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U.S. technology company Apple disclosed in its latest fiscal fourth-quarter results that its inventory levels rose significantly, growing from $11.09 billion in Q3 to $14.54 billion in Q4, a 31.1% increase quarter-over-quarter. Compared to $5.72 billion in the same period of 2025, inventory surged 154.3% year-over-year, reflecting the company's stockpiling strategy amid tight component supplies.

Apple CEO John Ternus stated during the earnings call that the current situation with memory pricing is "unprecedented." He noted that while carrying inventory and lower costs on certain non-memory components partly offset higher memory costs seen since June, the company expects even higher memory costs in the December quarter. Consequently, Apple lowered its gross margin guidance for the next quarter to between 45% and 46%, down from 47.9% in the September quarter.

Ternus also revealed that Apple's manufacturing purchase obligations increased by $33 billion from June to the September quarter as the company works to improve supply. Chief Operating Officer Sabih Khan joined the discussion on memory issues, reiterating the increased purchase obligations and not ruling out future price increases on devices. Additionally, weak iPad sales were attributed to supply constraints, with revenue at $6.2 billion, below analyst expectations of $6.7 billion and down 10% year-over-year.

US-based AI company Anthropic has updated the system prompt for its coding tool, Claude Code, to explicitly prevent it from being used to recreate specific existing video games. The change was confirmed by Anthropic engineer Addy Osmani on November 2, 2026, aiming to curb growing issues with AI-generated game infringement.

Osmani stated that two key sentences were added to the prompt for the Opus 5.5 model: “Do build original games, games in a genre, and games from the user’s own designs and assets. Don’t build a recreation of a specific existing game, even if it is named or called original.” This follows reports from Tom's Hardware and screenshots shared by users on social media highlighting the new restrictions.

While direct cloning is blocked, Anthropic emphasized that users can still create original games, work within specific genres, or use their own assets. Osmani noted that this aligns with the company's broader usage policy against intellectual property infringement. He indicated that Anthropic would continue to iterate on these guidelines to balance creative freedom with legal compliance, inviting users to report false positives where the AI refused legitimate requests.

As of the close on November 2, 2026, the tech-heavy Nasdaq 100 Index has fallen 10% from its peak in October, officially entering a technical correction. The S&P 500 Index declined approximately 4% over the same period.

The Philadelphia Stock Exchange Semiconductor Index (SOX) dropped 3.4% on Monday, adding to Friday's 7.2% plunge. The chip-stock gauge failed to rise above its 200-day moving average on Monday after closing below that level for the first time since June 2025.

"Lower oil should be a tailwind for tech, but the fact that stocks could not hold the open even with crude plunging tells shows how little conviction is left even after last week’s carnage," said Dave Mazza, chief executive officer at Roundhill Financial.

Vercel has announced that its AI Gateway now supports the LongCat 2.5 model from Meituan. This model combines native visual understanding with coding and multi-step tool use capabilities, designed to assist agents working across complex codebases and full-project builds.

According to Vercel, LongCat 2.5 features improvements in visual programming, creative design, and 3D tasks, alongside longer agent workflows that carry engineering work from planning through execution. The model supports text and image inputs with a 1 million-token context window for handling code, documents, and extended task histories.

Developers can access the model by using meituan/longcat-2.5 as the model name. It is compatible with coding agents such as Claude Code, Codex, and Cursor, requiring the latest Vercel CLI installation and running npx vercel ai-gateway setup for configuration.

Vercel AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations. The gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including for Bring Your Own Key (BYOK) requests.

Configuring Kubernetes for new GPU hardware is a process prone to errors. Drivers, kernels, container runtimes, networking, operators, and schedulers must work in concert, with settings tuned specifically to the hardware and workload. Even minor mismatches can prevent workloads from running or limit their performance. In a technical blog post dated November 2, 2026, NVIDIA cloud architect Mark Chmarny and software engineer Yuan Chen detailed how they completed this adaptation on early Vera Rubin NVL72 hardware and solidified their experience into versioned recipes within the NVIDIA AI Cluster Runtime (AICR).

The upgrade from GB200 NVL72 to Vera Rubin NVL72 involves more than fine-tuning; user-visible changes are significant. The GPU driver requires a new branch: the validated R615.23 is an open-source kernel module driver pre-installed on the host, while the official platform support target is R620. On the network side, ConnectX-7 NICs and BlueField-3 DPUs are replaced by ConnectX-9 SuperNICs and BlueField-4 DPUs, with DOCA 3.3.0 being the first GA release supporting ConnectX-9. The device topology also differs: the Vera Rubin NVL72 compute tray exposes eight groups of ConnectX-9 PCI devices totaling 32 functions, whereas the GB200 has only four ConnectX-7 adapters. Multi-node NVLink workloads still rely on IMEX, but DRA-managed IMEX is mutually exclusive with the host's static nvidia-imex service, which must be disabled.

These changes are scattered across various layers, from low-level bindings to the orchestration layer. AICR addresses this by encoding all findings into recipes: host-managed mode drivers (GPU Operator sets driver.enabled: false, while the container toolkit remains operator-managed), DRA drivers enabling ComputeDomains, a minimum Kubernetes version of 1.34.1 (1.34 provides the GA resource.k8s.io/v1 DRA API, and the .1 patch is required because the RKE2 CDI/NRI toolkit path needs containerd 2.1), Ubuntu 26.04 paired with a Vera-optimized arm64 64k-page kernel, and raising the NFD topology updater's default 60Mi memory limit—otherwise, it triggers OOM kills on multi-TB NVL72 worker nodes. The recipe also introduces DraNet for allocating ConnectX-9 devices to Pods and employs NodeWright to enforce an exclusive RDMA network namespace mode, ensuring each Pod sees only its own Host Channel Adapter.

The validation process executes in four steps on a live Vera Rubin NVL72 reference cluster. Step one generates the recipe, offline parsing 14 components and 5 overlay layers. Step two renders the deployment bundle, producing 61 files including ordered Helm charts and deployment scripts. Step three snapshots the cluster and runs validation: the deployment phase passes all four checks; the conformance phase passes eight, covering GPU Operator health, DRA/ComputeDomain support, KAI gang scheduling, Pod autoscaling, and secure accelerator access. Step four is the cross-node ComputeDomain test, verifying IMEX channel injection and NVLink fabric connectivity. The performance phase runs a cross-node NCCL all-reduce, completing a 16 GiB message all-reduce over the NVLink fabric via IMEX ComputeDomain, achieving a bus bandwidth of 744.71 GB/s, meeting the recipe's floor. The entire sequence (recipe, bundle, snapshot, deployment, and conformance validation) completes in about five minutes, with the performance phase adding the runtime of the NCCL job itself.

The PubHub internal publishing tool at Cisco DevNet underwent a time-constrained redesign in 2026. Instead of following the traditional process of designing first and then implementing, the team had an AI coding agent generate end-to-end interfaces directly from user stories and backend APIs. The result was usable: users could complete the main flows, and the interface connected to the backend. However, designer Alejandro Marrufo and Head of UX Casey Tong wrote in a blog post that "usable" does not equal "finished"—the interface did not follow Cisco's visual language, some AI-generated components were unintuitive, and multiple workflows needed cleanup.

This raised a practical question: how can we leverage AI to accelerate design modifications and integrate them into the final product workflow? Their initial attempt involved converting existing HTML pages back into Figma, allowing designers to continue refining in a familiar tool, and then using a Figma MCP connection to sync changes back to code via the coding agent. The idea was appealing—keeping design work within Figma while AI handled the transfer to implementation.

In practice, three issues emerged. First, details were lost in transit: the round trip between Figma and code missed interactions, complete page and button flows, and animations—the very elements that make an experience feel "finished." Second, updates were slower than expected: modifying the HTML prototype via Figma was slower than the project timeline allowed; some pages even had to be rebuilt in Figma and reconnected to flows before any refinement could begin. Third, the source of truth became blurred: with Figma files and HTML prototypes evolving in parallel, it was difficult for designers and other team members to determine which version was current.

Figma is not without merit. It remains a superior tool for maintaining asset libraries and handling fine-grained UI elements. However, under tight deadlines, it is not the fastest place to refine an interface that is already running in code.

So they tried another path: letting AI coding tools inspect the existing prototype and modify its HTML directly. Design work remained within the existing interface, eliminating reconstruction steps. The HTML prototype became the single source of truth. Designers could make staged changes, review each iteration, and provide feedback to the AI along the way. Design work no longer required rebuilding the interface in a design tool first; instead, it targeted a running implementation directly, including interactions and flows that Figma struggled to capture.

A practical constraint quickly surfaced: token limits. Midway through the project, the designer ran out of tokens, switching from Cursor to Claude, then to Codex, resuming once limits reset. The work survived these switches because the HTML artifact—not any specific tool—held the state of the design. They recommend that teams adopting this approach plan for such constraints in advance.

With this approach, designers could edit and deliver working HTML pages daily, producing new iterations. Under the old Figma-first process, a single page took days. This speed created a compounding effect across the team: faster design output meant faster engineering testing, which in turn meant faster feedback for the next design iteration.

According to German tech outlet heise online, the price of NVIDIA's second-fastest high-end gaming graphics card, the RTX 5080, has risen by more than 30 percent in the last two weeks, making it difficult to find below 2,000 euros. This surge was partly fueled by earlier rumors that the flagship RTX 5090 might be discontinued. Although NVIDIA Vice President Jeff Fisher later denied these claims, market anxiety had already driven up demand for the secondary product. Meanwhile, the price of the RTX 5090 itself has climbed to around 8,000 euros.

More critically, the planned 24GB VRAM version of the RTX 5080, intended as a potential alternative or supplement to the RTX 5090, appears to be delayed. According to Taiwan-based hardware news site Benchlife, graphics card manufacturers have not yet received the complete design documents from NVIDIA for this new version, which were originally scheduled for October. Additionally, existing stocks of the GB203 GPU chips at manufacturing bases in China are sold out, with only enough supply to last one to two months. This casts significant doubt on the original plan to launch the new RTX 5080 during the CES exhibition in January 2027.

The root cause of this shortage is the ongoing artificial intelligence boom. NVIDIA uses the same GPU chips for its workstation graphics cards (such as the RTX Pro series), which feature larger memory capacities suitable for running local AI models and offer higher profit margins. For instance, the GB202 chip is used in both the RTX 5090 and professional cards like the RTX Pro 6000. With GDDR7 memory also in short supply, NVIDIA prioritizes the higher-margin professional workstation market, thereby squeezing the production capacity and pricing space for consumer gaming graphics cards.

Large models can confidently tell you there are vulnerabilities in your code, but you have no way of knowing which claims were actually checked, whether you will get the same answer next week, or why a specific finding exists six months from now when an auditor asks. Cisco Chief Engineer Julio Gomez argues that this gap represents the entire distance between LLM security tools moving from demonstrations to actual security programs.

He implemented the Foundry security governance specification released by Cisco—a design document rather than a product—and applied it to real code: the complete v1 API of Langflow 1.7.3 (289 function-level units across 25 modules), MLflow 2.9.0 (830 units), and OWASP's PyGoat training application (180 units). He set himself a hard rule: no strawman baseline. The control group used the same model, the same code, and a fair, disciplined, equally demanding prompt from a senior application security expert requiring structured findings and precise line-number citations.

The results were as follows: Foundry presented 32 findings for humans, while the baseline presented 15; both had 100% citation accuracy; but the baseline marked 14 out of its 15 findings as 'confirmed' with no mechanism enforcing that judgment, whereas Foundry's 28 confirmed and 4 pending-review items were delineated by mechanical checks. Gomez summarizes this by saying: The model was never the bottleneck; trust was.

There are two core design decisions in Foundry. First, detection is inclusive, while decision-making is strict. The Detector is instructed to prioritize recall—bringing forward anything plausible—all discipline being placed downstream. This allows the same model to be thorough and trustworthy simultaneously, rather than forcing a choice between the two. Second, the evidence gate is not a second opinion. It never asks the model 'are you sure?'. Instead, it takes every citation back to verify against the bytes on disk: the file must exist in the target, the line number must be within range, and the quoted text must actually appear there. Only findings whose citations survive are labeled 'confirmed'; others are downgraded to 'pending review' or rejected outright. A model cannot argue its way past a line number that does not exist.

Gomez illustrates why this separation matters through repeated runs. He independently ran the full pipeline twice; the 122 records stored collapsed into 67 distinct fingerprints, with 56 recurring across both runs and landing on existing identities—roughly 90% overlap, despite the inherent non-determinism of model output. This means the next CI run can calculate deltas (new, recurring, gone) rather than forcing the team to re-read a wall of differently worded text. After the second run, teams usually stop running it again; once stable identities are established, they continue.

On the broader scope of MLflow, governance held true: 830 units, 41 batch calls, and 4.8 million tokens completed within the declared 6 million cap. Findings landed in the same area as MLflow's historical CVEs (path traversal in the artifact storage layer). However, only 13 were labeled confirmed, while 110 were leads pending review—the gate became more conservative on large-scale unfamiliar code, which is precisely what the layered approach is doing right.

KPN, the largest telecommunications provider in the Netherlands, has partnered with AI audio company ElevenLabs to launch Mobiel AI. The service is an add-on to KPN EEN MKB mobile subscriptions, integrated directly into KPN's existing mobile app, designed to help Dutch small business owners and freelancers manage their work calls more efficiently.

Key features of Mobiel AI include voicemail transcription for missed calls, automatic summaries and transcripts of every call (capturing follow-up actions), and a smart call menu to route callers to colleagues or departments. KPN notes that 53% of entrepreneurs report that being available by phone detracts from employee concentration, while 69% cite customer satisfaction as a top challenge; the tool aims to balance 'reachability' with 'productivity'.

Technically, the partners optimized the underlying transcription model for Dutch, a language dense with compound words and regional accents. By gathering more Dutch-language data and fine-tuning for short, high-stakes sequences, verification accuracy improved from roughly 50% to 90%. All voice data is processed in KPN's own data centers within the Netherlands, ensuring data sovereignty, with customer data explicitly excluded from AI model training or third-party sharing.

When running distributed AI training or inference tasks on Kubernetes, a real-world workload is rarely a single Pod. It is a small resource tree: several groups of Pods, each with a role, replica count, Pod template, and status dependent on the workload type. Frameworks such as JobSet, LeaderWorkerSet, PyTorchJob, and RayCluster encode this information in different ways. Teams building schedulers, controllers, dashboards, or cost tools therefore face a combinatorial explosion problem: for every new framework CRD integrated, they must write a set of adapters to answer the same three questions—what is the structure of the workload? How is the state interpreted into operational phases? And how can Pod specifications be modified without breaking the workload?

The NVIDIA Run:ai team encountered this bottleneck while building their platform. In an NVIDIA technical blog post published in November 2026, Alon Lavian (Product Manager at NVIDIA Run:ai, responsible for positioning and promoting Workload Map), Yuval Grauer (Engineering Lead at NVIDIA and maintainer of Workload Map), and Ron Lev (Senior Software Engineer at NVIDIA and technical lead for Workload Map) described the current situation: every new customer framework requires another adapter; thus, adapter development becomes a roadmap item scheduled per framework rather than a one-time cost. This is why support for new workload types often has to wait for a version release.

Workload Map (formerly Karta; the CRD kind remains Karta) proposes moving workload structural knowledge from compiled code to declarative data. It consists of a Kubernetes CRD and a Go interpretation library. Each definition uses YAML to describe the Pod template path, replica count calculation method, and status field location for that workload type. The article uses JobSet as an example to show key parts: the root component translates the JobSet-specific replicatedJobsStatus field into unified Running, Completed, and Failed phases using jq expressions; child components use path expressions to locate the Pod template for each replicated Job and calculate the actual number of Pods by multiplying replicas by parallelism.

This definition provides more than just status translation. statusMappings maps the distinct native states of various frameworks into a unified vocabulary of Running, Completed, and Failed—since JobSet does not directly report a Running condition, the definition uses a jq expression to check if any Job is ready and active with no failures; when RayJob or LeaderWorkerSet are described, their respective different status fields are mapped to the same shared categories. Meanwhile, path expressions in the definition allow consumers to extract Pod templates, replica counts, metadata, and understand the workload hierarchy, as well as update Pod specs, labels, and annotations across all instances. Consumer tools therefore do not need to write dedicated parsing logic for each framework but instead complete inspection, scheduling, and management through the same structural description.

The article demonstrates scenarios where the same JobSet definition is used by three consumers. The Workload Map CLI loads the definition and parses live objects in the cluster, outputting a workload tree containing leader and worker roles with a total request of 12 GPUs. KAI Scheduler uses the gangScheduling instruction in the definition for Pod grouping and gang admission; for workload types without native plugins, Workload Map serves as a fallback pod grouper providing structural information, while existing native plugins retain priority. NVIDIA Run:ai registers catalog definitions via a single API call, managing JobSet as a single workload. Additionally, the multi-cluster scheduling tool Elotl Nova uses Workload Map to interpret workload structure and resource requirements during cluster selection.

At the Kinexions 2026 conference in Barcelona, Spain, Canadian supply chain planning software company Kinaxis announced a strategic pivot. CEO Razat Gaurav outlined plans to turn the company's core planning platform 'inside-out,' establishing a dedicated semantic layer that enables AI agents to coordinate supply chain processes from signal sensing to concrete action. This move targets what Gaurav identifies as the primary issue in modern supply chains: latency—the time gap between receiving signals, making decisions, and executing those decisions.

Gaurav argued that while change is often cited as the main challenge, the real problem is response speed. He estimated that inventory alone leaves approximately $17 trillion worth of goods sitting idle in global supply chains. To address this, Kinaxis intends to abstract the mathematical models and constraints encoded over decades in its Maestro system into an extensible semantic ontology layer. This layer will integrate external systems of record, including Warehouse Management Systems (WMS), order management, ERP, and financial systems, allowing agents to traverse a unified graph of dependencies.

The approach, termed 'operational orchestration,' acknowledges the fragmented reality of supply chain data. Gaurav noted that no single system of record exists for entire supply chains, but new semantic architectures combined with agentic AI can navigate this ambiguity. By understanding semantic equivalence across different systems, agents can bridge organizational silos and connect upstream planning decisions to downstream operational changes without requiring grueling custom integration projects.

This evolution reflects a broader trend in enterprise software where vendors are moving beyond predictive analytics and planning tools toward platforms capable of driving autonomous action in physical, constrained environments. While Kinaxis emphasizes that planning remains foundational, the ability to operationalize those plans through agents is presented as the key driver for the next wave of business outcomes.

U.S. data analytics software firm Palantir Technologies Inc. (Nasdaq: PLTR) released its third-quarter financial results on November 2, 2026, marking a historic shift in its business structure. In this quarter, Palantir's U.S. commercial revenue reached $984 million, growing 148% year-over-year and 29% sequentially, while U.S. government revenue stood at $953 million. This represents the first time in the company's history that U.S. commercial revenue has surpassed U.S. government revenue.

Overall financial performance was equally robust. Total revenue for the quarter was $2.313 billion, up 96% year-over-year and 20% quarter-over-quarter, crossing the $2 billion threshold for the first time in a single quarter. Adjusted operating margin was 62%, and GAAP earnings per share were $0.44. CEO Alex Karp emphasized in the statement that the "Sovereign AI" movement is forcing rapid differentiation between institutions, with Palantir's product suite (including Foundry, Ontology, and AIP) driving a self-improvement loop through this demand.

Based on the strong Q3 performance, Palantir significantly raised its fiscal year 2026 guidance. The company increased its full-year revenue expectation to between $8.530 billion and $8.534 billion, representing approximately 91% year-over-year growth. Additionally, U.S. commercial revenue guidance was raised to over $3.559 billion, implying a growth rate of at least 143%. This guidance substantially exceeds market consensus, reflecting management's confidence in the pace of AI-driven commercial adoption.