In DAX, we reuse existing measures all the time when writing new measures. What happens when we write a measure based on another measure and try to change a filter already set in the nested measure?
The post How to Solve Issues When You Nest Measures While Overwriting the Same Filter appeared first on Towards Data Science.
At DevDay 2026, OpenAI gave Codex reusable cloud environments, automatic security scans for GitHub repositories, and a code review view in the desktop app. The Agents API now supports Computer Use, and a new Decisions API handles fast, single decisions. The Ultrafast premium tier promises up to eight times the speed at six times the price.
The article OpenAI expands Codex and its API at DevDay with security scans, a Decisions API, and Ultrafast appeared first on The Decoder.
Get more out of your coding agent subscriptions
The post How to Maximize Your Coding Agent Subscriptions appeared first on Towards Data Science.
A GPU cluster can pass every health check and still fail to run an AI workload. Even when every GPU, network link, and pod reports healthy, a 512-GPU training...A GPU cluster can pass every health check and still fail to run an AI workload. Even when every GPU, network link, and pod reports healthy, a 512-GPU training job can underperform or fail. The cause may be one slow GPU, a link that degrades under load, or a configuration that quietly routes traffic over a slower path. Operators may not discover the problem until hours into the run or until a…
Brace yourself: It turns out AI is being optimized for cheating. OpenAI’s agents hacked into Hugging Face to get the answers to a cybersecurity test. Next, they solved a prestigious math problem (or just stole from two top mathematicians’ answer sheets). Anthropic’s models have also hacked into other companies’ systems four times already. And that’s only what we’ve caught so far.
Freaking out? You’re not alone. AI lab researchers are quitting their jobs and issuing dire warnings that if we keep going this way, AI might eventually kill us all. Bill Gates is sounding the alarm. Bernie Sanders has teamed up with Steve Bannon, of all people, to call for curbs on AI. Anthropic CEO Dario Amodei is urging a slowdown, and other top US AI executives agree. But fear not: President Trump has a plan. He says the only guardrail AI needs is “a STRONG AND SMART (High IQ!) PRESIDENT.”
Kubernetes manages what runs on your nodes. Managing the nodes themselves is the challenge: kernel settings, system packages, storage layouts, security agents,...Kubernetes manages what runs on your nodes. Managing the nodes themselves is the challenge: kernel settings, system packages, storage layouts, security agents, and the host-level tuning that GPU workloads depend on. Many teams manage this with Ansible playbooks, custom scripts, and manual runbooks. That works until a new cluster comes up in a different region, a kernel upgrade breaks RDMA…
AI factories are power-limited systems that deliver maximum value when fully optimized. GPU workload placement is a key optimization. Poor workload placement...AI factories are power-limited systems that deliver maximum value when fully optimized. GPU workload placement is a key optimization. Poor workload placement fragments topology domains and forces traffic across shared links, reducing throughput, raising job costs, and leaving GPUs consuming provisioned power while waiting on data without advancing the workload. GPUs exchange data continuously…
Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize...Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize output within the factory’s limited power budget. This makes performance per watt—rather than raw, unnormalized throughput—the ultimate measure of an AI platform’s value. The NVIDIA Vera Rubin platform is designed to enable power…
Keep your apps looking professional with Claude Code design skills
The post How to Build Consistent Designs with Claude Code appeared first on Towards Data Science.
It wasn’t that long ago that ClickFix attacks were exotic. Now the technique has become mainstream as attackers reap its simplicity and effectiveness in infecting users of PCs and Macs alike. All that’s required is a compromised website—a painless enough task—a fake CAPTCHA overlay, and the inclusion of a single terminal command. So many visitors get suckered into pasting and running the command that just about every malware pusher has adopted the technique. Even Kremlin-backed hacking groups are joining in.
“Reddit is becoming post after post after post of people getting their computer infected via ClickFix,” independent researcher Kevin Beaumont observed Thursday. “Legit websites everywhere [are] getting hacked to serve the fake captcha prompts.”
More seasoned Internet users—a fair number who read this site—are quick to dismiss the attack. They typically blame the people who fall for the scams and marvel at their gullibility and lack of attention. The reality is that for more casual users, using computers and the Internet has become so difficult—think impossible-to-close interstitials, CAPTCHAs with an endless series of pictures to analyze, and constantly changing interfaces that bury the features they’re looking for—that they have grown desensitized to instructions that seem ridiculous and burdensome.
© Getty Images
Learn where which coding agent is best
The post When to Use Claude Code and When to Use Codex appeared first on Towards Data Science.
When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels,...When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels, and capturing NVIDIA CUDA graphs. For large models, initialization can take several minutes, during which surviving workers must absorb the displaced traffic. Shadow engine recovery, available as a preview feature in NVIDIA Dynamo…
Chinese retailer AliExpress has been caught fingerprinting visitors after one of the metrics—an outdated technique that measures inaudible sounds it sends to browsers—impeded a researcher's ability to use his bluetooth headphones.
Researcher Matthew Callaghan said he stumbled on the stealthy tracking by mistake. After loading the AliExpress homepage, audio from his phone stopped playing over his multipoint headphones, which accept connections from more than one device at a time. He set the headphones to play sounds from his phone except when his PC was producing audio. Each time he loaded AliExpress, the phone audio stopped. Each time he closed the tab the site was loaded into, the phone was once again audible.
While investigating the odd behavior, Callaghan said he found two highly obfuscated scripts. Together, they rendered a graph that analyzed the WebAudio readings of each visiting browser. This graph acted as an oscillator that measured Sawtooth waves, which are common in output from digital audio.
© Getty Images
The massive growth of generative AI has fundamentally altered data center design. As distributed model training scales to span hundreds of thousands of GPUs,...The massive growth of generative AI has fundamentally altered data center design. As distributed model training scales to span hundreds of thousands of GPUs, the scale-out network connecting these nodes has emerged as a first-order performance bottleneck. For decades, traditional off-the-shelf Ethernet has been the undisputed king of enterprise and cloud networking. It is cheap, standardized…
Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users,...Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users, agents, applications, data sources, and storage systems to massively accelerated compute at multi-terabit bandwidth per server, making dedicated DPU processing essential for line-rate networking, storage, and security.
NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform. At the core of the platform is NVIDIA Vera Rubin NVL72, the...NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform. At the core of the platform is NVIDIA Vera Rubin NVL72, the most versatile machine ever built, delivering high throughput and interactivity across the widest range of AI workloads—from small to large models, both open and closed. Groq 3 LPX, when paired with Vera Rubin NVL72, extends the platform’s…
Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications...Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications range across exploratory data analysis, topic modeling, and single-cell analysis. Many of these workflows are iterative and exploratory, requiring UMAP to be run repeatedly as users analyze their data or tune parameters. As datasets grow…
Nvidia has disclosed that it owns nearly 123 million shares in SpaceX, further highlighting the chipmaker’s entangled financial relationships with some of its biggest customers.
The $5.5 trillion company owned SpaceX stock worth nearly $21 billion at the end of June, according to an SEC filing on Friday. Elon Musk’s rocket conglomerate’s shares have fallen sharply since its June initial public offering, meaning Nvidia’s stake would now be worth $17 billion.
The disclosure marks a huge pay-off on Nvidia’s investment in xAI, completed in January, shortly before Musk combined the AI lab with SpaceX.
© Bloomberg
Mergers, acquisitions, and the steady churn of business and technology initiatives are creating something nobody asked for: Duplicate infrastructure and expertise.
Here’s the typical split: A platform engineering team that owns cloud-native and Kubernetes workloads. Meanwhile, traditional IT holds the keys to virtual machine (VM) workloads. Two teams. Two domains. One budget. And the costs keep going up.
Even organizations that talk about standardizing on Kubernetes still have a substantial VM footprint. For many teams, this coexistence isn’t a temporary transition state. It’s the operating model.
On-premises, this split forces two separate environments. Each environment includes networking, servers, and storage. Such duplication can be structurally less cost-efficient than consolidation. VM-based mission-critical workloads aren’t going away anytime soon.
It’s not like teams don’t want to modernize. They absolutely do. But it’s not as simple as just picking between old-school VMs or diving into Kubernetes. What’s really happened is these two worlds have grown up on their own.
That kind of split often leads to extra infrastructure, more people doing the same jobs, slower projects, mixed-up governance, and budgets that keep ballooning. And when you’re on-prem or working at the edge, running two separate setups for networking, compute, storage, and playbooks just doesn’t make sense anymore.
To make matters worse, “just rewrite” bares its fangs on the modernization initiative. Finance and executive leadership see two teams running two tech stacks. It’s only natural that they reach for the obvious fix: Pick one team’s platform, with no technology consideration, migrate everything to it, and watch the added cost disappear from the executive briefing slide and move to the CFO’s budget spreadsheet.
Over time, we learned from our customers that “rewrite it” isn’t a modernization strategy. Rather, it’s a budget, risk, and timeline strategy all at once. In many cases, rewrite it doesn’t make sense financially. The tech industry loves the idea of re-platforming and re-architecting legacy applications. Even then, such a move only returns your enterprise to square one and functional parity. The more realistic path is to keep mission-critical applications as-is when scaling out cloud-native platforms to deliver new value.
Moving everything to Kubernetes/cloud initiatives won’t prevent two platforms either. Such initiatives often stall because some workloads don’t fit or take far longer than planned.
The economics of rewrites don’t disappear just because AI accelerates software delivery. AI can compress the time it takes to write code. However, writing code was never the expensive part of a rewrite. The costs that dominate many rewrite budgets are judgment costs, and those remain stubbornly human.
Start with architecture. Organizations still need software engineering expertise to design the target system. That design problem has gotten harder, not easier. Cloud-native applications built on microservices for horizontal scaling bear little structural resemblance to the traditional enterprise applications they replace. Someone has to make those translation decisions and then spend the time directing the AI on what to build. That direction time is a real line item.
Validation is the next cost that survives. When customers or employees depend on a piece of software, even small behavioral changes are disruptive, making it non-negotiable to prove feature parity. Testing and validating that parity remains heavily human work. AI can generate test cases. It can’t tell you which broken workflow will cost you a customer.
Then comes the data. Teams must migrate and adapt data to the new system, and that work almost always surfaces complexities nobody scoped, including undocumented dependencies and format assumptions baked into decades of records. No amount of generation speed on the code side makes the data side move faster.
The rewrite math changes shape with AI. It doesn’t shrink to zero. The spend shifts from writing software to decision-making, verification, and migration.
The rewrite math changes shape with AI. It doesn’t shrink to zero. The spend shifts from writing software to decision-making, verification, and migration.
We see the same pattern repeat with rewrites among our customers. They keep mission-critical systems running as they are. Then they build new value with cloud-native applications in parallel. Modernizing selectively only when it’s truly worth it.
The gap we see isn’t philosophical — VMs versus containers — it’s operational. The tooling, workflows, and skills that define VM and cloud-native operations differ. If platform teams can’t deliver these services at the expected velocity, developers will blame the platform. When developers are accustomed to provisioning core services in minutes, any friction in on-prem or edge environments is perceived as the platform adding friction or slowing delivery.
The gap we see isn’t philosophical — VMs versus containers — it’s operational.
Historically, day-to-day operations in VM environments are UI-driven. Cloud-native environments are much more command-line interface (CLI) driven, where APIs, config files, and the terminal are the center of gravity. That gap becomes both an organizational and technical constraint. Moving from UI-driven operations to deep command-line interface (CLI)/config workflows isn’t a natural step without a significant shift in the team’s capabilities.
The operational gap shows up quickly in data services. Cloud-native workloads don’t just need compute. They need databases, object storage, file, and block services delivered at cloud-like speed. And despite the myth that containers are stateless, the reality is that most meaningful workloads have state somewhere as data, logs, metrics, or dependencies that must be handled consistently.
Another notable gap is that storage consumption differs:
The public cloud, by shaping cloud-native expectations, further contributes to the gap. Developers can click to get a database, such as Amazon Relational Database Service (RDS), and object storage, such as Amazon Simple Storage Service (S3), is just there. Developers expect this level of self-service simplicity when these platforms are extended beyond the public cloud, which isn’t always something platform teams are prepared for.
Today, edge and disconnected environments, such as air-gapped computing, have moved from niche use cases to mainstream constraints. When connectivity is intermittent or when latency matters, platform assumptions change. In these environments, reliability isn’t an IT metric. It’s a business outcome. Even minutes of downtime can cause major financial loss. It’s also a sign that data gravity is driving more pragmatic architectural conversations about the growing need to locate compute and data services closer to where data is generated.
AI raises the stakes further. If you’re collecting data at the edge, shipping it away for processing and pulling results back can be too slow and too expensive.
Before we’d bet on any platform, we’d ask a basic question: Can a single team operate both VM and Kubernetes environments without duplicating the entire organization? We’d insist on consistent governance: security controls and role-based access control (RBAC) should not fracture just because workloads are deployed differently.
We’d also look for cloud-like data services — object, file, block, and database capabilities — delivered quickly enough to keep developers moving toward their delivery targets, and designed to scale easily as application usage expands.
Then we would evaluate whether the platform helps reduce on-prem duplication. If it forces parallel networking, storage, and operational runbooks, the cost structure won’t improve.
Finally, we’d scrutinize lifecycle operations, including patching, upgrades, and maintenance, because “heroic” weekend work isn’t a sustainable strategy.
We use “dual native” to reject the binary choice. Enterprises need platforms that are both VM-native and container-native. Some workloads benefit from the operational efficiency of virtualization. Others are sensitive to latency or specialized hardware and are better served on bare metal. A one-size-fits-all mandate creates friction on both sides.
Dual native platform architecture isn’t just integration. It’s the one operational model that treats VMs and containers as first-class citizens. Teams no longer have to pick one architecture or stitch together separate stacks. In this model, organizations can keep mission-critical VM workloads running while building and scaling new cloud-native applications. Teams can maintain consistent management, governance, lifecycle operations, and cloud-like data services across VMs and bare metal servers across globally distributed infrastructure.
Nutanix Kubernetes Platform (NKP) solution with NKP Metal, which extends the Nutanix operating model and the NKP solution, supports Kubernetes deployments directly on bare-metal infrastructure. This solution provides unified Kubernetes operations, shared data services, centralized visibility, and automated bare-metal lifecycle management to support a dual native platform architecture.
Our approach with NKP starts with the premise that VM and bare-metal Kubernetes should operate under a consistent model rather than be split into separate toolchains and teams. To that end, a major focus has been on unified data services across deployment targets so the storage layer doesn’t become the breaking point when workloads span VMs and bare metal. We also purposefully centralize day-to-day operations and visibility across VMs and Containers in NKP so teams aren’t forced to manage two worlds with two separate management planes.
NKP Metal addresses lifecycle management, one of the biggest challenges of running bare metal at scale, including host OS setup, patching, and upgrades without resorting to late-night or holiday/weekend manual maintenance windows.
Some things we know with confidence. VM workloads aren’t disappearing — the coexistence of VMs and containers will remain the operating model for many enterprises well into the next decade. Edge and AI workloads will likely continue to pull compute toward where data is generated, and budget pressure on duplicated infrastructure will likely only intensify.
What we don’t know is the pace. How quickly enterprises consolidate two platform teams into one depends on skills, internal politics, and licensing decisions, which vary widely from one enterprise to the next. Nobody can credibly predict a timeline there.
What we think is coming: AI inference at the edge will make bare metal a first-class deployment target rather than a special case, and platform teams will be judged less on which architecture they picked and more on whether developers can self-serve their own infrastructure, including data services, without opening a ticket.
The path forward is about building an operational foundation that accepts reality where VMs, containers, and bare metal coexist under a unified model.
The path forward is about building an operational foundation that accepts reality where VMs, containers, and bare metal coexist under a unified model. Enterprises that will thrive in this future are those adopting dual-native approaches that are ready for whatever comes next.
The post “Just rewrite it”: What platform teams really think about modernization appeared first on The New Stack.
NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users...NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users access to CUDA-X performance for common math operations without disrupting existing workflows. Depending on the API, operations can run on a CPU, CUDA-enabled GPU, or distributed multi-GPU, multi-node systems.
Login