcasts, and local audio/video files (such as MP4, MP3, AAC, M4A, etc.) into text, while offering core features like multilingual translation, speaker diarization, real-time subtitles, and floating notes—making it ideal for meeting minutes, interview transcription, content learning, and more.
分类推荐榜首AAI ShortWriting & contentAIS ALERT MANAGEMENT AND SHARED PLATFORM, QUICK COMMAND FOR MULTIPLE SCENARIOS
分类推荐榜首UUdacity AI学院Education & languagesUdacity AI Academy is the gateway to AI courses and learning paths, covering machine learning, deep learning, computer vision, natural language processing, and AI product management, with courses and project-based training available at both beginner and advanced levels.
待评分 · 分类列表推荐ZZoom Workplace is Zoom’s team collaboration platform, integrating video conferencing, chat, calendars, and documents, and leveraging built-in AI to organize discussions, draft messages, and track follow-up tasks.
待评分 · 分类列表推荐妙妙播Marketing & commerceTHE AI LIVE TELECOM SOLUTION FROM THE AD
待评分 · 分类列表推荐AAssemblyAI is a speech AI platform for developers, offering pre-recorded, real-time, and short-form synchronous transcription APIs. It enables the building of voice-based applications by integrating capabilities such as speaker identification, emotion analysis, chapter and summary extraction, as well as speech agent functionality, dictation, and sensitive information processing.
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2026-11-05
Follow the timelineWhy Redshift Serverless can’t match ClickHouse Cloud for real-time analytics
ClickHouse Cloud delivered 1,996× better performance per dollar than Redshift Serverless in CostBench. We trace the gap from fresh data arriving to fast answers coming back.
Your Business, Always Current. Streamhouse - The Data Architecture for the Age of AI.
A Streamhouse is a data architecture that continuously captures, processes, governs and serves current business state and context across distributed systems, making them available to applications, AI agents, and analytics.
2026-11-04
Follow the timelineA Conditional Mutual Information Analysis of Response Variety in Language Models
arXiv:2611.03836v1 Announce Type: new Abstract: Large language models provide a variety of responses at inference (generation) that conventionally depend on a temperature parameter. Using a conditional mutual information approach, a quantitative, entropy based analysis of response variety is given. Three limiting behaviors are defined: arbiter, independent (conditionally) and neutral (deterministic) that span no to maximal mutual information. We give examples of each behavior and suggest their application to: temperature analysis, language analysis, prompt compression, and knowledge graphs.
AI video is having its Stable Diffusion moment
There are lots of models that are as good as OpenAI's Sora now.
An entirely open-source AI code assistant inside your editor
Continue enables you to easily create your own coding assistant directly inside Visual Studio Code and JetBrains with open-source LLMs.
b12094
Optimized TOP_K performance on Hexagon backend by skipping bitonic sort for tiles whose maximum cannot surpass the k-th best value, replacing tile sort with top_k_scan() to reduce 256-float blocks using HVX max-reduce, and inserting only surviving elements into a sorted k-entry list. Supports k values from 2 to 64 and row widths over 1024 in both single-row and multi-row paths. NaN treated as -INF, ties preserve first-seen index. Eliminated scalar reads from VTCM in hex-top-k.
b12096
The update introduces the TOP_K_MASK operation for Hexagon, simplifying the kernel and eliminating redundant host configuration.
b12098
The llama.cpp release b12098 includes accurate support for Qwen3-VL video prompt format and provides precompiled binaries for multiple platforms including macOS, Linux, Windows, Android, and openEuler, with various backend options such as CUDA, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon-specific optimizations. The release also features a standalone UI package and detailed setup guides for specific hardware configurations.
b12099
The update introduces initial support for the LIGHTNING_INDEXER operation in the Hexagon backend, including optimizations for HMX prefill, q4_0 and f32 K support, pipelined HMX paths, improved DMA pipeline with canonical double buffering, and enhanced kernel parameter alignment. It also adds options to force HMX/HVX execution paths and supports dimension collapse.
b12100
The update resolves issues with -hf tag matching in longer quantization names, ensuring tags start at token boundaries. It also adds support for underscores as separators in quantization tags, improving compatibility with filenames like step3p5_flash_Q4_K_S-00001-of-00012.gguf.
b12101
The gguf writer now writes host-buffer tensor data directly to the file, skipping the temporary staging vector, which eliminates per-tensor allocation, zero-initialization, and memcpy operations.
b12102
The ggml library now checks view bounds based on the actual bytes reached by strides, improving accuracy for strided views. Views are required to start from contiguous elements along dimension 0, ensuring correct data access. Convolution windows in delta_net are created with all rows at once to prevent stride-related issues.
b12109
The llama.cpp release b12109 is now available, offering pre-compiled binaries for macOS, Linux, Windows, Android, and openEuler across various architectures and backends including CPU, CUDA, Vulkan, ROCm, OpenVINO, SYCL, and specialized hardware like Snapdragon. The release includes support for Apple Silicon and Intel macOS, multiple Linux distributions, and detailed setup guides for specific platforms.
b12110
The llama.cpp project has been updated to version 0.9.0, with new releases available for multiple platforms including macOS, Linux, Windows, Android, and iOS. The update includes support for various backends such as CUDA, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon-specific hardware. Some builds are marked as disabled or experimental, and detailed setup guides are provided for specific configurations.
Building an Agentic SOC on a Stream
Discover how our security team built an automated multi-agent investigation pipeline that scaled alert triage throughput using a continuous streaming architecture.
Computation in Anonymous Directed Dynamic Networks: Exact Worst-Case Termination Time and Network-Wise Intractability
arXiv:2611.01761v1 Announce Type: new Abstract: We determine the exact round complexity of Counting in anonymous directed dynamic networks. Agents have no identifiers, but $\ell\geq1$ of them are designated as leaders. Every agent knows $\ell$ and its own leader status, while the population size $n$ is unknown and has to be determined by the agents. Communication proceeds in synchronous rounds; the directed communication graph may change arbitrarily and unpredictably from one round to the next, but is assumed to remain strongly connected. We solve Counting with a distributed algorithm that makes every agent output $n$ and halt after a number of rounds equal to $\begin{cases} n&\text{if }n=\ell\\ 2n-1&\text{if }n>\ell\text{ and }\ell=1\\ 2n-2&\text{if }n>\ell\text{ and }\ell\geq2. \end{cases}$ The algorithm works even for networks with loops and parallel arcs and even when agents learn their outdegree only after sending. We prove that this bound is worst-case tight over directed multigraphs for every $n$ and $\ell$ with $1\leq\ell\leq n$. Moreover, the local computation is polynomial (provided that each communication multigraph has at most $2^{\operatorname{poly}(n)}$ arcs): in every round, each agent solves at most one linear system with up to $n$ variables. By standard techniques, solving Counting also enables the evaluation of any computable function of the multiset of agents' input values within $2n-1$ communication rounds. The bound above optimizes the worst case over all networks. For an individual network, we show that even deciding whether an agent already has enough local information to determine $n$ is $\mathsf{coNP}$-complete (provided that the numbers of parallel arcs are encoded in binary). Thus, worst-case-optimal Counting admits polynomial local computation, whereas exact network-wise optimization is intractable under standard complexity assumptions.
Creative roundup: avatars, lightsabers, and LoRA tricks
We take a quick look at the latest creative models, experiments, and community projects.
Daybreak Red is now available in AWS GovCloud (US-East)
Daybreak Red brings GPT-5.6 Cyber to approved customers in AWS GovCloud (US-East) through Amazon Bedrock for authorized cybersecurity work.
Easel AI is now on Replicate
Advanced face swap and AI avatars from Easel AI are now on Replicate.
Every update links back to its source.