IPv6 and IoT News Archives - IPv6.net

ServiceNow integreert Armis en Veza in Autonomous Security & Risk

news-aggregator — Fri, 29 May 2026 10:07:07 +0000

ServiceNow brengt een nieuw platform uit dat identiteiten, toegangsrechten en verbonden assets samenbrengt in één overzicht. Autonomous Security & Risk integreert de recent overgenomen bedrijven Armis en Veza en richt zich specifiek op het beheer van AI-agents, OT-apparatuur, IoT-devices en cloudworkloads. Het platform omvat ook twee nieuwe AI-specialisten voor security operations en vulnerability management.

ServiceNow kondigt Autonomous Security & Risk aan, een platform dat identiteitsbeheer, asset-zichtbaarheid, risicomanagement en workflowautomatisering combineert. De oplossing integreert de recent overgenomen bedrijven Armis en Veza in het bestaande ServiceNow AI Platform.

Armis levert continu inzicht in assets voor IT, OT, IoT en andere verbonden apparatuur. Veza richt zich op zichtbaarheid en governance van zowel menselijke als niet-menselijke identiteiten. Samen moeten ze organisaties volgens ServiceNow in staat stellen om bij te houden wie of wat toegang heeft tot welke systemen, op basis waarvan die toegang is verleend en of die toegang nog geldig is.

Groei van niet-menselijke identiteiten

Het security- en risicosegment was bij ServiceNow vorig jaar goed voor meer dan 1 miljard dollar aan jaarlijkse contractwaarde, wat het tot een van de snelst groeiende onderdelen van het platform maakt. Het bedrijf stelt dat de druk op organisaties toeneemt doordat AI zorgt voor een sterke groei van het aantal identiteiten, toegangsrechten en verbonden assets.

AI-agents opereren via digitale identiteiten die toegang geven tot systemen, data en workflows. Die rechtenstructuren zijn volgens ServiceNow veelal ontworpen voor menselijke gebruikers en daarmee niet afgestemd op de snelheid en schaal waarmee AI-agents werken. Het aantal asset-identiteiten achter deze agents ligt inmiddels hoger dan het aantal menselijke identiteiten, aldus het bedrijf.

John Aisien, senior vice president en general manager Central Product Management Security & Risk bij ServiceNow, geeft aan dat CISO’s tegenwoordig twee vraagstukken tegelijk moeten managen: dreigingen in realtime neutraliseren en risico’s onderbouwd rapporteren aan de board. Volgens Aisien vervangt Autonomous Security & Risk versnipperde oplossingen door één centraal overzicht waarin preventie, detectie en respons samenkomen.

Koppeling tussen asset-context en incidentrespons

Het realtime inzicht dat Armis en Veza bieden, wordt direct gekoppeld aan de workflows voor incidentrespons van ServiceNow. De context die organisaties gebruiken om risico’s vooraf in kaart te brengen, is daarmee ook beschikbaar op het moment dat een incident zich voordoet.

ServiceNow stelt dat moderne IT-omgevingen bestaan uit IT-infrastructuur, operationele technologie, cloudworkloads, medische apparatuur en AI-agents die doorlopend communiceren over verschillende systemen en domeinen. Geen enkele afzonderlijke tool heeft daar volledig zicht op, waardoor zichtbaarheid van assets voor veel organisaties een aanhoudende uitdaging blijft.

Twee nieuwe AI-specialisten

ServiceNow introduceert tegelijk twee nieuwe AI-specialisten die worden ingezet binnen security operations en vulnerability management. Volgens het bedrijf kunnen deze specialisten zelfstandig phishingincidenten afhandelen en openstaande kwetsbaarheden oppakken. Daarmee beoogt ServiceNow de operationele belasting bij securityteams te verlagen en de responstijd te verkorten.

Het bericht ServiceNow integreert Armis en Veza in Autonomous Security & Risk verscheen eerst op MSP Business.

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How are enterprises using cloud today?

news-aggregator — Fri, 29 May 2026 09:37:05 +0000

Over the past decade and a half, cloud computing has become a foundational technology. It started as a way to rent servers but has evolved into a complex ecosystem that supports everything from basic infrastructure shifts to transformative AI initiatives. Having advised enterprises on thousands of cloud projects over the years, I have seen that most projects fall into a handful of categories. I can say with certainty that success depends less on hype and more on understanding each project’s nature, risks, costs, and lessons.

Cloud migrations

Enterprises continue to migrate existing workloads from data centers to public, private, or hybrid environments. This can involve rehosting (lift and shift), replatforming with minor changes, or full refactoring into cloud-native architectures. The goal is usually cost reduction, scalability, or the end of hardware refresh cycles. The risks here are well documented. Many projects underestimate dependencies, leading to performance surprises or integration failures. Data egress fees and unexpected operational costs can wipe out projected savings.

Cost profiles vary widely. Initial migrations often run 20% to 50% over budget due to discovery gaps and testing. Ongoing expenses can decline through rightsizing and reserved instances, but poor management often leads to 25% to 35% waste from idle resources. These lessons underscore the importance of modeling the total cost of ownership up front, including people, training, and change management.

What we’ve learned: Pure lift-and-shift rarely delivers the promised ROI. Organizations that succeed treat migration as an opportunity for modernization rather than a simple move. Phased approaches with strong governance and finops practices minimize overruns, which have historically plagued most efforts.

Cloud-native applications

Teams build microservices, serverless functions, or containerized apps on platforms such as Kubernetes, AWS Lambda, or Azure Functions. This approach leverages elasticity, devops pipelines, and managed services to accelerate time to market.

Risks focus on architectural complexity and skills gaps. Overengineering with too many microservices creates operational nightmares, while underengineering leads to unscalable monoliths. Distributed systems need constant security vigilance. New apps often begin well but gain technical debt when teams prioritize features over observability and resilience. Entry costs are usage-based, which sounds attractive, but they often spike at scale due to poor design.

What we’ve learned: Based on my years of observation, successful teams embed cost awareness in CI/CD, use spot instances strategically, and design for observability from day one. Cloud-native development accelerates innovation when paired with disciplined architecture.

Business analytics projects

Enterprises are moving data lakes, data warehouses, and ETL processes to services such as Snowflake, BigQuery, or Redshift. Real-time analytics, dashboards, and predictive modeling become possible at scale. The primary risks are data gravity and quality issues. Moving petabytes is expensive and complex, while poor governance leads to compliance headaches or “garbage in, garbage out” results. Integrating with legacy systems often delays the realization of value.

What we’ve learned: Fifteen years later, we know that centralized data strategies outperform fragmented ones but only when paired with strong data mesh or data fabric approaches that respect domain ownership. Cost profiles include storage, compute for queries, and egress. Optimization through partitioning and materialized views pays off, but many organizations waste money on unused data. Lessons emphasize starting small with high-value use cases and building governance early rather than bolting it on later.

Artificial intelligence projects

Artificial intelligence and machine learning projects represent the current frontier of cloud. This includes training models, deploying inference endpoints, and integrating ML into applications. Managed services lower barriers, but custom needs often require GPU clusters or specialized hardware. Risks are significant: model drift, explainability issues, high compute demands, and ethical concerns. Many projects stall after the proof of concept because production deployment exposes scalability or cost issues. Managed AI offerings from providers help, but enterprises still struggle to integrate them into core business processes.

Costs run high, especially for training. Inference can be optimized, but it often dominates bills. What we have learned is that AI succeeds when treated as part of a broader cloud-native architecture, not as a standalone science project. Hybrid approaches and cost controls are essential.

Generative AI projects focus on large language models, image generation, code assistants, and custom agents using services like Bedrock, OpenAI integrations, or fine-tuned open source models. Enterprises are experimenting with retrieval-augmented generation for grounded responses and agentic workflows. Risks include hallucinations, data privacy leaks, intellectual property issues, and runaway token costs. Many early adopters built impressive demos only to face governance and compliance walls in production.

What we’ve learned: After observing the wave, the lessons are clear. Start with narrow, high-value use cases and layer in strong prompting, evaluation, and human oversight frameworks. Cost profiles are usage-driven and can escalate quickly with volume. Optimization through caching, smaller models, and hybrid on-prem inference helps. Generative AI delivers ROI fastest when embedded in existing workflows rather than used as standalone tools.

Other project types

Modernization of legacy mainframe or monolithic applications falls between migration and new development. Internet of Things (IoT) initiatives use the cloud for device management and edge analytics. Disaster recovery and backup projects leverage the cloud to improve resilience. Edge computing projects move processing closer to users or devices. Compliance-focused sovereign cloud deployments address data residency requirements. Finally, sustainability initiatives focus on reducing carbon footprints by implementing efficient architectures.

What we’ve learned: Each approach carries tailored risks and cost dynamics. Modernization often uncovers hidden dependencies. IoT requires reliable connectivity. Edge computing introduces latency considerations. Lessons across all types highlight the value of multicloud strategies for negotiation leverage and risk diversification, though they increase complexity.

Common themes

Most projects do not fail because of technology itself but from inadequate planning, cultural resistance, or neglect of operational realities. Cost overruns are often caused by the absence of strict finops discipline. Security and compliance issues remain ongoing and require integrated design considerations. Skills shortages hinder progress, which makes managed services appealing despite concerns about vendor lock-in.

Successful cloud stories share common traits: strong executive sponsorship, iterative delivery, cross-functional teams, and continuous optimization. Enterprises that treat the cloud as a business transformation rather than an IT project perform best. They measure outcomes using business metrics, such as revenue impact, customer satisfaction, and speed to market—not just uptime or instance counts.

The cloud landscape continues to evolve as capacity markets, neoclouds, and AI-driven operations offer new options. Yet cloud fundamentals endure. Choose the right project type for your cloud maturity and goals. Understand risks thoroughly. Model costs realistically. Apply lessons from the thousands of cloud deployments that came before.

My advice sounds simple, but it will determine which cloud projects and enterprises will thrive in the next decade of cloud computing. Those who chase hype without discipline will only become another cautionary tale.

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u-blox ALMA-B2 Bluetooth 6.0 and 802.15.4 module features Nordic nRF54LM20 Edge AI wireless MCU

news-aggregator — Fri, 29 May 2026 07:37:04 +0000

u-blox has recently announced the ALMA-B2 standalone BLE 6.0 and 802.15.4 module family built around the Nordic Semi nRF54LM20 Cortex-M33 wireless microcontroller with a dedicated NPU for low-latency Edge AI applications. There are four specific product variants in the u-blox ALMA-B2 series: ALMA-B201, ALMA-B206, ALMA-B211, and ALMA-B216, all of which support Bluetooth 6.0 and Bluetooth Channel Sounding for distance measurement. They also support IEEE 802.15.4, including Thread, Zigbee, and Matter, as well as Nordic’s proprietary 2.4 GHz protocol and NFC. The company also mentions that the nRF54LM20B-based ALMA-B211 and ALMA-B216 variants include an Axon NPU that performs machine learning tasks up to 15 times faster and with greater energy efficiency than running the same tasks on the main processor alone. u-blox ALMA-B2 Series Specifications: Wireless SoC – Nordic Semiconductor nRF54LM20A (for ALMA-B201 and B206) or nRF54LM20B (for ALMA-B211 and B216) CPU Arm Cortex-M33 application processor clocked at up to 128 MHz RISC-V […]

The post u-blox ALMA-B2 Bluetooth 6.0 and 802.15.4 module features Nordic nRF54LM20 Edge AI wireless MCU appeared first on CNX Software – Embedded Systems News.

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AMD launches Versal Prime Gen 2 2VM3454, 2VM3254, and 2VM3104 adaptive SoCs in compact 23x23mm packages

news-aggregator — Fri, 29 May 2026 00:37:06 +0000

AMD has added three new chips to its Versal Prime Series Gen 2 lineup — the Versal 2VM3454, 2VM3254, and 2VM3104. Designed for space-constrained applications like Pro AV, broadcast, and industrial IoT, these new devices deliver up to 100K DMIPS of scalar compute in packages as small as 23 x 23 mm. AMD started shipping the first production units of the Versal Prime Gen2 Series with the 2VM3858 device late last year. The 2VM3558 has since entered full production, while the 2VM3358 is currently sampling. These new devices are designed to provide an optimized footprint and processing subsystem compared to the earlier models. Despite the reduction in core count, AMD claims these devices can deliver up to 5x the scalar compute performance compared to existing AMD adaptive SoCs. AMD Versal Prime Series Gen 2 (2VM3104, 2VM3254, and 2VM3454) specifications: Processor Subsystem (PS) APU – Quad-core Arm Cortex-A78AE with 64 KB […]

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Industrial-grade vision inspection, made accessible by the Arduino® UNO™ Q board

news-aggregator — Thu, 28 May 2026 15:07:05 +0000

Factory lighting can be brutal. A label looks perfect from one angle and unreadable from another. A reflective pouch catches glare. A conveyor casts shadows. A package edge disappears under mixed LED lighting.

Traditional industrial vision systems solve these very real problems, and that’s why they became expensive. However, many inspection tasks don’t require a closed, high-cost smart camera. They just need a reliable prototype path: collect images, train a model, deploy locally, trigger an action, and improve.

On UNO Q, the Linux side of the board can run the camera pipeline, OpenCV preprocessing, an Edge Impulse object-detection or classification model, and a local web dashboard. Meanwhile, the MCU side can handle encoder pulses, trigger timing, stack-light outputs, and reject-actuator logic. You can already browse Arduino® Project Hub for a variety of practical vision examples that combine UNO Q with Edge Impulse models. We highly recommend the one for a robot arm that recognizes people and offers gadgets through intuitive interactions, and the one for OCR (optical character recognition) with a two-stage text detection and recognition pipeline running locally with Arduino® App Lab, plus image classification examples using a USB webcam and Edge Impulse Linux runner.

Real-world industrial applications are within reach

Imagine the following setup: a small conveyor rig with an overhead camera pointed at the product as it passes through an end-of-line station. A quantized model running locally detects pass/fail – checking for the right label, a properly seated connector, a sealed cap, or a surface defect – with inference times under 50 ms. The microprocessor running Debian hosts the dashboard made with Python and logs every result; the MCU triggers the operator’s alert system without waiting for a round trip to the cloud. No frames leave the board, no proprietary software license is required, and the same fixture can be reconfigured for a different product without rearchitecting the system from scratch. Sound like a dream? Nope, it’s real: just check up the setup IDT Solution validated in their open-architecture AOI proof of concept for automotive end-of-line inspection.

Want to learn even more? You can also use the UNO Q to run a defect classification model, such as a missing label, wrong color, missing cap, or damaged package. Train the first model in Edge Impulse. Deploy through Arduino App Lab. Run the application as a Debian service or Arduino App Lab app. Use the MCU for deterministic reject timing.

UNO Q turns vision into action

UNO Q has the potential to become the leading SBC for its price and power category, because of the real value it offers.

1. Industrial vision without industrial pricing. Build credible inspection prototypes without committing to proprietary smart-camera systems.

2. Better inspection under real lighting. Use multiple camera views, local preprocessing, and optimized vision models to improve robustness under glare, shadow, and reflective surfaces.

3. AI plus deterministic action. Run inference on Linux; trigger conveyors, lights, and reject mechanisms through the MCU.

The real promise of UNO Q is not just that it can run a vision model. It is that it can turn vision into action.

A traditional camera can capture an image. A cloud model can classify it later. But an industrial inspection system needs more than recognition. It needs timing, reliability, local decision-making, and a way to respond immediately when something is wrong.

Edge AI for machine vision: from concept to working prototype

By combining Debian Linux, Edge Impulse, local AI inference, and deterministic MCU control, developers can build inspection systems that see the product, understand the defect, log the result, and trigger a physical response – all at the edge.

This means a faster path from concept to working prototype. For developers, it means open tools, flexible deployment, and real-world control. For manufacturers, it means machine vision can move beyond expensive, closed systems and become something more accessible, adaptable, and scalable.

That is how industrial vision becomes practical, repeatable, and affordable – and it is exactly the kind of edge AI workflow UNO Q was built to unlock.

Ready to build your first AI camera inspection system? Explore UNO Q and start prototyping real-world inspection systems today.

Arduino, UNO and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

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Supply chain battles intensify as takedowns meet AI-driven noise

news-aggregator — Thu, 28 May 2026 12:37:12 +0000

Taking down a sprawling malware operation once signaled progress in securing the open-source ecosystem. Now, it barely registers. The GlassWorm campaign disruption comes at a moment when attackers can quickly reconstitute, and defenders are increasingly grappling with a new challenge: distinguishing real threats from automated noise.

“I think coordinated actions, like GlassWorm, can sever control, significantly increase attacker costs, buy time for remediation, and signal the possibility of a fightback,” said Agnidipta Sarkar, chief evangelist at ColorTokens. “But most takedowns are temporary actions in a long fight.”

The CrowdStrike-led takedown, conducted alongside Google and the Shadowserver Foundation, disrupted infrastructure linked to the campaign that had poisoned hundreds of repositories with malicious packages targeting developers.

A day after the takedown, in an independent development, the OSV database withdrew 157 malware reports after maintainers determined the submissions were likely automated false positives.

Takedowns help, but analysts question long-term impact

The takedown happened on May 26, at 14:00 UTC, with CrowdStrike confirming the operation to have struck down “all four of GlassWorm’s command-and-control (C2) channels simultaneously”. This reportedly helped sever the botnet operators from their infected machines, blocking them from pushing out new malware.

CrowdStrike described the GlassWorm operation as targeting infrastructure used to distribute malware through developer-focused repositories, an increasingly popular attack vector as adversaries chase CI/CD access, developer credentials, and downstream enterprise environments.

GlassWorm was a cross-platform operation affecting Windows, macOS, and Linux systems, with trojanized VSCode extensions and compromised npm and Python packages for information and credential harvesting.

“As part of our disruption efforts, we are working with partners to bring more pain to attackers, especially when we see them abusing our products or targeting our users,” said Google Threat Intelligence Group’s (GTIG) chief analyst, John Hultquist, in an X post.

Still, the broader economics of repository abuse remain unchanged. Open-source ecosystems continue to offer attackers low-cost distribution, massive reach, and relatively weak identity verification compared to traditional software distribution channels. That means operators behind campaigns like GlassWorm can often reappear quickly under new accounts, domains, or package names.

“It is disruption, not eradication,” Sarkar warned. “To build resilience after a takedown, defenders should prioritize rapid post-takedown scanning to detect the reemergence of malicious artifacts across related repositories and distribution platforms.”

They should then establish granular micro-perimeters, build capabilities to contain propagation across workloads, endpoints, IT/OT/IoT/cloud assets, and limit the blast radius of supply-chain compromises (e.g., a poisoned npm package or a GitHub workflow stealing creds can’t easily pivot).

Sarkar advised developers and organizations to establish “granular micro-perimeters,” build capabilities to contain propagation across workloads, and limit the blast radius of supply-chain compromises.

AI False positives are becoming part of the supply chain problem

If GlassWorm highlights the persistence of real malware campaigns, the OSV withdrawal incident exposed a parallel issue affecting the open-source software (OSS) supply chain. It is the growing reliability surrounding automated security reporting.

The withdrawal of 157 malware reports believed to be AI-generated false positives matters, especially when it includes packages like FastAPI v0.136.3. FastAPI is a heavily adopted Python framework powering production APIs, AI services, and cloud-native applications across industries. Even a few days of false flagging can trigger costly deployment delays, CI/CD disruptions, and hours of development time in isolating legitimate software.

“I would recommend that enterprises be concerned enough about signal-to-noise problems to consider remedial measures, as automation erodes trust in defensive tools,” Sarkar said. “Unless you have a highly microsegmented enterprise, noise wastes analyst time, slows velocity, and risks missing sophisticated attacks amid fatigue.”

In 2026, with AI-assisted malware and reporting both accelerating and rising false positives in SAST/SCA tools, defensive automation is getting asymmetrically compounded by supply-chain volume, he noted.

In a blog post, Socket called bad OSV records particularly dangerous as the popular database gets rapidly carried through dependency scanners, CI checks, registry controls, SBOM tools, dashboards, and internal policy systems.

All hope is not lost, though, as newer tools promise lower reliance on AI for hunting dependency vulnerabilities. CVE Lite CLI, a light-weight, JavaScript and TypeScript dependency vulnerability scanner, is offering developers a way to know dependency risks while they are still writing code, much earlier than failing automated scanners in CI pipelines.

The article originally appeared on CSO.

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Gesture HW1 is a 10-DOF ESP32-S3 robotic hand with high-dexterity manipulation (Crowdfunding)

news-aggregator — Thu, 28 May 2026 08:37:05 +0000

The HW1 by Gesture Platforms is a 10-degree-of-freedom (DOF) high-dexterity robotic hand and wrist built around an ESP32-S3 wireless MCU. It’s primarily designed for researchers, educators, and hobbyists; it bridges the gap between basic DIY robotic hands and expensive industrial models. The device weighs just around 500 grams but can handle a 1kg dynamic load and a 3kg static load. It communicates via USB-C or Bluetooth 5.0 and comes with a companion desktop app as well as Python and C++ SDKs for custom development. Gesture HW1 specifications: Microcontroller – Espressif Systems ESP32-S3 dual-core LX7 microprocessor with WiFi 4 and Bluetooth 5.0 LE connectivity Degrees of Freedom (active) – 10 DOF Finger Flexion – 0° – 90° (at each joint) Finger Adduction/Abduction – -10° – 40° Thumb Flexion/Extension – 0° – 120° Thumb Adduction/Abduction – 20° – 90° Thumb Distal Flexion – 0° – 90° Wrist Flexion/Extension – -50° – 50° Wrist […]

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Iran’s Internet is partially restored, Cloudflare Radar data shows

news-aggregator — Wed, 27 May 2026 18:37:05 +0000

On Tuesday, May 26, Iran’s vice president announced that Internet access had started to be restored in the country after being cut off almost three months ago, following the launch of U.S. and Israeli attacks on February 28.

Cloudflare Radar data confirms increased activity and indicates a partial restoration of the Internet in Iran. In this blog post, we’ll examine a range of data points that provide a lens into this prolonged shutdown – and the signs that Iran’s citizens are increasingly able to connect once again. As the situation continues to unfold, Radar will have the latest data on Iran’s connectivity.

The first shutdown

Iranian citizens have experienced two national Internet shutdowns this year. The first began on January 8 around 16:30 UTC (20:00 local time), and we explored the impact seen over the first few days in a blog post. Traffic from Iran remained near zero until January 21, when a small amount of traffic returned, only to disappear a little over 24 hours later. A similar brief restoration also occurred on January 25, before traffic recovered more fully beginning on January 27.

The second shutdown

In late February, as military strikes on Iran escalated, a second nationwide Internet shutdown began. That sweeping shutdown has persisted for nearly three months.

The shutdown began on February 28. On that date, Cloudflare Radar observed a sharp drop in traffic from Iran beginning around 10:30 local time (07:00 UTC). Traffic levels fell to well under 1% of previous levels, with only small amounts of Web and DNS traffic leaving the country.

Activity on May 26

Our observations indicate that more traffic is now finally able to get through. Starting at around 11:00 UTC on May 26, 87 days after the second shutdown started, Cloudflare Radar observed a marked increase in both traffic and DNS queries.

Traffic increase

Data for bytes transferred across Cloudflare’s network shows a brief spike at 11:45 UTC, followed by a steady increase starting at 12:00 UTC. This surge in activity is roughly 15x than the levels observed during the prior week. Following expected diurnal patterns, the traffic starts declining around 21:00 UTC, followed by an increase starting at May 27 3:00 UTC (6:30 local time).

An increase in bytes transferred shows that a higher volume of data is successfully moving across Cloudflare’s network, which is a hopeful signal that a partial restoration is underway.

Traffic volume by region

Cloudflare Radar’s regional breakdowns, shown below, indicate that the vast majority of this new traffic is localized to Tehran, with 91.6% of HTTP requests originating from the capital city. While other regions show minor increases, they are not nearly as significant.

Traffic volume by network

Following an initial burst at 11:45 UTC, Internet providers TCI, IranCell, RighTel and MCCI each saw increases in traffic. Cloudflare Radar measures this traffic by ASN, the unique identifier assigned to an individual network or group of networks.

DNS query increase

As shown in the graph below, queries to Cloudflare’s public DNS resolver (1.1.1.1) have also spiked. Because an increase in DNS traffic indicates that more users are requesting websites and services, this upward trend serves as a strong indicator that online access is returning.

Traffic has returned to 40% of previous levels

These increases in traffic validates that a partial restoration of Iran’s Internet has taken place. However, though these increases in DNS queries and traffic are significant, they remain well below what we observed prior to either disruption. As shown in the graph below, at its peak on May 26, traffic had only returned to 40% of the maximum amount of activity observed so far in 2026.

Network activity over the coming days will reveal whether traffic levels will successfully return to their pre-shutdown baselines. It should also be noted, however, that these changes could be temporary; as demonstrated in January, brief periods of recovery can quickly reverse.

IPv6 remains impacted

In January, we reported a near-complete loss of announced IPv6 address space that began several hours before the January 8 traffic drop. While a partial restoration of the country’s networks appears to be underway, the volume of announced IPv6 address space — and thus IPv6 traffic from Iran — remains effectively zero.

This is noteworthy because in contrast with IPv6, address space announcements for IPv4 have remained fairly consistent and stable throughout both major 2026 shutdowns in Iran. The fact that IPv4 addresses were not removed from global routing tables, combined with the complete loss of actual traffic, suggests that Iran’s shutdown was achieved through other technical means such as application filtering or whitelisting.

^{IPV6 address space dropped precipitously in January and has not returned to normal levels.}

^{Amid both 2026 shutdowns, IPv4 address space has stayed relatively consistent.}

Conclusion

Having spent the majority of 2026 offline, Iranian citizens face severe disruptions to their daily lives, making these early signs of traffic recovery a critical turning point. We will continue to closely monitor Internet connectivity in Iran and share our findings as the situation evolves.

You can explore more data on Internet connectivity in Iran and the rest of the world at Cloudflare Radar. And you can find our latest observations on X, Mastodon, and Bluesky.

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LichtBit’s open-source ESP32 Art-Net/sACN NeoPixels controller can drive up to 2,720 RGB LEDs

news-aggregator — Wed, 27 May 2026 10:37:05 +0000

Dutch hardware designer LichtBit has launched a fully open-source ESP32-based Art-Net/sACN NeoPixels LED strip controller designed for large-scale lighting installations and custom stage design. Built around an ESP32, the hardware routes lighting data over wired Ethernet or Wi-Fi to manage up to 16 universes of addressable LEDs across 4 dedicated outputs. We have previously written about various NeoPixel LED controllers, such as the xcrhom WLED Type-C, the Adafruit Sparkle Motion Stick, the full-featured Adafruit Sparkle Motion, and others, which are designed for portable lighting setups and standalone animations. But LichtBit’s board targets the professional event industry by bridging the gap between commercial live-performance software (such as Resolume Arena, MadMapper, or xLights) and low-cost digital LED strips. LichtBit’s Art-Net and sACN LED controller specifications: Development Board – Generic ESP32 based developmt board with ESP32-WROOM-32 MCU Display – 128×32 I2C OLED display for real-time device configurations, status, and IP mapping Network 10/100Mbps Ethernet via WIZnet W5500 […]

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PolyCast5 – An ESP32-C5 multi-tool remote with dual-band WiFi 6, BLE, ESP-NOW, LoRa, and Infrared Tx/Rx (Crowdfunding)

news-aggregator — Tue, 26 May 2026 13:37:04 +0000

PolyCast5 is a portable, hackable ESP32-C5-based multi-tool remote to control devices through five different core wireless technologies: WiFi 6, Bluetooth LE, ESP-NOW, LoRa, and infrared Tx/Rx. The all-in-one controller can be used for cybersecurity work, a standard IR learning remote control, a voice-enabled password manager, a robotic arm controller, an AI keyboard using the built-in microphone and Bluetooth connectivity, a long-range LoRa remote control, DIY electronics projects through a 4-pin GPIO header, and more. PolyCast5 specifications: Wireless module – ESP32-C5-WROOM-1 SoC – ESP32-C5 CPU Single-core 32-bit RISC-V processor @ up to 240 MHz Low-power RISC-V core @ 40 MHz acting as the main processor for power-sensitive applications Memory – 384 KB SRAM on-chip, 8MB PSRAM Storage – 320 KB ROM Wireless Connectivity Dual-band (2.4GHz/5 GHz) 802.11ax WiFi 6, with 802.11b/g/n WiFi 4 standard fallback Bluetooth 5.0 Low Energy (LE) 802.15.4 radio for Zigbee 3.0 and Thread 1.3 (note: support not […]

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