AI/ML news, top picks, and generated innovation digests.
35554 matching items
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 .
The UK saw its two hottest June days, and France its hottest day ever.
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
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...
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…
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 […]
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...
The post Copilot in Excel: Built for the era of Frontier Finance appeared first on Source .
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 […]
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...
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…
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 .
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 […]
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 .
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 .
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 .
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…
Alibaba allegedly used 25,000 accounts to mine Claude over 28.8 million exchanges.
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…
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 .
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 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...
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…
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 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.
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
Now our plants won't shut up. Give your plants a voice: https://github.com/openai/planttalk
Students submit over 1,100 research projects in year-long STEM scheme
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
This post shows you how to configure training jobs on Amazon SageMaker AI to get the most out of Blackwell’s architecture on AWS. You learn how to select batch sizes and sequence lengths that take advantage of Blackwell’s expanded memory, choose the right precision format for your model size (1B to 64B parameters), and apply activation checkpointing strategically. By the end, you have a practical framework for tuning your training configuration and launching distributed training jobs on P6-B200 instances.
In this post, we demonstrate how to implement video upscaling using SeedVR2 on SageMaker AI. We cover the solution architecture, walk through the deployment steps, and show performance comparisons that highlight the quality improvements and processing efficiency you can achieve. By the end of this post, you’ll have the practical knowledge needed to implement this super resolution solution.
In this post, we show you how to build Chaplin (Customer Health and Planned Lifecycle Intelligence Nexus), an open source solution that uses AI agents exposed through the Model Context Protocol (MCP) to provide self-service health event analytics.
This post shows how to build a governed, serverless data mesh on AWS that provides the secure, scalable data foundation production agentic AI requires.
Pocket FM's India Country Head Suyog Gothi has stepped down from the company after a two and a half year stint. He is the latest senior executive to leave the company over the past month, multiple sources told Entrackr . Gothi joined Pocket FM in December 2023. According to sources, Nitin Verma has taken over Gothi's responsibilities as part of an internal leadership reshuffle. "The transition has already taken place and Nitin is handling the role," said a person aware of the development. "The company is realigning teams and more senior level exits are expected over the next few weeks.” Confirming the development to Entrackr , a Pocket FM spokesperson said, “Suyog Gothi moved on from Pocket FM over seven months ago to pursue new opportunities. This was unrelated to Pocket TV.” However, Gothi’s LinkedIn profile reflects that he served as India Country Head until May 2026. Before joining Pocket FM, Gothi spent nearly seven years at PhonePe, where he led the company's UPI payments business before taking over as Head of Merchant Lending. Earlier in his career, he worked as a Business Analyst at Flipkart between June 2014 and July 2015. His next move could not be ascertained. Queries sent to Gothi did not elicit a response until the publication of this story. Responding to queries on more senior level exits, the spokesperson said, “As with any growing organization, leadership transitions happen from time to time for a range of personal and professional reasons. We are not aware o…
For a decade, confidential computing has been chipping away at one of security’s hardest problems: data is well encrypted in transit and at rest, but when a processor works on it, that data sits in memory in the clear, exposed to anyone with privileged host access. “Confidential computing’s aim was to solve this with a trusted execution environment, a subset of the CPU that runs the encrypted workload and handles things like memory encryption,” said Marina Moore , lead security researcher at Edera . For years the field felt like post-quantum cryptography PhD research scientist types agreeing the work is essential, while waiting for it to reach mainstream practitioners. At the Confidential Computing Summit in San Francisco this week, the breakout use case came into focus: agentic AI. Like the web before HTTPS “I was in the really early days of HTTP, and then HTTPS came along pretty quickly,” said Mike Bursell , executive director of the Confidential Computing Consortium . He sees agentic AI where the web sat before certificate authorities and public key infrastructure brokered trust online. “The original agent specifications were not written by security architects,” Bursell said, and “some of it feels in need of refinement.” The gap confidential computing fills is attestation, which provides proof of what runs. The hardware hashes the memory and firmware of a protected execution environment and signs the result inside the chip, Bursell explained, producing a measurement a ver…
Whether you should stick to a classic Ordinary Least Squares regression, introduce interaction terms, or pivot to a Tweedie distribution depends entirely on how your data handles the messy reality of zeros and extreme outliers. The post Beyond the Straight Line: Choosing Between OLS, Interaction Terms, and Tweedie Regression appeared first on Towards Data Science .
98% of UAE investors now use AI tools, but most still want human advice on big calls
Smartworks Coworking Spaces has announced the acquisition of Singapore based flexible workspace provider Workstudio Spaces through its wholly owned subsidiary, Smartworks Space Pte. Ltd. The transaction is expected to close in July. Workstudio operates around 26,000 square feet of managed workspace in Singapore. Following the acquisition, Smartworks' footprint in the city state is expected to increase to about 76,000 square feet across four centres, with seating capacity of more than 1,500. According to the company, the acquisition will be funded through resources available with its Singapore subsidiary. Smartworks founder and managing director Neetish Sarda said Singapore remains a strategic market for the company. He added that the acquisition will expand its presence in a high demand micro market and broaden its enterprise customer base. As of March 31, 2026, Smartworks managed around 16.1 million square feet of workspace across 66 centres in 15 cities in India and Singapore. The company primarily serves enterprise clients, including multinational corporations, global capability centres, and large Indian businesses. Smartworks' revenue from operations rose 45% year on year to Rs 520 crore in Q4 FY26 from Rs 358 crore in the same quarter last year. The company reported a profit of Rs 16.6 crore in Q4 FY26 against a loss of Rs 8.3 crore in Q4 FY25, as revenue growth outpaced the increase in expenses.
Let’s walk through the NVIDIA cuFOLIO Developer Example. This open source, customizable notebook enables GPU accelerated portfolio optimization by constructing an optimal portfolio from the S&P 500 universe and then backtesting against customizable parameters and portfolios. ➡️ Start now: https://build.nvidia.com/nvidia/quantitative-portfolio-optimization 📥 Code: https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization/ 📝 Tech blog: https://developer.nvidia.com/blog/accelerate-large-linear-programming-problems-with-nvidia-cuopt 00:00 Interactive Backtesting Intro 00:11 Quantitative Portfolio Optimization 00:26 Deploy on Cloud (Brev) 00:57 Launchable Setup 01:50 Github 01:57 Run Notebook 02:42 2. CVaR Formulation 03:00 3. Data and Model Setup 04:26 4. Solve CVaR Optimization 07:15 5. Backtest Portfolio 08:09 6. GPU v CPU 09:40 7. Appendix 10:05 Outro #quantfinance #portfoliooptimization #algorithmictrading
We have identified the gene that, when activated, initiates the developmental programme that results in cells forming a human body
In this tutorial, we will show you how to use Gemini to create Google Sheets, build a useful table, generate formulas, analyze data, and improve the spreadsheet with follow-up prompts.
Researchers introduce generative causal testing, which translates black box models into clear hypotheses and verifies them in the scanner, revealing what specific brain regions respond to in language. The post Understanding the brain with AI-driven explanations and experiments appeared first on Microsoft Research .
The loss of Antarctica’s doomsday glacier would transform our planet. Now scientists are revealing the secrets of this remotest of places, and asking the question: is its demise inevitable?
I am estimating the effect of a continuous treatment on a continuous outcome using a two-way fixed effects (TWFE) model with individual and year fixed effects ( fixest::feols ) and cluster-robust standard errors. I want to estimate heterogeneous effects by sex, a binary, time-invariant grouping variable. With OLS, I get exactly the behaviour I would expect. m The female marginal effect returned by avg_comparisons() is exactly the same as the coefficient from the female-only regression (-0.00206), and likewise the male marginal effect is exactly the same as the male-only regression (-0.00009). However, the same is not true with fixed effects. I estimate m The fitted coefficients are treatment = 0.00279 treatment:sexFemale = -0.00932 and therefore marginaleffects::avg_comparisons( m, variables = "treatment", by = "sex" ) returns Female = -0.00652 Male = 0.00279 However, estimating separate fixed-effects models gives feols( outcome ~ treatment | id + year, data = subset(dat, sex == "Female"), cluster = ~neighbourhood ) feols( outcome ~ treatment | id + year, data = subset(dat, sex == "Male"), cluster = ~neighbourhood ) with estimates Female = -0.00379 Male = -0.00034 The subgroup samples are additive: Female observations + Male observations = pooled observations. Female individuals + Male individuals = pooled individuals. I also verified that this is not an issue with avg_comparisons() ; without fixed effects in fixest::feols() , the marginal effects and subgroup regressions co…
German company ARX Robotics and Ukraine's Roboneers today announced the formation of ARX Industries, a joint venture to industrialise the mass production of unmanned ground vehicles. The new entity wi...
Modern development has shifted one old IDE paradigm significantly: Now, not only is it possible that a project is not hosted on the same physical or remote machine as your IDE instance, it could even be that both share the same host but are separated from one another inside isolated environments. If you are a […]