Search headlines, summaries and articles to connect the dots.
NVIDIA released Auto Ontology, an Apache 2.0 library that generates a semantic layer from enterprise metadata. It grounds agent SQL in certified definitions and serves context through MCP, CLI and API. The post NVIDIA Releases Auto Ontology: An Open Semantic Layer That Grounds AI Agent SQL appeared first on MarkTechPost.
The artificial intelligence buildout is making an enormous bet on what businesses will eventually be willing to pay for intelligence. In our research this summer on forecasting the AI bubble, we argued that a key indicator to watch is productive utilization by enterprise customers. Specifically, we said at the time, if infrastructure supply catches up […] The post Can enterprise AI pay for the boom? appeared first on SiliconANGLE.
Amazon Bedrock AgentCore Session endpoints give each runtime session an authenticated URL that forwards any HTTP method, path, and WebSocket connection to the ports you allow. In this post, you deploy a coding agent that keeps a Vite website running in its session, then watch the live site update in your browser as the agent edits files.
Teams building AI applications need a repeatable path from a model configuration to a working inference endpoint. NVIDIA Dynamo is a distributed inference...
AI is expanding beyond the data center as developers build a new generation of agents to act at the edge: monitoring driver attention and cabin conditions,...
As potentially the largest IPO in history, Anthropic PBC is breaking new ground. Its prospectus lists 88 risk factors covering 93 pages alone. From artificial intelligence posing an existential threat to humanity to the possibility AI will blackmail people, the company lists a litany of risks that should make any potential Anthropic investor wary. One […] The post The hidden threat in Anthropic’s prospectus: diseconomies of scale appeared first on SiliconANGLE.
## [1.123.91](https://github.com/n8n-io/n8n/compare/n8n@1.123.90...n8n@1.123.91) (2026-10-30) ### Bug Fixes * **core:** Fix $('Node') expressions returning null for legacy Code node output ([#42249](https://github.com/n8n-io/n8n/issues/42249)) ([18c89cd](https://github.com/n8n-io/n8n/commit/18c89cdf2eda635fbb892730189a2de1a48b46da))
Mistral’s open-weight release, frontier monitoring, and the day’s AI Twitter and Reddit recaps.
arXiv:2610.37881v1 Announce Type: new Abstract: Deepfake detection models achieve high overall accuracy but exhibit large performance disparities across demographic groups, causing some groups to experience substantially higher false-positive rates than others. Existing fairness-aware methods address this by constraining model outputs to equalize group predictions, but do not tackle the underlying feature-level bias. As a result, models may still rely on demographic characteristics, such as skin texture, as indirect cues for detecting manipulated content. To address this problem, we propose Intersectional Invariant Learning (IIL), a framework that improves feature learning rather than only adjusting model outputs. IIL alternates between identifying the most challenging demographic partitio
arXiv:2610.37680v1 Announce Type: new Abstract: Closing the gap between frontier and smaller language models is usually approached by distillation: fine-tuning the small model (student) on a frontier model's (teacher) outputs, which binds transfer to training compute and leaves the transferred knowledge largely opaque. We propose Artifact Distillation: a teacher solves training instances and externalizes its problem-solving procedure into an artifact bank that a student queries at inference time, with no gradient updates. Across four LiveBench tasks, a bank written by Claude Opus 4.5 lets Claude Haiku 4.5 outperform unaided Claude Sonnet 4.5 on spatial reasoning (0.707 vs. 0.647) at 60% of its cost, and reach Sonnet 4.5's level on table joining at 89% of its cost. The same raw bank raises
nemotron-halloween
Turn a PHP-to-Java migration from a one-time leap of faith into a measurable, repeatable process with Kiro. This post walks through a reusable engine: a mechanical discovery scanner and an eight-phase, parity-first workflow that treats the original PHP application as the specification and verifies the Java port with a parity harness.
## New Models ### Mistral Large 4 (Le Chonk) Mistral Large 4 is Mistral's largest and most capable model to date: a 1 trillion-parameter natively multimodal Mixture-of-Experts model with 49 billion active parameters per token (52 billion including embeddings and output layers), trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's own datacenters in Europe. Mistral Large 4 unifies instruction following, reasoning, coding, and agentic workflows in a single set of weights. While a generalist model, it shows especially good performance on workloads such as cybersecurity, finance, and law, and it surpasses frontier closed models on visual grounding. It is natively fluent in 160+ languages, including every official language of the European Union. #### Key Features Mistral Large
The weights of Mistral Large 4 (Le Chonk) are now open. A 1T parameter multimodal model, and best open-weight model for coding, trained in Europe.
Infini-AI-Lab released Hermit, an Apache-2.0 harness adapter framework that trains an external layer of instructions, skills, memory and tools from task feedback, leaving the agent’s model and harness unchanged. The post Infini-AI-Lab Releases Hermit, an Open-Source Harness Adapter Framework for AI Agents appeared first on MarkTechPost.
sycl : vectorize reordered-layout dequantization stores (Q4_0, K-quants, IQ3, MXFP4) (#30976) * sycl : widen reordered q4_0 dequantization stores The reordered-layout Q4_0 conversion kernel processed one qs byte per work-item with two scalar stores, strided 16 elements apart. Give it the same treatment the Q8_0 kernel got in #30775: a flat sycl::range<1> launch with four qs bytes (8 outputs) per work-item inside one 32-element block and two vec stores. Same float math and rounding as the scalar kernel; output is bit-identical. The existing k % QK4_0 == 0 assert guarantees whole 32-element blocks, so the k/8 launch covers every row exactly. The now-unreferenced scalar reorder kernel is removed. Arc Pro B70, primed prefill (prefill after decode on a reorder-installed tensor): +6.4% to +7.3%
Google’s new frontier model leads independent evals, with access initially limited to trusted defenders.
Nvidia Corp. today released a leaderboard-winning open artificial intelligence model for multilingual audio transcription and translation, along with an open data pipeline for fine-tuning Nemotron 3.5 Transcribe. Traditional transcription AI models follow a workflow that detects speech, transcribes it, identifies and labels different speakers, then answers questions about the transcription. Transcribe does all of this […] The post Nvidia releases all-in-one adaptable Nemotron 3.5 audio transcription and translation model appeared first on SiliconANGLE.
Since coding is solved, why code for the job?
Human beings struggle with emergency communication. Chatbots can help—if people take them seriously.
