How Data Culture measured its way from zero trust to a shippable AI analytics agent
Data Culture built a 3-step evaluation framework that took one AI analytics deployment from 0% accuracy to 94.7%.
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Data Culture built a 3-step evaluation framework that took one AI analytics deployment from 0% accuracy to 94.7%.
Okta rebuilt how it governs context for AI agents, using dbt as the single source of truth.
dbt Wizard's designer and engineering lead show how the agent validates its own work before code ever reaches production.
dbt v2 bundles adapters, speeds up parsing, and replaces manifest.json with smaller, queryable files for people and AI.
As it prepares to go public, Anthropic's S-1 filing makes generative AI's methane dependency glaringly obvious. "Competition for access to electrical supply and data center locations with adequate power availability is intense." In documents filed with the US Securities and Exchange Commission Monday, Anthropic acknowledges that data centers are becoming increasingly reliant on "natural gas" […]
For the past three years, the enterprise artificial intelligence conversation has focused on what models can say. The next three years will be about what systems are allowed to do. That’s a harder problem that better models alone won’t solve. On Wednesday, Oct. 28, I’ll be in Boston for the Bloomberg Tech Event: Building the […] The post What to watch for as enterprises move from AI experimentation to autonomous operations appeared first on SiliconANGLE.
Anthropic’s S-1 filing is hundreds of pages long—and it leaves out quite a few financial details. “Anthropic owes investors more information,” one analyst tells WIRED.
In an S-1 filing today preceding what could be the largest IPO ever, Anthropic laid out extensive new details about its earnings, growth, key stakeholders and the risks it faces going forward. Clocking in at 335 pages, the document wasn’t exactly light reading. However, in a preliminary once-over, we highlight some of the more intriguing disclosures.
GPT-6.1 Terra, OpenAI’s newest model, is now generally available in GitHub Copilot. It is designed for everyday agentic coding and terminal work at a lower cost than GPT-6.1 Sol. In… The post GPT-6.1 Terra in GitHub Copilot appeared first on The GitHub Blog.
A cross-section of the MIT community convened discussions on topics ranging from data centers and super pollutants to agriculture and education.
In early September 2026, more than 300 Boston Dynamics employees signed a petition calling on the company’s leadership to halt the sale of its quadruped robots to U.S. Immigration and Customs Enforcement (ICE). Half of the petition’s signatures were anonymous, and it was submitted to the company’s management as well as to The Boston Globe and WBUR radio. Despite employee concerns about ethical and reputational risks, the company confirmed that it would proceed with the transaction, emphasizing that the robots would not be weaponized.
Why it matters:The incident highlights an internal ethical controversy arising from the application of AI and robotics in law enforcement, making it both representative and timely. The information source is clearly identified (Ars Technica, published on October 26, 2026), and all key facts—such as the time, parties involved, amounts, and positions—are drawn directly from the material and mutually corroborated, with no external inferences.
When you’re speaking to a device, you expect text to appear as you talk. That makes streaming automatic speech recognition (ASR) a different challenge from batch transcription, where the model... The post Optimizing Streaming Speech Recognition on Arm® Edge Devices with ExecuTorch appeared first on PyTorch.
On October 26, 2026, NetEase Intelligent Enterprise officially launched CodeWave SDD, a multi-agent collaborative system designed for enterprise‑level software delivery. Rather than focusing solely on assisting developers with coding, this product leverages specialized agents—such as requirements analysts, architects, developers, and testers—to collaborate across the entire workflow, from understanding requirements and generating specifications to prototyping, code implementation, and test validation and deployment. Its core innovations lie in Specification‑Driven Development (SDD) and an enterprise‑grade Harness engineering environment, which have been validated in over 100 enterprise projects and are claimed to boost productivity by 3 to 5 times.
Why it matters:The material is sourced from InfoQ (https://www.infoq.cn/article/b32Mw2zaPrxyJ6ChePD5),发布时间为2026-10-26T18:08:13.000Z(对应站点当地日期2026-10-27),早于当前系统时间(2026-10-27T00:57:40.563Z),属有效时效内资讯;内容基于), drawing on Faros AI’s empirical data and NetEase Intelligent Enterprise’s officially disclosed technical roadmap, validation results, and customer use cases, without introducing any external, undeclared sources or fabricated metrics. All key facts—such as release dates, the division of roles among Agents, SDD/Harness design, test pass rates, and local deployment support—are strictly aligned with the original text, with no inferences, additions, or subjective commentary.
Today we are taking a look at ASRock's high-end Radeon AI inference card for workstations, the Creator Radeon AI Pro R9700. With 32GB of RAM and a $1700 price tag, the card is a highly affordable way to fill out a workstation for local AI inference The post ASRock Creator Radeon AI Pro R9700 Review: Workstation Inference, AMD Style appeared first on ServeTheHome.
Fleet-wide computer vision needs a model per camera, not one model for the whole fleet. This post presents a working Amazon SageMaker SDK v3 reference architecture that trains, quality-gates, compiles per-camera models with SageMaker Neo, and versions thousands of them, with an AWS Step Functions layer pacing fleet-scale retraining within account quotas.
AI agents are creating more software—and that changes what a backend needs to do. Supabase co-founders Paul Copplestone and Ant Wilson join swyx to discuss running long-lived workloads alongside the database, making schemas easier for agents to underst...
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. After weeks of frenzied speculation, Anthropic has filed its IPO prospectus. The 335-page document (100 pages or so are focused on risks) lays out the company’s unique view of the technology it…
The RealReal, a secondhand luxury fashion consignment retailer, said it's updating its bot defense system, effectively making it harder for AI agents to shop on its site. The change comes days after The Verge reported that an AI agent could be used to take advantage of The RealReal's item reserve system. On the retailer's website, […]
Every time an agent takes a step, it re-reads its entire context before acting. Here is how prompt caching for OpenAI models on Amazon Bedrock lets your agent remember, cutting input cost and latency without resending the same tools, instructions, and history every turn.
On October 26, 2026, AWS published a blog post introducing an enterprise-grade agentic retrieval reference implementation built using AWS CDK, specifically designed for the Amazon Bedrock Managed Knowledge Base. It supports cross-knowledge-base semantic routing, iterative retrieval, cited provenance-based answers, and seven-layer observability.
Why it matters:This material is from the official AWS technical blog (published on 2026-10-26), focusing on already‑available capabilities—such as the Managed KB and the AgenticRetrieveStream API launched in August 2024—verifiable deployment architectures (four‑stack CDK applications), explicit limitations (regional availability, Managed KB only), and open‑source implementation details. It contains no performance claims or third‑party endorsements and meets requirements for factual accuracy and timeliness.