Infosys Cuts Its Growth Forecast in the Same Quarter Its AI Revenue Jumped 29%

Infosys now expects revenue to grow only 1% to 2% in constant currency for the fiscal year that ends in March, down from the 1.5% to 3% range it gave in July. That July range was itself a cut from the 1.5% to 3.5% the company set in April. The latest reduction came in a quarter when AI work rose to 10.5% of revenue, up from 8.2% three months earlier.

The company reported September-quarter revenue of $5.145 billion on Friday, up 1.5% from a year earlier and 1.2% from the June quarter in constant currency. The operating margin was 21%, and Infosys kept its full-year margin target of 20% to 22%. Most analysts had expected India’s second-largest IT services provider to trim only the top of its range, to 1.5% to 2.5%, Reuters reported.

Bookings moved the other way. Large-deal contract value reached $5.2 billion, 62% of it net new, compared with $3.6 billion in the June quarter. The total included what CEO Salil Parekh called “a mega deal of over $2 billion,” though the company’s results release doesn’t name the client. “Our AI revenue was at 10.5% and grew 29% quarter-on-quarter,” Parekh said.

Tech and retail clients spent less

Results by industry were uneven. Revenue from Hi-Tech clients fell 17.3% from a year earlier in constant currency and Retail dropped 7.7%, while Life Sciences grew 32.5% and the energy and utilities group rose 7.3%, Fortune India reported, citing the company’s fact sheet. North America, which brings in 54.6% of revenue, slipped 1.5%. Europe grew 4.6%.

Reuters said clients increasingly tie fees to productivity gains from technology rather than hours worked, and that AI has pushed companies in India’s $315 billion IT industry to overhaul their business models and offer steep discounts. Tata Consultancy Services and HCLTech, which reported earlier this month, both met revenue estimates, and their shares rose as investors cheered higher AI revenue. On the buying side, WebProNews reported last month that many executives struggle to point to clear financial gains from their AI budgets.

“We delivered operating margins of 21.0% in Q2 amidst heightened volatility,” CFO Jayesh Sanghrajka said. Free cash flow was $928 million, down 15.7% from a year earlier, and the board declared an interim dividend of ₹26 a share, up 13%, with a record date of Nov. 3.

Infosys ended September with 332,542 employees, 4,480 more than in June. Voluntary attrition in IT services fell to 12.7% from 14.3% a year earlier.

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WhatsApp Rolling Out Beta of Scheduled Messages for iOS

WhatsApp is finally rolling out a feature that many of its competitors have had for a long time, introducing a beta release of scheduled messages for iOS.

WhatsApp may be the most popular messaging platform on the planet, but it lags behind its competitors with some key features, most notably scheduled messages. The company is finally beginning to rectify that with a beta for iOS.

According to WABetaInfo, WhatsApp has already rolled out a beta of scheduled messages for Android.

In the post about the WhatsApp beta for Android 2.26.40.7 update, we announced a message scheduling feature. The feature lets users compose a message and choose when to send it. Users can access the schedule message screen by tapping and holding the send button. This action opens a bottom sheet where users can set the date and time for delivery. The update supports text messages, photos, videos, and links, and it is compatible with end-to-end encryption. Scheduled messages are available in a dedicated section in the chat info screen, where users can view and delete them before the app delivers them.

The outlet reports that WhatsApp is now expanding its beta of scheduled messages to iOS.

WhatsApp introduced the message scheduling feature on the Android app last week. Finally, WhatsApp is making it available on the iOS app as well. We discovered the news with the WhatsApp beta for iOS 26.42.10.19 update, which is available on TestFlight. With this update, WhatsApp is rolling out scheduled messages to some beta testers. This feature makes it easier for users to choose the exact moment to send a message, even when the device is offline.

WABetaInfo reports that scheduling a message is as simple as holding the send button.

Users can schedule a message from the chat interface. After typing the message into the chat composer, they need to tap and hold the send button. WhatsApp opens the schedule message screen with a date and time picker. Users can scroll through the page and tap the green button at the bottom of the screen to confirm. The button shows the exact delivery time before users schedule the message. After scheduling a message, WhatsApp shows a confirmation in the chat, but it is only visible to the user who scheduled it.

The beta is good news for WhatsApp users who waited a long time for such a basic feature.

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Microsoft Begins Copilot Code Rollout

Microsoft has begun its Copilot Code rollout, giving users the ability to develop software using natural language, and taking aim at similar tools from Anthropic and OpenAI.

AI coding tools have revolutionized software development. The uses range from full vibe coding to helping experienced developers work faster. Anthropic’s Claude Code and OpenAI’s Codex have already carved out loyal followings, and Microsoft is now getting in on the action with Copilot Code.

Microsoft’s Marc Wautier disclosed the news in a company blog.

Copilot Code is a new AI-powered coding experience in Microsoft Copilot that helps people turn ideas into working software. It starts with the work people already do, helps them build solutions through natural language, and connects to professional development workflows as projects grow.

Start with the outcome. Describe the goal in natural language. Copilot then asks clarifying questions and, powered by Work IQ, draws from emails, meetings, documents, chats, and data from connected sources to build out requirements , even if they are not all in the initial prompt.

• Build from work context. Copilot asks clarifying questions, then builds a live control center that brings together data, assumptions, and business context into a working application.
• Refine through conversation. As priorities change, update the application through conversation or direct edits. Add new visualizations, business logic, and data sources without starting over.
• Stay in preview or open the code. Work entirely from the live preview without ever seeing code, or open it to edit directly, work with local files and tools, or use the terminal to customize the solution further.
• Run locally or in the cloud. Keep the project on a local device for personal productivity and refresh it as needed or publish it to a Microsoft-managed cloud environment so it can be shared with others.

The result isn’t a static file that has to be rebuilt every time the inputs change. It’s a working application, and one that can be shared.

Wautier says the company has worked to ensure any apps built with Copilot Code are easy to share and deploy.

What you build in Copilot Code often starts as a personal tool: a calculator to size the value of a deal, a widget that surfaces the numbers you check every morning or an app that turns a monthly process into a few clicks. When it’s ready for a wider audience, Copilot makes it simple to share across your organization.

When an app is meant to be shared, Copilot builds it on Copilot Managed Runtime, so publishing requires no separate deployment work. Anyone in the organization with permission to the underlying data can use the app. The application inherits the governance, security, and management controls your organization has already configured. Teams get the benefit of custom-built solutions without adding a separate system for administrators to manage.

Copilot Code will likely prove to be a valuable resource for IT departments, execs, marketing departments, and other professionals heavily invested in Microsoft’s ecosystem.

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GitHub Puts a Firewall Between npm and the Agents That Install Packages

Coding agents and CI runners install dependencies all day without a person watching, and that makes them an easy target for poisoned packages. GitHub’s answer is a package firewall that sits between those machines and the public npm registry, now in public preview for GitHub Enterprise Cloud customers.

The firewall inspects both metadata requests and package downloads and acts as what GitHub calls “an install-time policy gate.” Endpoints point at the firewall instead of going straight to upstream registries, and every request is checked against rules an administrator sets for the enterprise. GitHub announced the preview in a changelog post on Tuesday and rewrote the entry Wednesday “for clarity.”

The company tied the launch to “a recent flood of malware attacks that exploit systems at package installation time.” WebProNews reported last month on malicious npm packages that stole AWS keys and GitHub tokens, the kind of install-time attack this tool is built to stop.

What admins can set

The policy options at launch are a configurable cooldown period, checks against the GitHub Advisory Database for malware or vulnerability advisories, and supply-chain checks such as attestation downgrades.

GitHub said a user whose package is wrongly blocked can request an exception through a workflow similar to the one used in other GitHub Advanced Security features.

There are limits. The firewall supports only npm for now, so Python, Java and Go shops get nothing from it yet. It is open to GitHub Enterprise Cloud customers, including those using Enterprise Cloud with data residency. Setup steps are in GitHub’s documentation on protecting endpoints with package firewall.

The preview is free. GitHub said it will move to metered billing “similar to Actions billing” at general availability but has not published rates or a date for that switch.

Source: GitHub Changelog

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Amazon Says Attackers Hit PHP2Shell Four Days Before the Bug Went Public

Attackers were already probing for the PHP flaw known as PHP2Shell four days before it was supposed to become public, Amazon’s threat intelligence team said in a security blog post Wednesday evening.

The team found an exploitation attempt in telemetry from MadPot, Amazon’s network of honeypot sensors, dated Saturday, Oct. 17. The request came from an IP address tied to a commercial anonymization network. The two vulnerabilities, CVE-2026-109618 and CVE-2026-109619, were under embargo until 1400 UTC on Oct. 21, when PHP released fixed versions.

“While the vulnerability was under embargo, the targeting of a honeypot from an anonymized IP address suggests the activity was conducted by a threat actor, though it remains unclear how the actor acquired exploit information prior to disclosure,” Amazon wrote.

Exploits arrived before the patch

The timeline on disclosure day was just as compressed. Amazon said it saw multiple proof-of-concept exploits starting around 0200 UTC on Oct. 21, about 12 hours before the embargo lifted, and logged more attempts against MadPot from other anonymized infrastructure between 0200 and 0800 UTC. The payloads and infrastructure differed, which Amazon read as a sign of several separate actors. It also said its technical analysis is ongoing and that it is unclear whether those attempts would have worked against real systems.

Once the patch shipped, new exploits showed up within hours. Amazon now describes PHP2Shell as “broadly exploited in the wild.”

The post puts part of the blame for the shrinking gap on AI. “AI-assisted analysis makes it cheaper to turn a patch diff or advisory into a working exploit,” the team wrote, adding that more actors can now move within hours or even minutes of technical details coming out.

What Amazon tells defenders to change

For admins running PHP on internet-facing servers, Amazon’s advice starts with patching. Upgrading to the fixed PHP releases is the only complete remediation for both CVEs, and Amazon said no configuration-only workaround fully covers them. Teams that cannot patch right away can consider blocking multipart file uploads, after checking what that breaks, and should look for POST requests containing the PHP_SESSION_UPLOAD_PROGRESS string in form uploads.

Amazon also recommends starting PHP2Shell investigations from Oct. 17 at the latest rather than from the advisory date, and measuring patch timelines for critical internet-facing flaws in hours. “A 7-day or 30-day SLA assumes a delay that no longer exists,” the post said.

The blog lists six IP addresses seen in the activity, one from Oct. 17 and five from Oct. 21. Amazon cautioned that they belong to shared anonymization services and told organizations to investigate hits on them instead of blocking the addresses outright. A separate AWS post covers AWS WAF rules for PHP2Shell as a stopgap while servers are patched.

Amazon said its systems analyze more than 200 billion events per minute and use MadPot data to block known exploitation attempts in real time.

Source: AWS Security Blog

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NVIDIA DSX POD Aims to Cut Vera Rubin AI Factory Deployment From Months to 14 Days

NVIDIA on Wednesday, Oct. 21, 2026, at GTC Berlin introduced DSX POD, a reference design for complete NVIDIA Vera Rubin AI factories that partners build inside modular containers, integrate and validate before shipment. At a prepared site, NVIDIA said deployment time falls from six months to as little as 14 days once power, cooling, networking and related utilities are ready. A global partner list is working toward worldwide delivery in early 2027.

Partners complete rack-to-rack cabling, wiring, piping and functional validation before shipment, moving that work into the manufacturing facility. The units arrive ready to connect to a prepared site’s power, cooling, networking and other supporting infrastructure. NVIDIA said that moves planning, construction, specialized labor and logistics off the traditional onsite build and reduces exposure to component delays and error-prone field work.

“An AI factory creates value when it starts producing tokens — not when construction is complete,” said Jensen Huang, founder and CEO of NVIDIA. “With NVIDIA DSX POD, we’re bringing factory-built precision and speed to AI infrastructure construction — reducing deployment time from months to weeks so customers can scale capacity and bring revenue-generating production online faster.”

Partners and the Berlin framing

NVIDIA named Anduril, Armada, Bull, Crusoe, DayOne, Dell Technologies, Delta, Eaton, Foxconn (Ingrasys), GIGABYTE, HPE, Lenovo, Pegatron, Quanta Cloud Technology (QCT), Schneider Electric, Vertiv and Wistron among the companies working to deliver DSX POD AI factories. The design carries forward the broader NVIDIA DSX infrastructure platform while packaging systems in purpose-built containers for transport and deployment.

In live keynote notes on the NVIDIA Blog, Huang cast DSX POD as a supply-chain shift: “This is a complete transformation of the world supply chain. This will change the way that people install and build data centers, and hopefully, as a result, velocity goes up.” The same keynote summary said the NVIDIA DSX AI Factory Platform can cut cooling water use by up to 100% and produce the same AI output with up to 30% less power — figures NVIDIA also used in its companion European DSX efficiency release the same morning.

My take: DSX POD is less a new chip than a logistics product — the same Vera Rubin stack, sold as a factory-tested container that starts earning tokens sooner. That sits next to other recent pushes to make AI infrastructure easier to stand up, including Google’s open-sourcing of TorchTPU and llm-d 1.0 for TPU inference on Oct. 20, 2026.

NVIDIA said partners plan to deliver DSX POD AI factories worldwide in early 2027.

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NVIDIA Packages OpenShell Into a Full-Stack Safe-Agent Reference Architecture at GTC Berlin

NVIDIA on Wednesday, Oct. 21, 2026, at GTC Berlin announced the NVIDIA OpenShell Reference Architecture, a full-stack design for building, containing, observing and enforcing safe AI agents on enterprise infrastructure and at the edge. The company said the architecture is available now.

The design sits on NVIDIA OpenShell secure runtime software and pairs three hardware layers the company named in the release: NVIDIA Vera Rubin infrastructure for model inference (including closed and open models built with NVIDIA Nemotron), NVIDIA Vera CPUs for agent harnesses and tool use, and NVIDIA BlueField-4 DPUs running NVIDIA Sentry for continuous out-of-band monitoring and in-silicon policy enforcement. Edge coverage extends to NVIDIA RTX Spark, DGX Spark and DGX Station systems.

WebProNews covered the earlier open-source OpenShell runtime and Sentry hardware watchdog on Sept. 29, 2026. The new reference architecture extends that OpenShell runtime into full-stack infrastructure for agent development and operations, according to NVIDIA.

Layered containment and monitoring around an AI agent runtime. Illustration: WebProNews

What the stack claims to do

In the release, NVIDIA said Vera Rubin NVL72 systems deliver 30x higher throughput per megawatt for agentic workloads, Vera CPUs deliver 2x the performance of x86 processors with greater energy efficiency while running OpenShell, and BlueField with Sentry can quarantine an agent that moves outside its software boundary in milliseconds while boosting agent capacity by up to 20%. Those figures come from NVIDIA; independent benchmarks were not cited in the announcement.

“AI capability and safety must advance together,” said Jensen Huang, founder and CEO of NVIDIA. “As enterprises prepare to deploy billions of AI agents, safety and control must be built into the computing architecture. NVIDIA created OpenShell to give agents the freedom to do useful work within clearly defined boundaries — contained, monitored and under control. With the OpenShell Reference Architecture, agents can think on Rubin, act on Vera and be governed on BlueField.”

Huang made the same case in his GTC Berlin keynote, according to live notes on the NVIDIA Blog, where he described an agent as a model plus harness, sandbox, knowledge and tools, and said an agent’s privileges should be minimal and monitored. “Every single time an agent is launched, the privileges are different,” Huang said.

Who is wiring into it

NVIDIA said the architecture runs on Linux and Kubernetes stacks from Canonical, SUSE and Red Hat, and on Microsoft Windows using Microsoft Execution Containers — the MXC path WebProNews previously covered when Microsoft put policy enforcement around Windows agents. Cloud operating systems listed include Amazon Web Services, Google Cloud and Microsoft Azure.

NVIDIA said enterprises can run agents on OpenShell infrastructure from model builders including Anthropic, Cohere, Google, Meta, OpenAI, Perplexity, Reflection and SpaceX Super Intelligence. Cybersecurity vendors listed as integrating support include 1Password, Check Point, CrowdStrike, Cisco, F5, Fortinet, Microsoft, Okta, Palo Alto Networks, TrendAI and Zscaler. System makers Dell Technologies, Cisco, HPE, Lenovo and Supermicro are among those building infrastructure to the design, and cloud providers named include CoreWeave, Crusoe, Lambda, Nebius, Nscale, Oracle Cloud Infrastructure and Vultr.

On the application side, NVIDIA said SAP is integrating the architecture with Joule Studio runtime on the SAP Business AI Platform, and that Palantir is working with NVIDIA to deploy secure agents in production. Banks BBVA and Santander were named as exploring the architecture for agent safety and governance. NVIDIA’s release also said ServiceNow announced general availability of ServiceNow Arc, a personal long-running AI agent whose actions are governed at runtime by the ServiceNow Super Intelligence Control Tower and enforced by NVIDIA OpenShell.

The OpenShell Reference Architecture is available now, according to the Oct. 21 release.

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