01:00
As AI agents reshape the web, Google opens Gemini and Search to more apps
A new developer platform will let businesses bring their services to Google's AI experiences, potentially changing how consumers discover and use apps.
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Data & engineering / 01Introducing ClickHouse Security in private preview, built on RunRevealClickHouse
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Introducing ClickHouse Security in private preview, built on RunReveal
ClickHouseData & engineering / 02Langfuse launches AI GatewayClickHouse
02 / IN FOCUS
Langfuse launches AI Gateway
ClickHouseCompute & infrastructure / 03b12229llama.cpp releases
03 / IN FOCUS
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EvoMapAgents & 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JJAXModels & 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AAI ShortWriting & contentAIS ALERT MANAGEMENT AND SHARED PLATFORM, QUICK COMMAND FOR MULTIPLE SCENARIOS
分类推荐榜首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RRowsData & analyticsRows is a spreadsheet tool that combines AI analytics, data integration, and interactive reporting, enabling you to organize documents and business data using natural language, merge datasets, generate reports, and build computational models. The official website currently indicates that it has been integrated with Superhuman and provides a corresponding link to continue using the service.
待评分 · 分类列表推荐IIamIPIndustry applicationsIamIP is a patent platform that provides AI-driven search, analysis, and monitoring tools to help users conduct patent research and management more efficiently.
待评分 · 分类列表推荐EElicitKnowledge & researchElicit provides semantic search, research report generation, literature screening, and data extraction for scientific research. It enables traceable answers within source materials and organizes evidence through a literature library, research agents, and ongoing tracking workflows, making it ideal for advancing from initial problem exploration to systematic reviews.
待评分 · 分类列表推荐AI, as it happens
1528 published storiesModel releases & updates · 199 storiesClear filters ×
2026-10-29
Follow the timeline2026-10-28
Follow the timeline19:04
The Sequence Learning Loop - Issue #960: Last Week in AI Infrastructure
DeepSeek, Google, and NVIDIA build the agent stack
16:54
Foreign blogger uses AI earphones as a medical translation tool: “Just listen and translate—communication becomes much smoother.”
Foreign blogger “Carlo Wu (Carlo)” shared his experience of visiting a traditional Chinese medicine doctor on social media. He wore the Qwen AI clip-on earphones—one for himself and one for the doctor—and used face-to-face translation to facilitate communication. Carlo said he finds traditional medicine very interesting, but language has always been an obstacle. During the consultation, when the doctor asked where he was feeling discomfort, he could only gesture while saying, “Everything hurts.” After putting on the earphones, the two were able to communicate more effectively. He remarked that this was the first time he truly understood what the doctor was saying.
16:18
Polymathic AI and Princeton Researchers Release FlashMap, a Flow Map Drafter for 6.14x Faster LLM Decoding
FlashMap is a speculative decoding drafter from Polymathic AI, Princeton and USC ISI researchers. It drafts 16 tokens jointly in one pass and reaches up to 6.14x lossless speedup on Qwen3-8B. The post Polymathic AI and Princeton Researchers Release FlashMap, a Flow Map Drafter for 6.14x Faster LLM Decoding appeared first on MarkTechPost.
15:34
Meet Google DeepMind’s Genie 4: Real-Time World Model With Native Audio
Google DeepMind released Genie 4 as a research preview. The real-time world model adds native audio, live dialogue and text steering, with Gemini 3.8 Flash planning events inside the simulation loop. The post Meet Google DeepMind’s Genie 4: Real-Time World Model With Native Audio appeared first on MarkTechPost.
15:08
Is the next “big” wave coming? DeepSeek V5 has appeared in the official code repository, with the new-generation flagship model expected to debut in November.
On October 28, users noticed that DeepSeek had once again changed its large-model fingerprint, sparking speculation about the imminent release of a major new model. The official DeepSeek Harness codebase now includes code for DeepSeek-V5, V5-open, and V5-next. Currently, the V5-related content mainly consists of placeholders, but it indicates that the mechanism for releasing the new model is already in place.
12:00
Detect Before You Disambiguate: Perturbation-Based Input Ambiguity Detection in LLMs
arXiv:2610.36353v1 Announce Type: new Abstract: Large language models (LLMs) often answer ambiguous inputs as if they had a single intended meaning. This is problematic because input ambiguity is an upstream source of uncertainty: when a prompt admits multiple valid interpretations, the key question is whether the model should answer directly at all. Existing input uncertainty methods often rely on LLM-generated statements or clarifications, but such transformations may already disambiguate the input before the ambiguity is measured. We propose a label-preserving perturbation framework for input ambiguity detection in which perturbations remain close to the original input while preserving its label. We instantiate this principle with two detectors: a black-box rule-based perturbation libra
12:00
LLM Hallucination Detection via Low-rank Multilinear Decomposition
arXiv:2610.36191v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate fluent yet factually incorrect responses, raising concerns about their reliability. Recent hallucination detectors increasingly exploit internal representations, but extracting informative signals from high-dimensional hidden states remains challenging. Existing approaches often rely on selected activations or pooling-based compression, potentially overlooking discriminative information distributed across Transformer layers, feature modes, and tokens. We propose LOTUS (Low-rank Order-statistic TUcker-based Subspace learning), a multilinear low-rank decomposition framework for hallucination detection that extracts compact representations from LLM hidden states while reducing dependence on labele
12:00
Causal Localization of Evidence Effects in Transformers
arXiv:2610.36524v1 Announce Type: new Abstract: Can controlled symbolic inputs reveal attention heads that influence a language model's decisions from natural language evidence? We introduce controlled evidence aggregation (CEA), which varies the counts and order of two symbols in otherwise fixed prompts and measures changes in a frozen Transformer's preference between two answer tokens. The preference is measured by their logit difference; the prompts prescribe no correct answer. Balanced utility causal selection (BUC) identifies compact head sets by jointly evaluating how much ablating their outputs removes the count effect and how closely patching mean outputs between count conditions recovers the effect in both directions. We derive conditional bounds linking head attribution to interv
12:00
A Quantitative Approach to Asch Conformity in Frontier Large Language Models
arXiv:2610.36668v1 Announce Type: new Abstract: The Asch conformity experiment has recently gained considerable attention as a way of measuring conformity in large language models (LLMs). While previous studies have focused on demonstrating conformity in LLMs, our work adds a further quantitative layer to this approach. Instead of sampling a frontier LLM on a suite of questions, our approach samples a single question repeatedly, leveraging the probabilistic nature of LLMs to produce a distribution of answers. In doing so, we can compare the distributions of answers before and after the model is presented with the manufactured consensus. Our experiments confirm that frontier LLMs do conform, reinforcing prior literature, and, through our distribution-to-distribution comparisons, provide add
02:53
How Anthropic ships Claude with Claude: Separating the Brain from the Hands
Katelyn Lesse and Angela Jiang on Opus 5.5, the Claude Platform, and the engineering behind AI agents.
02:40
How Anthropic ships the #1 LLM in the World safely: Separating the Brain from the Hands
Katelyn Lesse and Angela Jiang join Latent Space to explain the engineering behind the Claude Platform: launching Opus 5.5, separating the model’s “brain” from the agent’s “hands,” scaling APIs, and building reliable managed agents. A new introduction ...
2026-10-27
Follow the timeline21:52
Regarding the Kimi quota issue, Anthropic has found a solution.
After the release of K3.1 Pro, due to numerous users reporting that their quotas were being depleted too quickly, Moonshot AI urgently issued an announcement stating, “The issue lies with us, and we should have fixed it sooner.” Alongside compensating affected users, Moonshot AI also indicated that it would “reduce unnecessary sub-agent calls and output lengths.” Just two days before the model went live, Anthropic published an article titled “Building agents: When to fold subagents into your main agent loop,” which addressed precisely the topic of sub-agents. Anthropic concluded that, by comparing Claude deployments across multiple companies, they found that a single agent equipped with specific skills actually outperformed “a group of...”
19:21
Anthropic files its prospectus, with违规 users contributing at least 5% of its revenue; the Hurun Rich List’s top 100 includes AI heavyweights such as Liang Wenfeng | What’s happening in the AI world again?
1. GPT-6.1 Terra, better suited for workers, has launched, with API prices cut in half and faster computer operations On October 26, OpenAI released GPT-6.1 Terra, focusing on enhancing visual comprehension, computer operations, factual accuracy, and programming capabilities, ....
17:19
Most computing power, slowest progress: ByteDance’s large model stuck in a race-to-arms dynamic.
The old “horse racing + short-cycle evaluation” approach can no longer be sustained.
17:01
From Large Language Models to World Models
Yan Deli, a senior expert at Tencent: LLMs are probabilistic models. They do not truly understand language; rather, they predict the next token (Next Token Prediction). Essentially, they are monotonous “word-chain” machines—moving forward one step at a time without retracing their path. At their core, LLMs are probabilistic models that learn, from statistical co-occurrence patterns, “what often appears alongside what,” and output “this is usually the case,” without grasping “why it is so.”...
16:21
In the era of large models, who will become that “iPhone” no one is willing to replace?
The champion on the AI rankings may change, but the products users rely on every day don’t necessarily. The next round of competition in large models will hinge not just on whose model is stronger, but on turning a single use into a habit.
13:00
Just now, GPT-6.1 Terra was released at a discounted price, and the salaried worker model of the Agent era has arrived.
OpenAI has once again raised the bar for the cost-effectiveness of large models. #Follow iFanr’s official WeChat account: iFanr (WeChat ID: ifanr) for more exciting content delivered to you第一时间.
12:00
SLICE-Mem: Salience-Gated Lightweight Information Curation for Efficient LLM Agent Memory
arXiv:2610.33015v1 Announce Type: new Abstract: Long-term memory enables large language model (LLM) agents to maintain personalized and temporally coherent interactions, but repeated LLM inference for memory admission, extraction, and organization introduces substantial cost and latency. We present SLICE-Mem, a lightweight memory framework that combines salience-guided admission and predicate-centered information extraction. A compact classifier identifies user turns worth retaining, and a semantic-dependency-based extractor converts admitted turns into concise memory entries. This online pipeline requires no generative LLM calls, while cross-entry reconciliation is deferred to an offline consolidation stage. We evaluate SLICE-Mem on the weekly subset of Memora and LongMemEval-S using 8B a
12:00
Async-RBench: Benchmarking Asynchronous Task Execution in LLM Agents
arXiv:2610.32429v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly execute tasks asynchronously: a main agent continues working while its subagents run, and the task state may change before their results arrive. The main agent must decide which work to update or preserve and which checks to repeat, while avoiding prohibited changes. Final task success alone does not reveal whether these results were handled correctly, and natural asynchronous arrivals are hard to reproduce. We introduce Async-RBench, a benchmark for asynchronous task execution in LLM agents, with 200 executable tasks covering eight scenarios. A scenario controller regulates result delivery using predefined task-state conditions, while an Event Response Contract (ERC) specifies what each response
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