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With all the talk of superchips driving AI inference in the data center and the likes of the Spark RTX architecture bringing Windows AI capabilities to unprecedented heights, it’s easy to overlook how much room there is for improvement in some of the most challenging device classes when it comes to power management: IoT and wearables.
For products like environmental sensors that may have to relay information from remote areas, a battery life measured in years can be essential since. On the other hand, small wearables such as rings, watches, and pendants require smaller batteries while their biomarker monitoring functions
Two of the largest SoC vendors, however, have taken aim at one of these markets with major upgrades to their product lines aimed at them.
Genio gears up for physical AIMediatek has long targeted the IoT market, which includes a mélange of applications such as digital signage, smart city sensors, retail, and industrial robotics. The last of those is increasingly turning to physical AI, where the intelligence of the machines can match their ability to lift and move items.
The company seeks to accelerate the market with its new Genio Pro 5100 IoT chip, which is built on a state-of-the-art 3 nm process, combines CPU performance that rivals that of Intel’s Core chips, ARM’s Immortalis GPU architecture, and a powerful NPU.
With many IoT applications focused on analyzing images, the chip can support up to 16 full HD camera inputs or or two 4K inputs (both at 30 frames per second) as well as Vision Langauge Models optimized for the platform.
Robotics is one of the fastest growing applications for these kinds of chips. MediaTek is supporting both Linux and the ROS (Robotics Operating System) for applications in factory automation, transportation and logistics, and hospitality and intends to support the line for seven to 12 years.
While the Genio Pro doesn’t integrate wireless connectivity, modules are available for Wi-Fi and Bluetooth as well as cellular, including low-power 5G RedCap.
A new breed of wearablesQualcomm has been active in the wearables market for years (and even once released its own smartwatch called Toq, although that was intended more to highlight its since-abandoned Mirasol display technology).
Variants of its W5 chip have powered the last few generations of Pixel watches. The company’s new Snapdragon Elite represents a significant jump in performance from the previous W5, which, like MediaTek’s current-generation Genio IoT chip, will stay in the market for more cost-sensitive devices.
The new SoC, which like the Genio Pro is based on a 3 nm process node, boasts improvements in four main areas: micropower connectivity, low-power islands, performance, and user experience. With wearables expected to instantly apply AI processing to inputs that surround us, Qualcomm has implemented a dual-NPU architecture.
The main NPU can handle the same level of token processing as a two-year-old smartphone (about 18 to 20 tokens per second for a roughly 1-billion parameter model). However, a new eNPU is designed to take on tasks that require fast responses like voice activation and active noise cancellation while allowing for days of usage between charges.
(Image credit: Future / Cas Kulk)The new chip also supports Qualcomm’s micropower Wi-Fi (which uses 80% lower power than the previous generation) as well as the low-power 5G RedCap cellular standard and satellite connectivity.
This flexibility is intended to help drive the next wave of wearable applications such as health and fitness coaching, lifeblogging, on-device translation, and USB-based authentication using standards such as Aliro, a sister standard of Matter.
At Mobile World Congress, Samsung, Motorola, and Google announced support for the Snapdragon Wear Elite, with the first noting that the new chip will be used in at least one version of the Galaxy Watch 9.
However, we should see far more devices based on both company’s leaps in 2027 and beyond, bringing new levels of intelligence to products that are constantly with us and many that work invisibly on our behalf.
Having been put on Konami’s backburner for over a decade, Silent Hill’s resurgence has been nothing short of extraordinary.
A tepid start with Ascension and The Short Message was counterbalanced by the excellent Silent Hill 2 remake from Bloober Team and the homegrown Japanese horrors of Silent Hill f from NeoBards. The series seems like it’s now heading further in the right direction with the next game, Silent Hill: Townfall.
Early this month I played three hours of Silent Hill: Townfall and spoke with both Screen Burn director Jon McKellan and Konami series producer Motoi Okamoto about the game’s approach to retro tech, the Scottish setting, and cultural authenticity.
Going retro(Image credit: Konami)Silent Hill: Townfall follows a man named Simon Ordell in the fictional town of St. Amelia, Scotland. As he wakes up, he finds the entire place shrouded in fog. Slowly walking around, I eventually pick up a pocked CRTV television, which is used for both gameplay and narrative reasons. It's Townfall’s primary mechanic and star of the show. Through it, you can see old static video recordings of Simon’s mysterious partner, Zoe Ellis, who is seemingly his connection to this island.
The radio’s screen also typically shows you the next place to go, which means it’s up to you to search around town until you find the correct spot. Additionally, you can hold up the radio to see through walls to check a monster’s position. It’s a wonderful twist on a common trope. In similar games, protagonists sometimes come with a ‘radar’ sense so that players can stay stealthy and avoid enemy detection. Implementing the radar into the radio itself is not only clever, but reinforces that Simon is, after all, just human, in an unrelenting world filled with monsters.
The time period that Townfall takes place in is paramount to its horror themes. A puzzle hints that the current year is 1996, which is where the mass adoption of technologies like the internet and home dial-up started happening. The transition to an increasingly connected world filled people with anxieties. Simon’s CRTV radio is just advanced enough, but is nowhere near the cutting edge technology we see today.
“The world was on a technological cusp around the mid 90s, where the internet was starting to become a thing. But you were too early to have Google Maps in your pocket and you didn't have this magic bullet for all the problems that might come up,” McKellan explained.
He continued, “So with analog tech, you feel like you can't quite rely on it. It’s a little bit uncertain, and I think that's a great place to be for horror.”
Another theme that was prominently was medicine and healthcare. As I strolled through St. Amecia, I saw protest signs littered around the town square with phrases about “taking this town back”.
I picked up collectibles and notes about St. Amecia’s local hospital, vaguely hinting at some sort of institution providing “protection.” As an American citizen in a country with mainly for-profit healthcare, my mind immediately began trying to connect the dots and figure out the story’s direction. Scotland has a public system, but perhaps some private healthcare provider came in and tried to take over? And what about the “protection” aspect? Did Simon, or someone else, snitch?
The medical symbolism doesn’t end there. Zoe herself is a nurse, and Simon also has an IV bag strapped to his arm. With ingredients found scattered throughout town, he can craft items Resident Evil-style. These include items like blood bags for his IV, which let him resurrect with a sliver of health after being killed by an enemy.
Thankfully, Townfall’s combat feels much smoother compared to Silent Hill f’s. In the latter, there always felt like a second of awkward hit stun when your melee weapons made contact with enemies. Here, smashing planks over their heads feels satisfying. The way their bodies all of sudden go limp simultaneously gives me a feeling of surprise relief every time.
Simon also eventually gets a powerful pistol that one-hit kills many monsters, rivalling even Grace Ashcroft’s Requiem from Resident Evil Requiem. The downside is that it makes a ton of noise when fired, so enemies nearby will zero in on your location. With limited ammo, it’s definitely your last resort weapon, and it’s gratifying every time it goes off.
McKellen ensured that social commentary would be at the heart of the story, similar to how Silent Hill f’s overarching message was around gender expectations. “There are a few different threads in this game that start to coalesce as the game progresses, and I think it takes people in kind of unexpected places,” he explained. McKellen is hoping that people (like me) have an idea of what’s happening, then excitedly twist our expectations.
So why does Simon have a cannula in his hand, and why does it bring him back when he dies? They’re not just gameplay gimmicks—they represent something. McKellen adds, “There's a lot behind it, and we pride ourselves as developers that everything in the game has some relevance. Nothing is there just because or to fill a void.”
Crafting different Silent Hills(Image credit: Konami)Over the years, the setting for most of the Silent Hill games were, well, Silent Hill. But recently, Konami has expanded settings for the series to explore other areas. Silent Hill: The Short Message introduced “Silent Hill” as a phenomenon, where similar psychological and experiences occur outside of the titular town in Kettenstadt, Germany. Silent Hill f took the series back to its hometown in the fictional town of Ebisugaoka in 1960s Japan.
“Our current approach in branding is to introduce that concept and show similar types of psychological experiences and narratives that place outside of the boundaries of Silent Hill,” Okamoto said.
Screen Burn, headquartered in Glasgow, Scotland, has a wide age range within its developers. Some grew up with the older games, while others recently discovered it through the newer releases. But the big lesson that the team learned about what’s essential to Silent Hill’s DNA is the understated nature of the stories. They’re not explicit, with plenty of room for interpretation but still feeling satisfied after finishing the game.
“You can walk away from a Silent Hill game feeling like you know something, but whether you know everything or need to discuss that with someone else was one of the big things that came out,” McKellan explained. “People were saying that after every game they would end up talking to someone else about it and having different theories.”
(Image credit: Konami)The studio’s last game was the sci-fi horror adventure Observation (which is also one of my favorites that you should check out), and the team grew from the game before that, Stories Untold. Both of them had technology as a major theme to convey gameplay, with Stories Untold having players interacting with computers and Observation switching between different cameras in a space station.
You can see the theme of technology rearing its head again in Townfall through the analog CRTV, as well as the phone booth players call to manually save their progress.
Townfall is also Screen Burn’s biggest game to date. The most notable departure is the freedom that players have in exploring the town. “Townfall feels like a Screen Burn game unleashed. We’re taking our narrative sensibilities, puzzle design, and attention to detail, and blowing that out to something much larger with the support of Konami and Annapurna,” said McKellan.
“So if you've played our previous games, you'll probably see a lot of common ground there.”
The future of Silent Hill(Image credit: Konami)Looking ahead, Bloober Team’s remake of the first Silent Hill is set to come out after Townfall. Okamoto understood the worry players had about the series potentially becoming annualized, perhaps resulting in fatigue or lapse in quality. That's why Konami was open to new takes on the iconic horror franchise. The Short Message focused on social media, Silent Hill f was distinctly Japanese, and now Townfall is in Scotland.
With the rising cost of game development, it’s a wise idea to have other studios take on notable IP rather than just having your internal studios handle them. Not only does this result in more games being released, but outside talent can revitalize interest. I mean, Bloober Team and NeoBards are proof enough.
Okamoto, however, explained that this method was more about portraying cultural authenticity than anything else, and dependent on the kinds of projects Konami is working on.
“For example, the Metal Gear team has their own philosophy and approach for how they develop games,” Okamoto said. “At least from our perspective, with the goal being trying to create a wider, richer, and more culturally varied universe for Silent Hill, we feel that partnering with third-party developers works best.”
Silent Hill: Townfall launches on September 24 for PC and PlayStation 5.
AI has sparked a race to build ever more powerful infrastructure, with headlines dominated by GPUs, networking and energy demand.
But behind the scenes, another part of the technology stack is undergoing an equally significant transformation: storage.
As AI models grow larger and enterprises retain more data for training, inference and retrieval, storage is no longer simply where information resides.
It has become an active part of AI infrastructure, responsible for feeding compute efficiently, supporting vast datasets and helping organizations balance performance with cost.
That shift has fundamentally changed what primary storage looks like in modern cloud environments.
Historically, primary storage meant tightly coupled block or file systems sitting close to compute.
Today's hyperscale cloud providers have taken a different approach, treating object storage as the persistent system of record while software coordinates how data is stored, protected and accessed across distributed infrastructure.
The move to cloud computingThe move to cloud computing didn't simply increase the amount of storage organizations needed. It fundamentally changed how storage had to work.
Traditional enterprise storage was built around tightly coupled systems where applications interacted directly with file systems. That model worked well when infrastructure was relatively contained. At hyperscale, however, coordinating millions or billions of files across distributed environments introduces complexity that limits scalability.
Cloud providers responded by redesigning storage around software-defined architectures that separate how data is managed from where it is physically stored. Rather than treating storage devices as isolated resources, modern cloud platforms orchestrate entire fleets of storage through software, allowing them to scale far beyond the limits of traditional enterprise architectures.
AI has only accelerated this transition. Training large models, serving inference and retaining ever-growing datasets all place sustained demands on shared infrastructure, making software-defined storage more important than ever.
Although every hyperscale platform has evolved differently, the same architectural ideas appear time and again. Four principles in particular have become fundamental to supporting AI at scale.
1. Object storage is designed around sequential data movementTraditional cloud storage systems frequently modify files in place. At hyperscale, continually updating data across distributed environments becomes increasingly difficult to manage.
Object storage takes a different approach. Rather than overwriting existing data, new versions are typically written as separate objects. That naturally favors large, sequential data flows, allowing storage systems to operate more efficiently as datasets continue to grow.
As AI workloads generate larger datasets, frequent checkpointing and continuous data movement, designing storage around sequential throughput becomes increasingly important.
2. Metadata has become a software challengeAs organizations store billions or even trillions of objects, tracking where everything lives becomes just as important as storing the data itself.
Rather than asking storage devices to manage both data and metadata, cloud platforms increasingly separate these responsibilities. Dedicated software layers handle object locations, namespaces and system coordination while storage media focuses on delivering scalable capacity.
Separating these functions makes infrastructure easier to scale while allowing storage systems to concentrate on what they do best: storing vast amounts of data efficiently.
3. Resilience comes from architecture, not individual devicesCloud providers assume that, somewhere across thousands of servers and storage devices, failures will happen. Rather than relying on individual hardware to eliminate every fault, modern architectures distribute data intelligently across large storage pools.
Techniques such as erasure coding allow platforms to recover data efficiently while maintaining availability and reducing the overhead associated with traditional replication strategies.
The result is infrastructure that remains resilient even as AI workloads continue to grow in size and complexity.
4. Intelligent data staging protects performanceAI rarely produces smooth, predictable storage workloads. Training jobs, inference requests and application traffic often arrive in bursts, placing sudden pressure on infrastructure.
Rather than writing every request directly to capacity storage, cloud platforms increasingly use flash and memory as staging layers. These absorb incoming traffic before organizing data into larger, more efficient writes.
This allows organizations to maintain responsive applications while making better use of high-capacity storage as AI workloads become increasingly demanding.
Software has become the defining layerTaken together, these four principles point to a broader industry shift. Storage performance is no longer determined solely by faster hardware. Increasingly, it is software that decides how efficiently infrastructure performs by orchestrating data movement, coordinating metadata and balancing workloads across distributed systems.
That evolution has fundamentally changed the role of primary storage. Rather than serving individual applications or servers, storage increasingly underpins shared object platforms that support analytics, cloud services and AI workloads simultaneously.
In other words, primary storage is no longer defined simply by where data resides. It is defined by how effectively software enables that data to move, scale and remain available across distributed infrastructure.
A new definition of primary storageThe rise of AI has accelerated changes that cloud providers have been making for years. Primary storage is no longer defined by proximity to compute or by storage hardware alone. Increasingly, it is software-managed, globally distributed and designed to balance performance, resilience and economics at enormous scale.
For organizations investing in AI, storage decisions can no longer focus purely on capacity or latency. Understanding how software, data movement and storage media work together has become just as important.
The organizations that succeed won't necessarily be those deploying the most hardware. They'll be those that build storage architectures capable of feeding AI workloads efficiently while keeping infrastructure scalable, resilient and economically sustainable.
In modern cloud environments, primary storage is no longer simply where data lives. It has become one of the technologies that determines how effectively AI can scale.
We've reviewed, rated, and ranked the best business cloud storage.
This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit
A relative newcomer in the industry, DataImpulse has grabbed a slice of the proxy pie thanks to its wallet-friendly, high-performing service. From more than 90 million ethically-sourced residential IPs across 195 locations to mobile and datacenter proxies, users gain access to a speedy and reliable global network.
The company specializes in creating custom proxy services for businesses, though individual users who aim to access data from anywhere and collect the information they need will have no problem finding something for themselves.
Arguably, the biggest selling point is a highly competitive, pay-as-you-go pricing model with traffic that never expires. DataImpulse throws in free country-level targeting for every plan (more granular targeting options are also available), and full support for HTTP, HTTPS, and SOCKS5 proxy protocols across all of its proxy networks.
Plans and PricingDataImpulse operates on a transparent pay-as-you-go model where traffic never expires. In case you’re unfamiliar with the concept, the bandwidth you buy here is yours until your scrapers actually consume the bytes, whether that takes days or months.
The price structure is based on the type of proxy and the amount of traffic.
For instance, residential proxies start at a flat $1 per GB for a 5GB starter block, and go up to 50GB at the same dollar-to-gigabyte ratio. If you ramp up your scraping to the Advanced tier, the cost drops down to $0.80 per GB for an 800GB package, with custom enterprise pricing scaling down even further for terabyte-scale deployments - though you’ll have to pony up at least $4000 for the option.
Datacenter proxies start at a mere $0.50 per GB for new users, slipping down to $0.45 per GB when upgrading to a larger $450 volume pool. Once again, you can get a custom price per gigabyte.
Gaining access to mobile proxies kicks off at $2 per GB for the entry-level 5GB option. The price goes down to $1.6 per GB for bulk operations, with custom pricing available for truly massive projects.
Finally, premium residential proxies will set you back $5 per GB via 1GB and 10GB plans. And yes, there is a custom option too with a $20k starting point.
It’s worth mentioning that DataImpulse doesn’t play favorites with its plans. Each includes free country-level geo-targeting, rotating and sticky sessions, HTTP(S) and SOCKS5 support, full programmatic API access, 24/7 support, and unrestricted concurrent threads. There are no overage penalties or hidden connection fees, which means your infrastructure bill will match your actual code execution down to a T.
FeaturesIf you decide to give DataImpulse a go, your first step will be to register, either the old-fashioned way or through your Google, GitHub, or LinkedIn profile. A classic dashboard layout will greet you, where all the important stuff is located in the left sidebar.
(Image credit: DataImpulse )The usage chart and details will be your starting point. Once you settle on a plan, you can begin with the proxy configuration by setting the targeting options down to the ASN (Autonomous System Number) and IP rotation interval, which you can save altogether as a dedicated configuration.
You have to choose between a sticky or rotating proxy, as well as the protocol in charge. Sticky proxies are port-based (IP addresses are bound to a specific port for a specific timeframe), and the rotation interval can range from 1 to 120 minutes. Rotating proxies change the IP address automatically with each new request.
From here, it’s also possible to top up your balance by choosing the number of GBs you want to add in the event you need more traffic.
Here’s a more detailed look at DataImpulse’s offering:
Residential proxiesArguably the workhorse of the platform, the standard residential proxy pool provides on-demand access to a sizable footprint of over 90 million active IP addresses across 195 countries. DataImpulse proudly markets its pool as entirely ethically sourced, which means all nodes join the network legally and transparently.
As a result, the IPs maintain a pristine reputation score and are drastically less likely to be blacklisted by major CDN platforms. In other words, when your script routes data through these nodes, target web servers view the connection as an ordinary home user browsing a site, rather than an automated bot. In our tests, residential proxies delivered a consistently high scraping success rate.
Premium residential proxiesDataImpulse labels these as ultra-responsive IP addresses with minimal latency that deliver better reliability and security compared to regular residential proxies. So, you should see faster connection speeds and more precise geo-targeting, especially considering you get full customization options to tweak your scripts.
Moreover, upgrading to the premium residential line significantly reduces the risk of encountering unexpected access blocks or sudden CAPTCHAs. Since this is a premium offering, it includes additional operational perks, such as a dedicated account manager to help optimize your efforts and resolve any doubts or issues.
Datacenter proxiesWhen targeting open public directories or basic landing pages (basically, anything that isn’t buffed up with sophisticated anti-bot frameworks), residential IPs can be an expensive overkill. To that extent, DataImpulse offers 20 million datacenter IPs in 195+ countries to tide you over in a cost-efficient manner.
With a response time of less than 100 milliseconds and the fact that these IPs connect directly to high-speed enterprise data systems, this tier is built for pure speed and instances where you need to process thousands of requests per second. You can also configure your IP address to be active for up to 30 minutes.
Mobile proxiesFor the most challenging anti-bot systems, like social networks or review platforms, DataImpulse provides rotating and sticky mobile proxies. Pulling from a pool of over 16 million real 3G, 4G, 5G, and LTE cellular towers across 195 locations, these carry a distinct security advantage due to the same public-facing IP to thousands of mobile devices simultaneously.
Hence, web firewalls seldom block a mobile IP address outright since doing so would mean blocking hundreds of legitimate human users on that cell tower network. The largest pool of DataImpulse’s mobile IPs is in India (1.57 million), Saudi Arabia (444k), Italy (257k), Morocco (239k), while the United States is covered with 129k IPs. You can have a single address remain active for up to two hours.
Traffic that never expiresPerhaps the defining USP separating DataImpulse from a chunk of its competitors is the fact that your purchased pay-as-you-go traffic never expires.
In most cases, you’ll wind up with monthly billing subscriptions or restrictions on how long your traffic is valid for. If you have unused bandwidth, it’s gone for good. With DataImpulse, it remains completely active until your crawlers actively consume the bytes. Having peace of mind that you can use data as long as you need without worrying about losing access makes the model a great fit for small businesses and dev teams with limited budgetary and operational width.
ScrapingUnlike some of its peers, DataImpulse leans heavily into a developer-first, DIY network architecture. This means there is no web scraping API, as the vendor’s focus is solely on providing raw proxy connections, rather than managed scraping tools. You must write your own code to manage requests, parse the HTML, handle retries, and bypass CAPTCHAs.
On the plus side, such a model brings financial and structural flexibility for teams writing custom data collectors. By routing scripts through DataImpulse’s proxy layer, they can plug the proxies directly into any automation framework or headless browser engine using basic credentials or token structures.
(Image credit: DataImpulse)In that regard, DataImpulse provides a Gateway API to programmatically manage, authenticate, and automate the provisioning of their global proxy pools. From there, you can dynamically generate proxy lists, rotate IP addresses, track bandwidth consumption and request volumes, programmatically set advanced targeting filters, and more.
There are also comprehensive integration blueprints for popular developer frameworks and third-party apps. Some of these include Scrapy, Puppeteer, Selenium, Shadowrocket, Multilogin, AdsPower, Playwright, and Zapier, to name a few. The platform also provides short code snippets for Python, Node.js, PHP, C#, Go, Ruby, and cURL to set up proxies. All of the above is neatly documented with built-in support and code examples.
Advanced geo-targeting and session controlsCountry-level geo-targeting across all 195 covered regions is built natively into the base $1/GB price floor with zero add-on activation fees. For more accurate data parsing, there are State, City, ZIP code, and ASN filter toggles.
When it comes to rotating sessions, the DataImpulse gateway dynamically inserts a randomized IP address on every network request your scraper submits. For standard HTTP/HTTPS traffic, you simply point your scripts to gw.dataimpulse.com on port 823. In case your stack relies on the more robust SOCKS5 protocol to handle raw TCP tunnels or high-performance scraping, you use port 824.
For situations where your code requires session continuity (like managing a social profile or handling account-tied logins), you can bind your traffic to a dedicated port range anywhere from port 10000 through 20000. Doing so locks a single IP address to your active thread for up to 120 minutes, though the system gracefully defaults to a 30-minute window if no interval is set.
Ease of UseThere’s not much to say here. Most importantly, the dashboard allows you to move effortlessly around via a single-page command center tailored for fast deployment. That said, the arrangement of certain sections could be better.
For starters, the section ‘Resources’ that features the onboarding guide is tucked away in the upper right corner of your respective plan, instead of being the first thing you see. The same place holds the links for documentation and tutorials.
Then, the ‘Proxy Locations’ section provides a table overview of supported countries for each of the four types of proxies available on DataImpulse. There’s no additional functionality or details to it, which feels like a missed opportunity.
(Image credit: DataImpulse)Acting as a de facto homepage, there is a filterable real-time line chart that plots your request volume across custom date ranges and breaks down traffic metrics by total requests, charged traffic, and total spend.
The dashboard also logs individual session activity in an audit table showing the timestamp, targeted host, consumed traffic, and error/success states, with the option to export this data directly to a CSV report.
In terms of generating your target connection parameters, the dashboard provides a straightforward copy-paste system where you can grab your master username and password strings.
You can also manage whitelisted IPs via a simple menu. Just save your scraping server's public IP address within your dashboard profile, and the gateway will automatically recognize your incoming traffic tunnels, thus shaving valuable milliseconds off your script's execution times.
Customer SupportOne of the things DataImpulse singles out is its 24/7 personalized, human support. The company proudly mentions that you’ll get an answer in under 3 minutes, courtesy of an ever-present chat icon in the lower right corner. Our experience was no different - we got a tidy reply in less than a minute.
If you prefer self-service support, the company provides a thorough Documentation page. Here, you’ll find various technical reference materials, API endpoints, authentication methods, and basic proxy setup instructions.
(Image credit: DataImpulse)Covering the more practical elements is the Tutorials page. It acts as an extensive step-by-step engineering handbook, packed with detailed screenshots, videos, configuration blueprints, and copy-pasteable code snippets for integrating proxies across dozens of operating systems, scraping frameworks, and headless browsers.
For customers deploying huge data operations at the custom enterprise level, DataImpulse boosts its support to include dedicated account managers and custom feature engineering tailored specifically to the customer’s unique data requirements.
The CompetitionTruth be told, proxy services are tough competition. DataImpulse has robust providers like Bright Data, Oxylabs, and Decodo playing in the same field, though its unexpiring pay-as-you-go traffic and a highly disruptive $1 per GB residential proxy baseline certainly make it stand out. Nonetheless, the main problem is a lack of out-of-the-box software tools or any sort of target templates, leaving all the coding execution to your team.
Final VerdictIf anything, DataImpulse proves you don’t have to pay a premium to get access to a reliable and ethically sourced proxy network. In no small part thanks to its price tag and data that never expires, the company has created an accessible, developer-first platform that is finely tuned to the needs of individual developers, startups, dev squads, and growing small businesses.
So, if you have the know-how and are looking to drastically slash data collection costs, DataImpulse will more than suffice. But, if you’re not much of a technical user and require a fully managed, hands-off scraping solution, it will likely be too bare-bones for your liking.
- Take a look at our guide to the best proxy services
In an era increasingly dominated by AI and automation, it’s still incredible just how much construction work remains manual. The contrast is most stark in data centers – facilities built to power cutting-edge technology, but whose delivery is generally still slowed down by major fragmentation and time-consuming manual tasks.
One of the industry’s biggest challenges seems to be capturing and understanding what’s actually happening on a project. Project managers can spend countless hours walking sites, checking completed work and coordinating contractors operating to different schedules. When documentation falls behind, small issues can go unnoticed and eventually develop into costly delays.
While the industry is finally starting to get to grips with this technology, construction is presenting own on challenges. Autonomy works best within fixed parameters and with a limited number of variables, but live sites offer the opposite – changing plans, moving materials, new structures being built and multiple trades working alongside each other.
Construction is where automation fails – is now the turning point?These are the exact conditions that have made automation ineffective in construction. But automation isn’t impractical or impossible, it just means vendors will need to focus on the tasks where machines can deliver the best results.
Progress capturing, side documentation and routine inspections are some of the areas where automation could work best, and better still, companies like OpenSpace argue much of this work can actually be done outside of normal operating hours to both reduce disruption, and to reduce exposure to a live and dynamic environment.
Data centers could be a good proving ground for this, with large floor plans, repeatable layouts and intense schedule pressures. With up-to-date data from overnight checks, for example, leaders could assess progress and identify areas that need extra attention.
But collecting that data is only the first step, because the AI running behind the scenes need to be able to interpret site imagery to cross-reference it with site plans, drawings, schedules and other information held across other systems.
In this Q&A, OpenSpace CEO Jeevan Kalanithi explains why construction automation is starting to gain momentum, why ‘good enough’ may indeed be good enough without needing immediate perfection, and how automated monitoring could actually help soften the blow of ongoing labor shortages.
- Construction has traditionally been one of the least automated industries. Why is that beginning to change now?
I actually think the idea that construction is slow to adopt technology is a bit of a misconception. Builders adopt tools that genuinely make their jobs easier – they've just been waiting for technology that understands how they actually work.
Construction is fundamentally different from industries like manufacturing. Every project is unique. Jobsites change every day. Teams are working in environments that are constantly evolving, with dozens of trades operating simultaneously. That's a much harder environment to automate than a factory or warehouse where conditions are highly controlled.
For a long time, most construction software focused on documents, schedules and reports because that's what computers could understand. But construction isn't really a document problem – it's a physical-world problem. The most important information lives on the jobsite itself: what's been built, what's changed, where work is progressing and where risks are emerging.
What's changing now is that AI is becoming capable of understanding the physical world. Instead of asking people to manually document what's happening, AI can interpret images and other real-world data to understand the state of a project. That makes robotics and automation much more practical because they fit naturally into how builders already work instead of forcing them to adopt entirely new processes.
- Robotics in construction has been discussed for years but has seen relatively limited adoption. What factors are contributing to the increased interest in autonomous and semi-autonomous systems on jobsites today?
The conversation has become much more practical.
Ten years ago, people asked whether robots would replace construction workers or build entire buildings autonomously. Today the question is much simpler: How can robots help experienced builders work more efficiently? That's an important shift because construction has always adopted tools that solve real problems.
The biggest opportunities today are around repetitive, time-consuming tasks like documenting progress, capturing site conditions or performing routine inspections. Those activities are incredibly valuable, but they're not necessarily the best use of a superintendent's or project engineer's time.
I think of robotics a bit like the introduction of nail guns. Nail guns didn't replace carpenters – they helped carpenters work faster and more consistently. Robotics is following a similar path. The goal isn't to automate construction. It's to automate specific tasks that allow skilled professionals to spend more time coordinating work, solving problems and making decisions.
We're already seeing that with the robotics companies that we integrate with, each approaching different aspects of autonomy. Rather than trying to solve every problem, they're proving that robots can reliably perform specific tasks that create immediate value on today's jobsites.
- Data centers and other large-scale projects have been cited as promising early use cases for construction robotics. What characteristics make those environments more suitable for autonomous technologies?
Data centers are actually a great example of where robotics and AI can demonstrate value today because they combine three characteristics that work well for autonomous systems.
First, they're relatively structured environments. Compared to a renovation project or an occupied hospital, data centers typically have large floor plates, repeatable layouts and fewer unexpected obstacles, making them easier for robots to navigate.
Second, they're incredibly schedule-sensitive. AI infrastructure is expanding at an unprecedented pace, and every day matters. Owners and contractors need continuous visibility into progress because hundreds of activities are happening simultaneously.
Finally, they're highly repetitive. The same systems and construction sequences occur over and over, which allows robotics to operate more consistently and makes it easier to measure progress over time.
Autonomous data capture is particularly valuable in these environments because it creates a consistent visual record without disrupting work during the day. That gives project teams objective information about what's actually happening on site, helping them identify issues earlier, coordinate more effectively and keep stakeholders aligned.
- Construction sites are highly dynamic environments. What are some of the biggest technical and operational challenges robots face when deployed on active projects?
Construction sites are probably one of the hardest environments you could ask an autonomous system to operate in.
Unlike a warehouse, where everything is designed to be predictable, construction sites change constantly. Materials move. Equipment gets relocated. Walls appear. Doors that were open yesterday might be closed today. You also have dozens or sometimes hundreds of people working alongside autonomous systems, so safety has to remain the highest priority.
There are practical challenges as well. Connectivity isn't always reliable. GPS often doesn't work indoors. Navigation has to account for changing layouts and temporary obstacles that don't exist in more controlled environments.
That's why I think we'll continue seeing supervised autonomy for quite some time. Humans are still remarkably good at adapting to unexpected situations, and construction has plenty of them.
One lesson we've learned is that "good enough" often beats "technically perfect." Builders don't need the most sophisticated robot, they need one that's reliable enough to show up every day, operate safely and consistently, and fit into the way projects already run.
- As robotics adoption grows, what role does visual intelligence play in helping machines understand and navigate the physical world?
Whether imagery comes from a person carrying a 360° camera, a robot, a drone or another autonomous system, collecting the data is really only the first step. The real challenge is understanding what that data means.
AI has become remarkably good at understanding language, but if it's going to operate in the real-world economy, it also needs to understand physical places, physical assets and physical work.
Construction has always had information about what was supposed to happen – drawings, BIM models, schedules and specifications. What's historically been much harder is understanding what actually happened in the field.
That's where visual intelligence comes in. AI can compare what's been built against what was intended to be built, measure progress over time, identify potential issues and surface insights that help project teams make better decisions.
Our philosophy is simple: we don't really care how the data gets collected. Whether it comes from a phone, a 360° camera, a drone, a laser scanner or a robotics platform from one of our robotics partners, our goal is to bring that information together into a common understanding of the jobsite.
Today, our customers have captured imagery across more than 70 billion square feet of construction. That scale creates an opportunity not only to help builders today, but also to help train and validate the next generation of AI systems that need to understand the physical world.
- There's often concern that robotics could replace workers. How are builders currently thinking about the relationship between automation and the existing construction workforce?
I think that's a very understandable concern, but it doesn't reflect what we're seeing on jobsites.
The builders we work with aren't trying to replace experienced people. If anything, they're trying to figure out how to help those people do more.
Construction has faced labor shortages for decades, and demand continues to outpace the available workforce. There simply aren't enough skilled people entering the trades to meet the amount of work that needs to get done. That's especially true as we build more data centers, manufacturing facilities and infrastructure.
Robotics and AI help address that challenge by taking on repetitive work like routine documentation, progress capture or inspections, allowing experienced professionals to spend more time coordinating work, solving problems and applying their expertise.
The best technology doesn't replace people – it amplifies what people are already good at. That's how I think construction will continue to adopt AI-powered tools and robotics. They become another set of tools that help experienced teams make better decisions, work more efficiently and get more done with the resources they already have.
- The concept of "human-in-the-loop" robotics is gaining attention across industries. How does that approach apply within construction environments?
I think human-in-the-loop is going to be the dominant model for construction for quite some time.
Construction is simply too dynamic to expect fully autonomous systems to handle every situation. Something unexpected happens every day – a blocked corridor, a relocated piece of equipment, a new safety barrier, an area that's suddenly inaccessible. Humans are still exceptionally good at recognizing those situations and adapting in real time.
What we're seeing today is a very practical division of responsibilities. Robots handle routine, repetitive tasks consistently, while people provide judgment, context and intervention whenever it's needed.
Some of our robotics partners are already operating this way, where autonomous systems perform most of the work but remain remotely supervised so a person can step in if something unexpected occurs.
I don't think that's a compromise. I think it's actually the right model. The goal isn't autonomy for its own sake. The goal is giving project teams better information while maintaining the flexibility and judgment that complex construction projects require.
- Some contractors are experimenting with autonomous data capture during off-hours, such as overnight or before crews arrive. What lessons are emerging from those early deployments?
The biggest lesson has been consistency.
One of the challenges with manual documentation is that it depends on people having the time to do it. On a busy project, it's easy for documentation to become a lower priority because everyone's focused on solving immediate problems.
Autonomous capture changes that. Robots can document the site on a predictable schedule, often overnight or before crews arrive, creating a consistent visual record every day without interrupting construction activities.
That consistency gives teams a much clearer understanding of how a project is progressing over time. It also makes documentation much more resilient. If a superintendent is tied up or a project engineer is out that day, the visual record doesn't stop. Everyone still has access to current information about what's happening on site.
Capturing a visual record has always been a natural byproduct of walking a site with a 360° camera. It's also a very natural task to hand to a robot. That allows experienced people to spend more of their time interpreting information and making decisions instead of simply collecting data.
- Looking ahead, what indicators should the industry watch to better understand whether construction robotics is moving beyond pilot programs and toward broader adoption?
I don't think the biggest indicator will be the number of robots on jobsites. It will be whether contractors keep using them after the pilot is over.
Construction is a very practical industry. Builders don't adopt technology because it's exciting, they adopt it because it saves time, reduces risk or helps projects run more smoothly. If a technology creates more work than it eliminates, it won't last.
We'll also know the industry has reached the next stage when robotics becomes just another way of collecting project information. Builders shouldn't have to think about whether data came from a person, a robot, a drone or another autonomous system. They should simply have access to an accurate, up-to-date understanding of what's happening on their projects.
Ultimately, I think the story is bigger than robotics. The real-world economy doesn't run on documents alone. It runs on physical places, physical assets and physical work. If AI is going to create value there, it has to understand reality, not just language.
That's why we say agents need eyes. When AI can reliably see, understand and reason about what's happening in the physical world, robotics becomes much more than automation. It becomes a new way for people to interact with the built environment and make better decisions. I think that's the transition we'll be talking about over the next decade.
You all know what a guitar amplifier is. Big, intimidating stacks of speakers and dials, designed for some nice loud guitar playing. But do they work as well for other people's guitars; i.e., can a big powerful block of music-playing kit double as a home speaker for recorded music?
It may seem like a stupid question, but the more I thought about it, the more I realized that speakers and amps are the same thing... kinda. And for people who don't have the money to buy a guitar amp and a home speaker, could buying the former kill two birds with one stone? I had to find out.
Now, I’m not talking about a guitar amp-esque speaker made by a brand traditionally known for its amps (and before these new Bluetooth speaker things became so jolly well lucrative), such as the Marshall Stanmore IV or the Orange Box. No, I’m talking about a real power-speaker designed for guitars, with jacks and dials galore and no Bluetooth in sight.
Specifically, I’m talking about my Line 6 Spider IV amp, which I have owned for at least half of my life, thus far.
Now, I know Line 6 has a mixed reputation, making it an odd choice to incorporate into a home sound system. Its speakers aren't even for serious guitarists — they're for edgy 14-year-olds without much cash who just want goofy levels of distortion. In my defense, when I bought it, I was an edge 14-year-old without much cash, who just wanted goofy levels of distortion. And it still works fine for what I need.
For the tech-heads among you, my Spider IV offers 75W of power. For the purposes of this article, you should know it has a 3.5mm and a 6.35mm input, and I played music from my turntable as well as some Edifier speakers (the same set-up as in my recent article about setting up a new turntable, so an Edifier M90 and Sony PS-LX310BT). The amp was getting incorporated into an existing hi-fi system, not replacing it.
The other important point of context before we get started is that I had to make a conscious effort to turn off my brain before undergoing these experiments. In many parts of my rational brain, I could tell that a guitar amp wouldn't function smoothly as a speaker; they're not designed to hit a wide range of frequencies, just the ones guitars play at, and they're not intended to be connected to a chain of other players. If I switched off the scientific part of my cranium, I could just mess around with wires, have some fun, and see what happened.
Turning my amp into a subwoofer(Image credit: Future)With my first experiment, I decided to start small: I’d see if my amp could function as a subwoofer for my Edifier M90s. I wanted to use its wattage just to give me some extra 'oomph', even though it’s not a bass amp.
First, I used a 3.5mm to 3.5mm cable to connect the M90s, from their Sub Out port, to the amp’s smaller input… and nothing happened. I couldn’t hear a peep from the Line 6, though oddly, the amp’s volume control adjusted the output from the speakers.
Next, I took the risky move of using a 3.5mm to 6.35mm to connect the Sub Out port to the amp’s primary input — risky because I was using an unbalanced stereo cable across devices really not meant for this use, and I wasn’t sure what was going to happen.
I turned the Line 6’s dials all the way down, plugged the cable in, and popped on some Morgan Wallen before slowly turning the volume up.
The resulting speaker sound is best described as ‘rhythmic, distorted pulses’: I was getting something, but it sounded more like asthmatic coughs than music. The amp was clearly connected, and outputting something, but it was just rhythmic fuzz. I suppose that's the Line 6 effect.
I spent quite a while fiddling with the dials, but it was impossible to reduce the drive, while also retaining enough volume for the amp to be audible when I wasn’t putting my ear to it. So it wasn't good for most music, but turn the volume up while listening to a rock track, and the pulses sounded like the strums of a really keen rhythm guitarist trying to play along. This was pretty nostalgic for me, because when I bought the Line 6, that's basically exactly what I was doing.
I tried the same experiment with my turntable, which was connected to the Edifiers via Bluetooth, and found the same result, albeit watered-down.
How about as a giant aux speaker?(Image credit: Future)Perhaps I was trying to run before I could walk? For my next experiment, I decided to make things a bit more simple. I connected the iPad I was using as a music player, to a USB-C to 3.5mm converter, and then plugged in the aforementioned 3.5mm to 6.35mm cable which led to the guitar amp’s primary input.
I could hear music! I mean, if you could call it that. The Line 6 not being a bass amp became patently clear here, because I was basically just getting treble. It was surreal; I've tested speakers and earbuds with barely any bass, but never any with literally no low frequencies to speak of.
Plus, a weird phase effect was causing noticeable oscillations in the music, providing a powerful attack but fast fade. It was most audible in Post Malone’s voice (I’d moved on to Post Malone at this point) – it sounded like he’d learnt Mongolian Throat Singing.
The resulting sound was odd, and didn't match even the affordable Tribit Stormbox Micro 3 I use in the shower, but at least the guitar amp gave me some control over it: I could use the dials to add a bit of warmth, and some fuzz to mask the lack of bass.
Then I had a brainwave: I returned to the 3.5mm to 3.5mm cable from the first section, and music sounded much better. There was finally some bass (not much), and Malone was back to his regular singing voice. It was the closest to a speaker I'd managed to bring the amp.
It wasn’t perfect, though: Line 6 amps can sound quite tinny, and I felt like I was listening to music on some cheap earbuds you get free with a flight… just 75W ones that'd annoy the downstairs neighbor.
Let's add some drive(Image credit: Future)Obviously, the thing missing from my set-up was more things, so I whacked out a Pigtronix Fat Drive to add to the mix. You can probably tell what this does; it’s obviously a vital addition to an amp which is slated for offering nothing but drive.
Here’s how the set-up looked now: a USB-C to 3.5mm converter plugged into my iPad, leading to a 3.5mm to 6.35mm cable, which was plugged into the Fat Drive pedal, which was connected to another 6.35mm to 6.35mm cable, which led into my amp. That music was getting quite a few air miles before being played.
You can probably imagine what more drive did to already-too-distorted music; it sounded like it was playing from behind a waterfall. Add to that the phase effect that playing music from the 6.35mm port provided, and it was even more of a write-off than before.
I wasn't expecting sudden audio clarity, though. As with the rest of this piece, it was just a fun experiment to try.
Unfortunately, there was a casualty to this experiment: my Fat Drive pedal conked out the day after the test, and I’ve not been able to revive it. I don’t blame my tests, as much as the several years of misuse and poor management prior to them, but I’d still like to pour one out for the Pigtronix.
Lessons learnt(Image credit: Future)Obviously, I didn’t go into this experiment thinking that a guitar amplifier would make for a genuine home speaker alternative. That’s like thinking a pottery kiln is a good microwave replacement; it technically does the same thing, but things are going to get a little crunchy.
But I was hoping it'd turn into a fun, albeit messy, party speaker in a pinch, in case I had people over and suddenly wanted room-filling sound. Sure, I could buy such a device, but given how expensive tech is (and how much space that'd take if I already own an amp), who needs that?
I always knew, too, that some useful features would be missing. My amp doesn't allow for a wireless connection, for example, and it's far from being portable.
The solution is already out there: recent Bluetooth speakers released under the Fender name (though made by by a different company called Riffsound) had an XLR port that doubles as a 6.35mm jack, so you can plug a guitar and use it as an amp, even though it's technically a speaker.
Given how many guitar companies now make consumer tech (Marshall, Fender, Orange; Yamaha does too but it's an everything-music company really), it'd be great to see this kind of feature again. Or, at the very least, some way of blurring the borders between amp and speaker. Not all of us have the space or money for both.
Most fraud strategies fail before a fraudster is ever detected, because they evaluate documents, rather than people.
Consider the operational reality: a customer signs up for a fintech service. Their government-issued ID clears every document check. Their selfie matches. Their name passes the database lookup. Onboarding completes.
What no one detected was that the identity didn’t belong to a person. It was assembled by generative AI in minutes — a synthetic construction engineered to pass exactly the checks that were run.
The account sits dormant for weeks, builds behavioral credibility, then executes a scheme that costs the platform tens of thousands of pounds. Deepfake-enabled fraud alone caused more than $200 million in losses in the first quarter of last year.
This is not a stress-test scenario. Synthetic identity fraud is the operating condition for every business that onboards customers digitally. And the industry’s default response — collect an ID, verify it, move on — was not designed for it.
The document-first blind spotThe document-first model has a structural flaw in that it only responds to fraud after a document arrives. Intent, behavioral context and the coherence of a user’s digital existence sit outside its scope entirely. Identity verification becomes a single event rather than a continuous assessment.
That architecture now has a measurable cost. GenAI-enabled fraud losses in the U.S. will reach $40 billion by 2027, up from $12.3 billion in 2023 — a compound annual growth rate of 32%. The dark web has already industrialized the supply side: scam kits that produce deepfake videos and synthetic documents sell for as little as $20, placing sophisticated fraud tools within reach of anyone willing to pay.
The damage shows up at the portfolio level. Synthetic identity fraud, fabricated identities assembled from combinations of real and invented data, accounts for 10 to 15% of charge-offs in a typical unsecured lending portfolio. That is not a tail risk. It is built into the cost structure of credit.
From KYC to proof of personhoodThe problem is not that document verification is imperfect. The problem is that document verification asks the wrong question. Proof of personhood reframes the objective entirely. Rather than asking "Is this ID real?" it asks "Is there a real, unique human being behind this transaction?" And it maintains that inquiry continuously across the customer lifecycle rather than resolving it once at onboarding.
The distinction matters operationally. Fraudsters engineer synthetic identities specifically to clear document checks. A convincing fake ID passes the same OCR and liveness detection as a legitimate one. What it cannot reproduce — not convincingly, not at scale — is the full context of a real person’s digital existence. That context is where proof of personhood lives and where effective verification has to operate.
A real person leaves years of accumulated traces across the digital economy: email addresses registered to real services, phone numbers tied to consistent carriers, devices with histories spanning multiple sessions and networks, and behavioral patterns shaped by how actual humans navigate products.
None of these traces is individually decisive. But their presence or their absence tells a story that a fabricated identity, however polished its documents, has not had time to build.
What a multi-signal assessment looks likeNo single signal tells the full story. Effective identity assessment requires multiple independent signals that together reveal a coherent identity or expose its absence.
Explicit signals form the foundation. eKYC and eID database verification cross-references user-provided information against authoritative government records, confirming that the identity exists and belongs to the person claiming it. Document authentication validates submitted IDs. Biometric liveness checks confirm a real person in real time.
These explicit signals are necessary but insufficient. Implicit signals provide the surrounding context and a fraudster finds them far harder to fabricate. Digital footprint analysis examines whether an email address carries a real history, whether social media accounts show genuine activity and whether a phone number maps to real-world patterns. Device intelligence surfaces anomalies in hardware and software environments. Behavioral signals flag interaction patterns that deviate from how humans actually behave.
The value of layering these signals is combinatorial. A fraudster can fabricate a convincing document. Simultaneously manufacturing a years-old email address, a coherent social presence, a clean device fingerprint and natural behavioral rhythms is a different order of problem. Each additional signal compounds the cost and complexity of fabrication until spoofing the full picture becomes economically unviable.
Operating across bordersFor businesses with multinational customer bases, multi-layered verification is a structural requirement. The regulatory landscape is moving fast. The European Union’s Digital Identity Wallet, which all member states must support by December 2026, will reshape how consumer identity functions across the bloc. National eID programs are expanding across Asia, Africa and Latin America on varying timelines and to varying technical standards. The World Bank estimates 850 million people still lack official government identification — a reality that makes digital coverage and inclusion inseparable priorities.
Businesses that operate effectively in this environment will need verification coverage broad enough to match their customer base and adaptable enough to keep pace with shifting regulatory requirements. eKYC and eID checks across national and regional schemes are the baseline. The operational advantage lies in what surrounds them; the implicit signals that distinguish a genuine applicant from a synthetic one, regardless of which jurisdiction’s ID scheme they present.
Verifying the person, not the IDRisk assessment should be well underway before a document is ever presented. When digital footprint, device intelligence and behavioral signals work at first contact, synthetic identities are filtered before they reach your most expensive verification steps — and legitimate users clear faster because the system already has context.
That architecture only works when fraud, IDV and AML share a unified data environment instead of operating in parallel. The organizations building that capability now set themselves up to absorb fewer losses and onboard the customers that their competitors cannot confidently clear.
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