New Mauritius budget confirms AI core to economic strategy
Government shares plans to train 50,000 people in AI skills
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Europe’s heatwave is the hottest and most humid ever
The current temperatures in western and central Europe would have been virtually impossible 50 years ago, and unprecedented humidity levels make this heatwave especially dangerous
China’s Zhipu AI sparks new ‘DeepSeek moment’ with cost-effective coding model
Nearly a year and a half after China’s DeepSeek shook Silicon Valley with its powerful yet affordable artificial intelligence model, Beijing-based Zhipu AI has delivered another jolt to the US tech industry. American entrepreneurs and researchers are praising the coding performance and cost-effectiveness of Zhipu’s new flagship model, GLM-5.2. Released earlier this month, the model’s release is being hailed by some as a new “DeepSeek moment”, with users calling it the first-ever open-weight...
AI-powered vibe coding is gaining ground in Nepal
Tasks that once took weeks can now be done in hours, but experts warn that speed may come with security risks.
Inside India’s 90-day countdown for CBSE schools to embrace AI
The post Inside India’s 90-day countdown for CBSE schools to embrace AI appeared first on The Ken .
UN approved first global technical regulation on automated driving systems, co-led by China
The new rule sets core technical requirements for automated driving system products and outlines manufacturer obligations in areas such as safety management processes, product safety documentation, end‑to‑end testing and validation frameworks, and post‑deployment vehicle safety.
Presight accelerator draws global AI startups
Listen now | Middle East AI News Minute - 26-Jun-26
Australian musicians sound warning note after Nick Cave, Kylie and many more slurped into AI training tool
‘It’s all just rendered useless’, Something For Kate’s Paul Dempsey says as AI scrapes millions of songs to learn how to make music Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast Paul Dempsey and Bernard Fanning are among big-name Australian musicians upset that their original songs have been found in datasets used to train artificial intelligence. A dataset search tool recently created by US publication The Atlantic reveals millions of creative works have been scraped from the internet to train the disruptive technology. Continue reading...
On owning a codebase, and why it may be the hardest job in software
AI coding agents are producing more code than ever, but the world still runs on massive, decades-old codebases. Why owning and understanding them may be the hardest job in software.
On owning a codebase, and why it may be the hardest job in software
AI coding agents are producing more code than ever, but the world still runs on massive, decades-old codebases. Why owning and understanding them may be the hardest job in software.
Semantic Memory for Hermes Agent with LanceDB
Introducing a new LanceDB-backed memory plugin that gives Hermes Agent durable, semantic recall across sessions, with benchmarks and a hands-on remember/recall/forget walkthrough.
Recent U.S. court cases breathe new life into Mexico narcopolitics reporting
Mexican journalists who investigate alleged ties between politicians and drug cartels explain how they verify leaked information, avoid being manipulated and safely cover organized crime networks. The post Recent U.S. court cases breathe new life into Mexico narcopolitics reporting appeared first on LatAm Journalism Review by the Knight Center .
AI導入の最前線——3人のCIOが語る戦略的優先事項
保険ブローカーTrucordiaのCIO、Rajeev Khanna氏の戦略的優先事項は、多くのCIOと同様、組織全体へのAI導入が最上位にある。サイバーセキュリティ、データ・分析プロジェクト、イノベーションも並行して進めている。どれも特別なものではないが、Khanna氏は汎用的なテンプレートや曖昧な目標では進められないと理解している。自動化とAIでワークフローを効率化し、テクノロジーで顧客の特定のニーズに応え、新しい製品・サービスで市場での差別化と成長を実現する。 「テクノロジーがビジネスのイノベーションと提供スピードを実現している」とKhanna氏は言う。 CIO.comのState of the CIO調査によれば、CIOが挙げる最も戦略的に重要な技術施策は、「生成AI」が最上位、続いて「エージェンティックAI」「データ・ビジネス分析」「セキュリティ・リスク管理」「IT・ビジネスプロセスの自動化」が上位5つを占めている。「モダナイゼーション」「クラウド管理」「クラウドへのアプリケーション開発・移行」といった従来型のITタスクは、それより下位に位置する。 CIOやアドバイザー、アナリストは、この施策リストがテック幹部の関心の変化を映していると指摘する。技術的な卓越性そのものを主目的とするのではなく、組織の戦略とビジネス成果を形作り、実現することにより多くのエネルギーを注いでいるのだ。「CIOは、AI導入とビジネス価値をエンタープライズ規模で推進するために必要なITアーキテクチャ、組織構造、プロセス変革の先頭に立っている」と調査は指摘する。 こうした変化は、CIOの役割が「業務の指示を受けて動く存在」から「変革のリーダー」へと進化していることを示している。CIOは事業部門のリーダーと積極的に連携し、AI導入を推進し、すべての技術施策から高い価値の成果を引き出すことに注力している。実際、2026年のCIOは事業リーダーとの協働、新興技術の学習、AI施策を支える組織構造の構築に多くの時間を割くようになった。その反面で、ベンダーとの交渉、ITの危機対応、コスト管理に費やす時間は前年より減っている。 テクノロジーが新しいケイパビリティと製品を生む Khanna氏は「組織にとって差別化につながる新しいケイパビリティを生み出し、クライアント向けの新製品をより効率的かつ速いペースで立ち上げること」を優先している。そこでは、多くの場合AIを使っているという。データと分析を活用するプロジェクトも優先しており、たとえば分析ツールにLLMを組み込み、ユーザーが自然言語でデータに問いかけられるようにしている。 そこで「常に最優先」とKhanna氏がいうのが「サイバーセキュリティ」だは言う。「サイバーは常に動く標的だ。最新の状態を保ち、現代化し、攻撃者の手口の先を行く必要がある。それは永遠に続く取り組みだ」。 AIのスケーリングが目標になる MetLifeのグローバルCIO、Nick Nadgauda氏も同様にITの戦略的施策をビジネスの推進力として語る。「最も戦略的に重要な施策は、散発的な実験にとどまっていたAIを、企業の運営に組み込まれた中核的で信頼できるケイパビリティへとスケールさせることだ。MetLifeではAIを単独の技術プロジェクトではなく、戦略の重要な実現手段として捉えている。意思決定を磨き、業務を簡素化し、最終的に顧客とビジネスにより良い成果をもたらす」。 MetLifeは社内向けのAI統合プラットフォーム「MetIQ」を展開し、「セキュアでガバナンス…
Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks
Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency, while maintaining flexibility to choose among more than 20 models. The post Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks appeared first on The GitHub Blog .
France and UK swelter in record-breaking heat
The UK saw its two hottest June days, and France its hottest day ever.
AI and Liability
AI and Liability Bruce Schneier and Nathan Sanders on the recent German ruling that Google be held liable for errors introduced in their AI overviews: AI agents are agents of the person or organization that deploys them—and should be treated by the law as such. If a company hired human writers to write its summaries, that company would be liable for inaccuracies in those summaries. [...] To allow businesses to hide behind the excuse of faulty AI in those same circumstances would be a massive handout to companies, and would introduce disastrous incentives for corporate misbehavior. Why hire human writers, lawyers or doctors when AIs are not only cheaper, but also absolve employers whenever they make a mistake? Tags: bruce-schneier , google , law , ai , generative-ai , llms , ai-ethics , hallucinations
HKEX pushes deeper into index business as AI reshapes Hong Kong market
Hong Kong Exchanges and Clearing (HKEX) is expanding into the index business with plans to launch more proprietary benchmarks and related investment products, as traditional market gauges have lagged regional peers during the artificial intelligence-driven technology rally. The operator of Hong Kong’s stock exchange will debut the first exchange-traded fund (ETF) tracking its HKEX Tech 100 Index on Friday. The index, launched on December 9, tracks the 100 largest technology companies listed in...
Introducing the Cluster API plugin for Headlamp
Headlamp is an open-source, extensible Kubernetes SIG UI project designed to let you explore, manage, and debug cluster resources directly from a browser. Cluster API (CAPI) is a Kubernetes sub-project that brings declarative, Kubernetes-style APIs to cluster lifecycle management. It lets platform teams provision, upgrade, and manage the lifecycle of Kubernetes clusters using standard Kubernetes objects stored and reconciled in a management cluster. Managing Cluster API resources has historically required raw kubectl commands and deep familiarity with ownership hierarchies. The Headlamp Cluster API plugin brings visual clarity, faster debugging, and simplified operations for platform teams, directly inside Headlamp. What this plugin provides The Cluster API plugin adds a dedicated Cluster API section to Headlamp and brings full visibility into core CAPI resources through consistent list and detail views. Feature Description Cluster overview View clusters with live control plane and worker replica status. Machine visibility Inspect MachineDeployments, MachineSets, Machines, and MachinePools with status and conditions. Cluster API dashboard Get a centralized view of Cluster API resource health, active condition issues, provider information, and remediation guidance. Control plane monitoring Track KubeadmControlPlane replicas, versions, and associated Machines. Scale from the UI Scale MachineDeployments and MachineSets directly from Headlamp. Owned resource hierarchy Trace rela…
OpenAI will delay GPT-5.6 after Trump administration request
The Trump administration, apprehensive of potential security issues, has reportedly asked OpenAI to stagger the release of its next big-ticket model, GPT-5.6. The Information reported that OpenAI CEO Sam Altman told employees Wednesday in a company Q&A that it would release GPT-5.6 in limited preview form - granting access only to a small group of […]
Why Hong Kong’s bilingualism is uniquely indispensable in the AI era
Last week, while preparing a lecture on the visual culture of the Global South, I caught Google’s Gemini in a double hallucination. Cross-referencing a historical event between English and Chinese data sets, I found the English AI to be authoritative but inventing citations. In Chinese, the fabrications vanished but so did global context, replaced by an insular perspective. Disturbingly, the system cloaked Chinese content in English citations, creating a deceptive authenticity that made the...
Copilot in Excel: Built for the era of Frontier Finance
The post Copilot in Excel: Built for the era of Frontier Finance appeared first on Source .
Apple just raised prices: See which devices cost more now
Table of contents Which Apple products got more expensive? Full Apple price increase breakdown Which Apple products are still at the old price Why Apple raised its prices Is the iPhone affected too? What about prices outside the US? Should you buy now? How long will prices stay high? Sub Heading 2 Apple raised prices […]
US hearing warns Chinese economic espionage now targets AI
The United States has been asleep for decades as China undercut US economic strength by stealing ideas, technologies and, more recently, artificial intelligence advances, with the Chinese military first in line to benefit, according to testimony heard by a congressional committee on Thursday. The hearing by the House Select Committee on China, which focused on economic espionage and Chinese efforts to exert influence at state and local levels, was held amid mounting bilateral tension over export...
Inspect Volcano workloads faster with Headlamp
Volcano is a cloud native batch scheduler for Kubernetes, built for high-performance computing, AI/ML, and other batch workloads. Headlamp is an extensible Kubernetes web UI. With its plugin system, Headlamp can surface APIs and workflows beyond the built-in Kubernetes resources. The Volcano plugin brings core Volcano resources into Headlamp so you can inspect workload state, queue behavior, and gang scheduling details in one place. Kubernetes was originally designed around long-running services, where applications are expected to start and remain available over time. Batch, AI/ML, and HPC workloads often behave differently: jobs arrive dynamically, compete for limited resources, and may need multiple workers to start together before useful work can begin. Volcano extends Kubernetes with concepts such as queues, priorities, quotas, and gang scheduling. Instead of treating every Pod independently, Volcano schedules workloads with awareness of the job as a whole and the resources it needs to make progress. To make these workloads easier to operate and troubleshoot, the Volcano plugin brings that scheduling context directly into Headlamp. Watch this short walkthrough to see the Volcano plugin in Headlamp: Visual context helps teams understand Volcano jobs, queues, and PodGroups faster Working with Volcano often means moving across several related resources while trying to understand a batch workload. You might start with a Job, then look at the related PodGroup, inspect the Pod…
AI Evaluation Simplified: Automate Dataset & Metric Eval Workflows with Test Suites
You shipped an agent. It worked in the demo. In production, a user phrased a question differently than you expected and the agent fell apart. AI evaluation is supposed to catch that issue before your users do, but the standard workflow asks you to build a reference dataset, hand-pick metrics, write LLM-as-a-judge prompts for each […] The post AI Evaluation Simplified: Automate Dataset & Metric Eval Workflows with Test Suites appeared first on Comet .
Inference Is the New Bottleneck: How to Plan GPU Capacity for Production AI
By Adam Wolf Most enterprises sized their AI infrastructure with a playbook written for training. However, training is no longer the typical workload. Inference now eats up roughly two-thirds of all AI compute, and it is changing shape fast enough that the rules of thumb from 18 months ago just do not hold. Our view […]
Today we’re bringing skills to Copilot for Excel, giving teams a new way to scale their expertise across every workbook.
The post Today we’re bringing skills to Copilot for Excel, giving teams a new way to scale their expertise across every workbook. appeared first on Source .
Advanced Claude Code Cost Tracking: How to Save 30% on Token Spend
With tools like Claude Code and Codex now standard in engineering workflows, developers are shipping new products, features, and bug fixes at mind-blowing speed. But as coding agent usage grows and API billing plans mature, another mind-blowing factor is coming into focus: the cost. Almost every day, our team talks to an engineer, team lead, […] The post Advanced Claude Code Cost Tracking: How to Save 30% on Token Spend appeared first on Comet .
Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory
I benchmarked raw chat history, vector-only RAG, and a context graph on the same multi-agent conversations. The results exposed a surprising weakness in relational retrieval. The post Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory appeared first on Towards Data Science .
Researchers cast new doubt on Microsoft’s quantum computing advance
Microsoft’s controversial claim that its Majorana chip program will make possible a scalable quantum computer by 2029 has been thrown into new doubt by a scientific paper that questions whether the company has correctly interpreted its own experimental evidence. According to a peer-reviewed paper by Dr. Henry Legg from the University of St Andrews, published this week in Nature , Microsoft’s Topological Gap Protocol (TGP) framework, designed to infer the existence of quantum states in theorized Majorana particles, is flawed. “Last year Microsoft claimed they had built the equivalent of a precision Swiss watch. However, when I opened the case to examine the mechanism, I found what looked like a chaotic jumble of mismatched parts,” said Legg . He believed the results gathered from Microsoft’s TGP software data analysis could also be explained by other effects, as well as being skewed by the data chosen for analysis. Because of this, he believed the company’s researchers had jumped to the wrong conclusions. “Something was making noise, but it didn’t look like the breakthrough Microsoft had claimed. Despite the headlines, the vast majority of scientists in the field were skeptical of Microsoft’s claim from the start; my critique simply backs up that skepticism in the scientific record,” he said. Topological qubits The ability to create Majorana ‘zero modes’ that resist the errors suffered by traditional qubit-based designs is fundamental to Microsoft’s entire quantum computing s…
Anthropic says Alibaba must be punished for largest Claude cloning attack
Alibaba allegedly used 25,000 accounts to mine Claude over 28.8 million exchanges.
See your serverless: introducing the Headlamp plugin for Knative
Headlamp is an open-source, extensible Kubernetes SIG UI project designed to let you explore, manage, and debug cluster resources. Knative brings serverless workloads to Kubernetes, handling traffic routing, autoscaling, and revision management so teams can deploy and iterate without fighting infrastructure. But operating Knative workloads day-to-day can be difficult, there's still a lot of jumping between the kn CLI, kubectl , and the Kubernetes UI to get a full picture of what's running. We built the Headlamp Knative plugin to bridge that very gap, allowing operators to inspect, understand and act on their workloads all from a single place. This plugin was built as part of the LFX mentorship. Here's a tour of what we shipped. Here is a short walkthrough of the Knative plugin for Headlamp: Integrating Knative resources with Headlamp's map view Headlamp's resource mapping works for Knative CRDs too. You can see how KServices, Revisions, and DomainMappings relate to each other in a single graph view. KService management: edit traffic splits, restart pods, and view logs A KService is the top-level resource in Knative: it manages the lifecycle of Routes, Configurations, Revisions, and everything needed to run and expose your application. The plugin gives KServices a full detail view with an Edit Mode toggle for making live changes to traffic splits, autoscaling annotations, and more. Common actions like viewing the YAML, opening logs, triggering a redeploy, or restarting backin…
The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark
A reproducible benchmark on latency, cost, and reproducibility, and where agents actually earn their keep. The post The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark appeared first on Towards Data Science .
Retrofit, don’t rebuild: Agentic overlays for transforming legacy enterprise services
In this technical collaboration between AWS and the authors, we present a pragmatic solution: agentic overlays. Agentic overlays are thin wrapper layers that transform traditional REST-based services into agents capable of participating in A2A interactions. They also expose REST APIs as tools compatible with the Model Context Protocol (MCP). Together, they let enterprises add A2A capabilities to existing REST services without rewriting business logic, without duplicating code, and without running parallel infrastructures. This reduces agent sprawl in the infrastructure by reusing existing services as agents. We provide reference architectures and sample code that show how to build agentic overlays.
US is ‘superhero’, China ‘supervillain’ in global AI contest, American officials warn
US House Foreign Affairs Committee Chairman Brian Mast warned that “America is the superhero” and China the “supervillain” in the contest for global artificial intelligence (AI) leadership on Thursday, just two days after US Treasury Secretary Scott Bessent said America’s “biggest risk” on AI is China getting ahead. The United States and China remain locked in an increasingly competitive race for worldwide AI supremacy, with many American officials concerned that China is eroding the US’ early...
Why Does a Bank Need a Chief Scientist?
This article is brought to you by Capital One . After five years leading natural language understanding and eventually the entire Alexa AI organization at Amazon, Prem Natarajan made a nontraditional move: He became Chief Scientist at a bank. Not just any bank: Capital One, a financial institution serving over 100 million customers, helping everyday Americans manage their financial lives. For Natarajan, a veteran of DARPA-funded research and academia who had watched machine learning evolve from task-specific applications to foundation models, the logic was clear. Some of the most interesting advances in AI research and deployment were shifting from big tech’s horizontal platforms to industry verticals like finance, where the most complex problems aren’t just building models but making AI work under the constraints of real-world customer problems, contextual business knowledge, continuous learning, with an incredibly high bar for accuracy and privacy. That’s also what made Capital One the right place to do it. For decades, the company has been recognized as one of the most data- and analytics-driven financial institutions in the industry. Its business model from the very beginning was built around using data and technology to personalize financial products for customers. A decade ago, Capital One went all in on the cloud and rebuilt its data ecosystem, creating a unified environment for data, compute, and AI and machine learning experimentation. Today, its modern infrastructu…
10 Years of MongoDB Atlas: Built for What’s Next
Nearly a decade ago, I joined MongoDB as a Senior Product Manager to help build the company’s new cloud product, MongoDB Atlas. Our customers had been telling us they wanted to bring MongoDB’s familiar developer experience to the cloud, with the reliability and confidence teams needed to run in production. Atlas was our answer. Today, we’re celebrating 10 years of MongoDB Atlas, the generational data platform for AI applications, and the customers who pushed us to build it. Atlas was shaped in close conversation with those customers and scaled alongside them every step of the way. Today, more than 250,000 builders get started on Atlas every month. Atlas serves more than three trillion queries a day (a roughly threefold increase just since 2023!), and represents 75% of MongoDB’s revenue. Those numbers reflect something more important than growth: the trust builders and customers have placed in us to scale their businesses. That trust was earned by listening closely. Every major capability and architectural investment in Atlas was rooted in what customers asked for: the flexibility and speed of MongoDB’s document model, delivered in a platform that removed operational overhead and could scale with their applications. Over time, Atlas expanded beyond a managed database into a broader data platform, because builders kept asking for more flexibility, more simplicity, and more room to build. That matters even more in the AI era. AI applications create new demands, but the underlyi…
Chanel Hong
Chanel Hong robyn.cherinka… Thu, 06/25/2026 - 12:26 Image Director, Head of People Chanel Hong Chanel Hong is Director, Head of People at Toyota Research Institute (TRI), where she leads talent acquisition, people strategy and operations, employee experience, diversity, equity and inclusion, and learning and development. She focuses on building an inclusive organization that enables impactful research and innovation. Her work includes strengthening TRI’s culture, aligning leadership, and developing systems that enable effective operations, engagement, and growth. Since joining TRI in 2016 as an early employee, Chanel has played a foundational role in shaping the institute’s evolution. As chief of staff to TRI CEO and TMC Chief Scientist Dr. Gill Pratt, she led company-wide planning, drove global initiatives across TRI and Toyota Motor Corporation, and established TRI’s stakeholder relations function to strengthen trust and alignment with key stakeholders. She brings more than 25 years of experience in executive advisory, administration, operations, and corporate communications in the technology sector. Chanel holds a bachelor of arts in art history from Mills College and a SHRM Senior Certified Professional (SHRM-SCP) designation.
datasette-export-database 0.3a2
Release: datasette-export-database 0.3a2 An embarrassingly tiny release. The pyproject.toml had pinned to datasette==1.0a27 , inadvertently making this plugin incompatible with all other Datasette versions. It's now datasette>=1.0a27 instead. Tags: datasette
What if plants could talk?
Now our plants won't shut up. Give your plants a voice: https://github.com/openai/planttalk
ATRC school STEM drive reaches 5,300 students
Students submit over 1,100 research projects in year-long STEM scheme
Can home batteries help save the climate and save you money?
Growing numbers of homeowners are installing batteries that store electricity when it is cheap, which helps balance the grid and cuts emissions, and cheaper plug-in batteries will soon let more people do the same