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When you think of energy infrastructure, you probably imagine towering smokestacks, old power lines, cramped industrial parks, and lengthy coal trains. Once virtual power plants enter the picture, this could change. With a decentralized system stabilizing supply and demand, energy infrastructure goes from dirty and industrial to clean and welcoming. The Emerging Faults in Energy Infrastructure As data centers are built nationwide to support artificial intelligence and cloud computing technologies, electricity demand increases exponentially. However, energy infrastructure is not keeping pace. In the United States, most transmission lines and transformers are at least 50 years old — they have reached their planned life expectancy. Engineers are beginning to replace them, but the process will cost trillions and take years. In the meantime, peak load — the period when electricity demand is at its highest — is hitting an all-time high. If the grid can’t compensate, blackouts are likely. To avoid this outcome, operators use supplemental gas-fired peaker plants that remain dormant unless consumption is unusually high. Consumers Pay Operators Millions Operators often get millions of dollars to ensure the grid remains operational during peak load. For instance, two Southern England station operators will receive $15.5 million after supplying just three hours of electricity in January 2025. A combination of freezing weather and low wind speeds raised market prices, leading to payouts worth over 50 times the market value. Problems like this will only worsen as the grid ages and power demand increases. While these operators provide an essential service, there are undoubtedly more efficient — and less costly — alternatives. What if people didn’t have to pay surge pricing? What if, instead of relying on a centralized system, they generated their own supply? With virtual power plants (VPPs), this future might be possible. The Role of VPPs in the Clean Energy Future A VPP is a collection of small-scale distributed energy resources like solar panels, plugged-in electric vehicles, and air conditioners. What makes this system virtual if it relies on physical devices? Each electrified, interconnected node wirelessly communicates, constantly sharing information about production, consumption, and storage. When hundreds, thousands, or even millions of small-scale DERs are bundled with a control system, citizens can call on them to supply or store electricity when demand fluctuates. Unlike conventional coal or gas power plants, they can ensure stability without raising prices or contributing to climate change. However, they provide the same reliability and economic value. The Benefits of Leveraging VPPs Virtual plants help stabilize the grid by compensating for fluctuations in generation and consumption. Plus, since they don’t need a centralized physical space, they enable more integration with renewables. A typical solar farm would need around 1.88 million solar panels to produce 1 gigawatt, which takes up a lot of space. Instead of clearing out a local ecosystem to make room, civil engineers can use the existing built environment — homeowners’ rooftops. Moreover, this solution increases durability. Even if existing infrastructure could handle record-breaking peak loads, it is not resilient — few traditional power systems are designed to handle unlikely high-impact events like natural disasters or extreme weather events. Thanks to a VPP’s distributed nature, physical damage isn’t nearly as much of a threat. Can We Count on Consumers for a Solution? Since this technology relies on citizens instead of a centralized operator, some concerns about reliability exist. However, people who are a part of the system don’t get to pick and choose the rate at which electricity is produced, consumed, or stored. Further, communities have risen to the occasion in similar circumstances, proving they can be trusted. In 2022, Californians experienced a scorching, lasting heat wave. The California Independent System Operator’s electricity demandreached a record high of 51,426 megawatts at 4:55 p.m. on September 6. Around one hour later, the operator sent an emergency text alert to appeal for conservation. Consumption immediately dropped off, reaching 35,000 megawatts within six hours. Consumers demonstrated their ability to coordinate and follow through. Using VPPs in Urban and Rural Areas While this solution may be more effective in urban areas due to higher population density, it can still work in rural locations since peak loads will be considerably lower there. As long as enough consumers bundle their devices in a virtual plant, they can improve electricity production, consumption, and storage, eliminating the need for peaker plants and improving the rate of local renewable usage. Although many people don’t understand the inner workings of energy infrastructure, most pay an electricity bill monthly. Even if they have their own solar panels, renewables can’t produce power as consistently as fossil fuels. For this reason, many have to rely on the grid to some extent. If they joined a VPP, they could improve their resilience and sustainability. When Will VPPs Go Beyond Conceptualization? Today, towering smokestacks and fields of photovoltaic panels come to mind when considering energy infrastructure. As VPPs become more prevalent, this decades-old standard may soon change. The U.S. Department of Energy already plans to increase VPP capacity to 160 GW by 2030, up from 80 GW today, according to the MIT Technology Review. That’s the equivalent of eliminating roughly 160 future fossil fuel plants. If the U.S. DOE does this, it would address 10% to 20% of peak demand by the decade's end. While virtual plants have already been successfully deployed, they haven’t become widespread yet. If the DOE follows through, it could become a substantial part of the nation’s modern infrastructure in a few years. The Future of Clean Energy Powered by VPPs With virtual plants, a world where neighbors lend power to each other and dense urban areas prevent rolling blackouts could exist. This technology can incentivize homeowners to install solar panels, drive electric vehicles, and embrace electrification. As smart homes grow smarter and electricity demand rises, this technology may be the most effective solution. When you think of energy infrastructure, you probably imagine towering smokestacks, old power lines, cramped industrial parks, and lengthy coal trains. Once virtual power plants enter the picture, this could change. When you think of energy infrastructure, you probably imagine towering smokestacks, old power lines, cramped industrial parks, and lengthy coal trains. Once virtual power plants enter the picture, this could change. With a decentralized system stabilizing supply and demand, energy infrastructure goes from dirty and industrial to clean and welcoming. The Emerging Faults in Energy Infrastructure The Emerging Faults in Energy Infrastructure As data centers are built nationwide to support artificial intelligence and cloud computing technologies, electricity demand increases exponentially. However, energy infrastructure is not keeping pace. In the United States, most transmission lines and transformers are at least 50 years old — they have reached their planned life expectancy. Engineers are beginning to replace them, but the process will cost trillions and take years. are at least 50 years old are at least 50 years old In the meantime, peak load — the period when electricity demand is at its highest — is hitting an all-time high. If the grid can’t compensate, blackouts are likely. To avoid this outcome, operators use supplemental gas-fired peaker plants that remain dormant unless consumption is unusually high. Consumers Pay Operators Millions Operators often get millions of dollars to ensure the grid remains operational during peak load. For instance, two Southern England station operators will receive $15.5 million after supplying just three hours of electricity in January 2025. A combination of freezing weather and low wind speeds raised market prices, leading to payouts worth over 50 times the market value. Problems like this will only worsen as the grid ages and power demand increases. will receive $15.5 million will receive $15.5 million While these operators provide an essential service, there are undoubtedly more efficient — and less costly — alternatives. What if people didn’t have to pay surge pricing? What if, instead of relying on a centralized system, they generated their own supply? With virtual power plants (VPPs), this future might be possible. The Role of VPPs in the Clean Energy Future A VPP is a collection of small-scale distributed energy resources like solar panels, plugged-in electric vehicles, and air conditioners. What makes this system virtual if it relies on physical devices? Each electrified, interconnected node wirelessly communicates, constantly sharing information about production, consumption, and storage. When hundreds, thousands, or even millions of small-scale DERs are bundled with a control system, citizens can call on them to supply or store electricity when demand fluctuates. Unlike conventional coal or gas power plants, they can ensure stability without raising prices or contributing to climate change. However, they provide the same reliability and economic value. The Benefits of Leveraging VPPs Virtual plants help stabilize the grid by compensating for fluctuations in generation and consumption. Plus, since they don’t need a centralized physical space, they enable more integration with renewables. A typical solar farm would need around 1.88 million solar panels to produce 1 gigawatt, which takes up a lot of space. Instead of clearing out a local ecosystem to make room, civil engineers can use the existing built environment — homeowners’ rooftops. around 1.88 million solar panels around 1.88 million solar panels Moreover, this solution increases durability. Even if existing infrastructure could handle record-breaking peak loads, it is not resilient — few traditional power systems are designed to handle unlikely high-impact events like natural disasters or extreme weather events. Thanks to a VPP’s distributed nature, physical damage isn’t nearly as much of a threat. few traditional power systems few traditional power systems Can We Count on Consumers for a Solution? Since this technology relies on citizens instead of a centralized operator, some concerns about reliability exist. However, people who are a part of the system don’t get to pick and choose the rate at which electricity is produced, consumed, or stored. Further, communities have risen to the occasion in similar circumstances, proving they can be trusted. In 2022, Californians experienced a scorching, lasting heat wave. The California Independent System Operator’s electricity demand reached a record high of 51,426 megawatts at 4:55 p.m. on September 6. Around one hour later, the operator sent an emergency text alert to appeal for conservation. Consumption immediately dropped off, reaching 35,000 megawatts within six hours. Consumers demonstrated their ability to coordinate and follow through. reached a record high of 51,426 megawatts reached a record high of 51,426 megawatts Using VPPs in Urban and Rural Areas While this solution may be more effective in urban areas due to higher population density, it can still work in rural locations since peak loads will be considerably lower there. As long as enough consumers bundle their devices in a virtual plant, they can improve electricity production, consumption, and storage, eliminating the need for peaker plants and improving the rate of local renewable usage. Although many people don’t understand the inner workings of energy infrastructure, most pay an electricity bill monthly. Even if they have their own solar panels, renewables can’t produce power as consistently as fossil fuels. For this reason, many have to rely on the grid to some extent. If they joined a VPP, they could improve their resilience and sustainability. can’t produce power as consistently can’t produce power as consistently When Will VPPs Go Beyond Conceptualization? Today, towering smokestacks and fields of photovoltaic panels come to mind when considering energy infrastructure. As VPPs become more prevalent, this decades-old standard may soon change. The U.S. Department of Energy already plans to increase VPP capacity to 160 GW by 2030, up from 80 GW today, according to the MIT Technology Review. That’s the equivalent of eliminating roughly 160 future fossil fuel plants. increase VPP capacity to 160 GW increase VPP capacity to 160 GW If the U.S. DOE does this, it would address 10% to 20% of peak demand by the decade's end. While virtual plants have already been successfully deployed, they haven’t become widespread yet. If the DOE follows through, it could become a substantial part of the nation’s modern infrastructure in a few years. 10% to 20% of peak demand 10% to 20% of peak demand The Future of Clean Energy Powered by VPPs With virtual plants, a world where neighbors lend power to each other and dense urban areas prevent rolling blackouts could exist. This technology can incentivize homeowners to install solar panels, drive electric vehicles, and embrace electrification. As smart homes grow smarter and electricity demand rises, this technology may be the most effective solution.

The Role of Virtual Power Plants in the Future of Clean Energy

Staring at lines of code just to test if your website's signup form works properly is exhausting. If you're a marketer, product manager, or QA specialist without deep programming skills, you've probably thought of an easier way to test your or your client's website. Good news is there is. Codeless automation testing lets you verify your website or app works correctly without writing a single line of code. According to testRigor’s Heather Brooks, you can build automated tests 15x faster than QA engineers, which saves time and resources and is easy to replicate. Instead of programming, you use: Visual drag-and-drop interfaces for building tests.
Recording tools that watch what you do and turn it into test scripts.
Pre-built components for common scenarios across applications. Visual drag-and-drop interfaces for building tests. Recording tools that watch what you do and turn it into test scripts. Pre-built components for common scenarios across applications. This helps non-dev and developer teams to reproduce and verify bugs, run complex regression analyses, and speed up QA processes. complex regression analyses complex regression analyses In this article, I'll share four practical ways to use codeless testing tools for your website or application, even if you've never written a line of code before. 4 Ways To Use Codeless Automation Testing in Different Q&A Scenarios You need zero programming experience to create and manage automated tests without coding knowledge. Here's how: 1. Use Codeless Automation to Test for UI Regression. When you make front-end changes to your website or web application, visual elements, like layouts and navigation menus, can break off and appear differently on all browsers. This happens because browsers interpret HTML, CSS, and JavaScript differently, which can cause inconsistencies in these elemeunts. For example, after updating your CSS styling or a new checkout flow, you'll need to verify that users can still click the buttons, the forms still display correctly, and they can still navigate menu functions regardless of the browser they use. If you do this traditionally, you'd need to write test scripts for each browser to know and track how well the app works on each. This can be time-consuming and error-prone. But with codeless automation, you can record the action once, and configure it to run the same on every browser and device, so it appears the same to users on all browsers and devices. To do this; Record the actions you want to test (login, navigation, form submission, anything) in a codeless automation tool.
The tool will capture each click, form input, and validation points you navigate, and you can stop recording once you've completed the test flow.
Then, you can select the browsers you want to run the test through.
You can then review results through a dashboard that shows how the app interacts with every browser. Record the actions you want to test (login, navigation, form submission, anything) in a codeless automation tool. The tool will capture each click, form input, and validation points you navigate, and you can stop recording once you've completed the test flow. Then, you can select the browsers you want to run the test through. You can then review results through a dashboard that shows how the app interacts with every browser. Here's an example from Testsigma: 2. Testing for API Data Validation When you integrate third-party payment gateways or add your REST API endpoints to your application, you need to validate that the API responses return the expected HTTP status codes, JSON formats, and data structures. For example, if you run an e-commerce store with an inventory management API. You can create APIs that other parts of your software can use to check stock levels, and update quantities, etc… The API has an endpoint like “https://api.yourstore.com/inventory/products/123” that returns information about product #123 in your warehouse. If this validation fails, it could mean you have a bug in your system (for instance, your inventory tracking code could be storing quantities as strings instead of numbers, which would break your calculations later because you need quantities in numbers to calculate). https://api.yourstore.com/inventory/products/123 https://api.yourstore.com/inventory/products/123 With codeless testing tools like Testim or Katalon, you can configure API tests through visual interfaces. For instance, You can enter the URL of the endpoint you need to test,
Select the action you want to take from the drop-down menu (GET to retrieve your data, POST to create new data, and PUT to update existing data).
Add your authorization token to show that you have permission to access the API.
Select your content type and verify that everything is as it should be. (In this case, the product ID starts with “PRD” or the stock quantity field is a number. You can enter the URL of the endpoint you need to test, Select the action you want to take from the drop-down menu (GET to retrieve your data, POST to create new data, and PUT to update existing data). Add your authorization token to show that you have permission to access the API. Select your content type and verify that everything is as it should be. (In this case, the product ID starts with “PRD” or the stock quantity field is a number. 3. Testing for Data-driven Forms to Find Boundary/Edge Cases/ If you build a product registration form on your e-commerce website, you need to test it with different kinds of input to make sure it works properly no matter what customers type in. For instance, if your registration form has an "Age" field that should only accept values between 18 and 99, you need to test for: 18 and 99 Exactly 17 (just below minimum) - should be rejected.
Exactly 18 (minimum) - should be accepted.
Exactly 99 (maximum) - should be accepted.
Exactly 100 (just above maximum) - should be rejected. Exactly 17 (just below minimum) - should be rejected. should be rejected Exactly 18 (minimum) - should be accepted. should be accepted Exactly 99 (maximum) - should be accepted. should be accepted Exactly 100 (just above maximum) - should be rejected. should be rejected This is called boundary testing. For edge cases, you would also test unusual scenarios like entering text instead of numbers or leaving the field empty to ensure your validation works properly. With traditional coding, you'd need to write a series of code to test these boundaries. You'd write a lengthy code like this: test('Age field validation', () => { test('Age field validation', () => { enterValue('age', 17); enterValue('age', 17); expect(getErrorMessage()).toBe('Age must be at least 18'); expect(getErrorMessage()).toBe('Age must be at least 18'); enterValue('age', 18); enterValue('age', 18); expect(getErrorMessage()).toBe(''); expect(getErrorMessage()).toBe(''); And then repeat it for all test cases. With a codeless approach, all you need to do is: Create a simple Excel spreadsheet with the test data, like this: Create a simple Excel spreadsheet with the test data, like this: Age

Expected Result



17

Error



18

Success



99

Success



100

Error



abc

Error



""

Error Age

Expected Result



17

Error



18

Success



99

Success



100

Error



abc

Error



""

Error Age

Expected Result Age Age Expected Result Expected Result 17

Error 17 17 Error Error 18

Success 18 18 Success Success 99

Success 99 99 Success Success 100

Error 100 100 Error Error abc

Error abc abc Error Error ""

Error "" "" Error Error Use a codeless tool, like TestGrid, to record yourself filling out the form once.
Make your spreadsheet the data source.
Map the Age column to the Age field in your form.
And configure the validation to check for error messages. Use a codeless tool, like TestGrid, to record yourself filling out the form once. Make your spreadsheet the data source. Map the Age column to the Age field in your form. And configure the validation to check for error messages. These tools will automatically run everything in your spreadsheet. They'll run each value and report which tests pass or fail, and you wouldn't even need to do anything. Easy Peasy. 4. Testing for End-to-End Business Process E-commerce websites have multiple customer journey processes that pass through several systems before they end. Rather than testing just the check-out form or the product page alone, you should check that the entire purchasing flow works correctly from start to finish. For example, a complete e-commerce purchase process includes: A customer creating an account with their shipping address.
Searching for "wireless headphones" in your product catalog.
Filtering results by price range and customer ratings.
Adding items to their shopping cart.
Applying a discount code at checkout.
Entering payment information through your payment gateway.
Receiving an order confirmation email.
Checking their order status in their account dashboard. A customer creating an account with their shipping address. Searching for "wireless headphones" in your product catalog. Filtering results by price range and customer ratings. Adding items to their shopping cart. Applying a discount code at checkout. Entering payment information through your payment gateway. Receiving an order confirmation email. Checking their order status in their account dashboard. Traditional testing would need separate scripts for each of these steps, and these scripts are manually maintained and coordinated. On the other hand, codeless lets you build a visual workflow that represents the entire customer journey. Here's how: You'd create a visual map of the complete buying process from account creation to order confirmation.
Connect test actions (like clicking the "Add to Cart" button) without writing code.
Define checkpoints at each stage (verifying the cart total updates correctly). You'd create a visual map of the complete buying process from account creation to order confirmation. Connect test actions (like clicking the "Add to Cart" button) without writing code. Define checkpoints at each stage (verifying the cart total updates correctly). Most codeless platforms let you arrange these test steps as connected blocks. For instance, in TestCraft or Rainforest QA, you first record basic interactions, like searching for products and clicking the checkout button, then arrange them in sequence. You can also add verification points throughout the flow to check if products appear in the cart or if the order confirmation displays the right information. Conclusion Codeless test automation is beneficial, but it's not a complete replacement for all testing. Start with the basics and focus on important areas like your checkout processes, but don't abandon manual testing entirely. The most successful team combines codeless tools with traditional coding so everyone on the team can contribute to the testing process – even those who cannot code.

Codeless Test Automation is Beneficial, but Not a Complete Replacement for All Testing

Solana’s recent governance vote on the SIMD-228 proposal, which put forward the idea of lowering Solana’s inflation rate and boosting staking, showed the network can effectively handle major voting decisions without a hitch on the technical level. Meanwhile, we’ve also seen throughput on the network increasing to accommodate the huge volume of memecoin trading. However, one major hurdle remains on its path to becoming the “Nasdaq on the blockchain” – MEV bots. Solana’s recent governance vote on the SIMD-228 proposal, which put forward the idea of lowering Solana’s inflation rate and boosting staking, showed the network can effectively handle major voting decisions without a hitch on the technical level. Meanwhile, we’ve also seen throughput on the network increasing to accommodate the huge volume of memecoin trading. However, one major hurdle remains on its path to becoming the “Nasdaq on the blockchain” – MEV bots. “Nasdaq on the blockchain” MEV stands for maximum extractable value and refers to the additional profit that can be made from a transaction. MEV is like waiting in line at a store checkout, having someone cut in front of you to buy everything in sight, and then selling it back to the rest of the line at a higher price. The MEV problem So how do MEV bots actually work? When you make a transaction, like buying or swapping tokens, that transaction goes into a waiting area, generally called a mempool, before it’s added to a blockchain. Validators – the people or computers that add blocks to the chain – or bots can see these waiting transactions. They sometimes reorder or add their own transactions first to make a profit, often paying huge fees to cut in line. Advocates see MEV as a byproduct of a healthy network and free market mechanics, while others stress the downsides for users. Whichever camp you find yourself in, though, MEV bots have been responsible for huge losses due to front-running and sandwich attacks. For example, Ethereum saw a total of $675 million extracted through MEV between December 2019 to September 2022. total of $675 million extracted total of $675 million extracted However, since 2024, Solana has been seeing a marked uptick in MEV activity due to sandwich bots. This means traders on Solana are losing profits on a daily basis to MEV bots. As such, unless the ecosystem figures out a way to approach the challenges posed by MEV, it threatens Solana's goal of providing distributed financial services at the speed of light. marked uptick in MEV marked uptick in MEV MEV on Solana is different The thing is, MEV is intrinsic to all blockchains, especially when block proposers rotate. It’s not unusual for validators on any blockchain to manipulate transactions to extract profit. This can occur during NFT mints, liquidations, swaps, or by exploiting arbitrage opportunities. Not all MEV is bad, though, but certainly sandwiching and frontrunning are. \ Due to Solana's speed and architecture, however, certain types of MEVs are naturally eliminated, and network participants' ability to extract value hinges on their ability to outperform the time to finality of Solana transactions. Moreover, since Solana doesn't have a mempool, MEV searchers have to integrate with existing high-stake node operators to see potential value extraction opportunities. Mempools aren't the solution However, without a place to queue transfers, there's no other way than simply trying again when a transfer fails. To fix this, in 2023, Jito launched a protocol off-chain that auctioned partial blockspace, providing inclusion guarantees for sets of transactions called bundles. The aim of creating an auction mechanism off-chain was to increase efficiency and reduce spam. While this move pulled existing MEV activity into the open, mitigated some of the front-running activities, and established a transparent auction mechanism, the lack of solid MEV protection tools still left the end user at a severe disadvantage. As complaints from DeFi protocols and users mounted, Jito Labs shut down its mempool entirely. But Pandora's box had been opened, and private, even less beneficial mempools took its place. Centralization risks According to a Helius report earlier this year, much of the MEV revenue results from sandwich attacks and accrues to just one centralized entity (DeezNode). This suggests that while Jito’s memepool was at least allowing this to happen transparently, these attacks have now moved into the dark. report report sandwich attacks sandwich attacks The key concern here is that if those validators that extract more value are left unchecked, it will be a permanent drag to Solana users. In short, fixing MEV is not solely about creating a network that disincentivizes validators from lining their pockets with profits they sandwiched out of memecoin traders; it's also about ensuring the integrity and decentralization of the network. The search for a fix MEV is a natural part of every blockchain network, but currently, it's benefitting a few at the expense of many. And this problem will likely increase exponentially, with Jito Co-Founder Zano Sherwani predicting MEV will become a billion-dollar sector. sector sector Ethereum addressed its MEV problem mainly by establishing a transparent marketplace for it. This is complemented by various tools, including MEV-protection bots and RPCs, that aim to protect users from MEV. On Solana, suggestions to address the issue include whitelists – a last-resort option as it would lead to potential censorship – implementing RFQ systems, which replace AMMs with professional market makers to provide fair pricing to end-users, and protocol changes. However, protocol changes, such as introducing concurrent leaders throughout the block production process to prevent one from becoming the target of MEV bots, will take years to implement. We might not have years to find ways to address MEV on Solana, as users quickly move away once they are burned. concurrent concurrent Emerging solutions Fortunately, MEV research on Solana has picked up, and while developers are working on protocol changes, there are plenty of things other network participants are delivering. Ellipsis Labs, for example, recently launched its first experimental sandwich-resistant AMM. AMM AMM Meanwhile, Titan’s swap feature gives users more control with live updates that cuts the time it takes to execute a swap, reducing the attack surface by reducing expected slippage. Jito bundles are also used extensively to provide further protection from searchers. Another app-layer approach is implementing dynamic slippage, where the Talos algorithm, powered by statistics and machine learning, adjusts in real time to reduce negative slippage. Lastly, mirroring how TradFi is kept in check through the social layer, Solana stakers could stir network behavior by staking their funds with validators that don't engage in malicious practices. Simple, yet effective. A combination of stronger MEV protection, protocol layer changes, and social layer influence is a strong base for creating a better network for everyone involved. Only once Solana effectively addresses MEV can it truly shine as the mainstream blockchain it was designed to be.

Solana Needs to Fix Its MEV Problem

سولانا بحاجة إلى إصلاح مشكلة المركبات الكهربائية متعددة الاستخدامات

Solana MEV problemini düzəltmək lazımdır

Solana трябва да реши проблема си с MEV

Solana necesita solucionar su problema con los vehículos eléctricos

سولانا باید مشکل MEV خود را برطرف کند

Solana Perlu Memperbaiki Masalah MEVnya

SolanaはMEVの問題を修正する必要がある

Solana უნდა გადაიხადოს მისი MEV პრობლემა

លោក Solana មានតម្រូវការដើម្បីដោះស្រាយបញ្ហា MEV របស់គាត់

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සොලානාට එහි MEV ගැටලුව විසඳා ගැනීමට අවශ්‍යයි

Solana Yi Lava Ku Lulamisa Xiphiqo Xa Yona Xa MEV

Build Your Own BEP-20 Token in a Single Afternoon—No Prior Experience Needed

Authors:
(1) VINÍCIUS YU OKUBO, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil;
(2) KOTARO SHIMIZU, Department of Applied Physics, The University of Tokyo, Tokyo 113-8656, Japan;
(3) B. S. SHIVARAM, Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA;
(4) HAE YONG KIM, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil. Authors: Authors: (1) VINÍCIUS YU OKUBO, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil; (2) KOTARO SHIMIZU, Department of Applied Physics, The University of Tokyo, Tokyo 113-8656, Japan; (3) B. S. SHIVARAM, Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA; (4) HAE YONG KIM, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Related Work II. Related Work III. Methodology III. Methodology IV. Experiments and Results IV. Experiments and Results V. Conclusion and References V. Conclusion and References II. RELATED WORKS A. JUNCTIONS AND TERMINALS DETECTION A. JUNCTIONS AND TERMINALS DETECTION A. JUNCTIONS AND TERMINALS DETECTION Junctions and terminals are shapes with relevance that extend beyond materials. Within computer vision, recognizing and enumerating them has been performed in diverse contexts, such as natural landscapes [20], biology [21] and handwriting images [22]. B. CLASSICAL METHODS FOR JUNCTIONS AND TERMINALS DETECTION B. CLASSICAL METHODS FOR JUNCTIONS AND TERMINALS DETECTION B. CLASSICAL METHODS FOR JUNCTIONS AND TERMINALS DETECTION One prevalent approach for detecting junctions and terminals involves using skeletonization as a pre-processing step. This process reduces the image to one-pixel-wide lines to represent its structures. Points in skeleton can then be identified as terminals, junctions and crossings based on their neighboring pixels. This technique has been applied in vascular images [23] and in the analysis of handwritten Chinese characters [24]. Pre-processing techniques using contour information have also been explored for junction detection. Lee and Wu [25] investigated stroke extraction in Chinese characters. Their method segments regions according to their contour, identifying junctions by counting neighboring regions. Maire et al. [26] proposed a junction detector in natural images by locating intersecting contours. This approach applies an expectation–maximization style algorithm to iteratively select relevant contours and suggest the junction’s position. However, skeletonization and contour finding are noise sensitive processes, and a pre-processing error will lead to a detection error. Junctions and terminals can also be identified by analyzing the arrangement of linear structures within the image. Su et al. [27] describe a technique for identifying these linear structures using the Hessian Matrix. This approach has been validated in biological images such as blood vessels, neutrites and tree branches. Xia et al. [20] present a junction detection method in natural images, based on amplitudes and phases of the normalized gradients of the image. Template-based approaches quantify the similarity of the appearance of image regions and the template. Deriche and Blaszka [28] modeled this approach as energy minimization, which is calculated by the deviation between the image and a predetermined model. This enabled the detection of key image features, such as edges, corners and terminals. C. DEEP LEARNING METHODS FOR JUNCTIONS AND TERMINALS DETECTION C. DEEP LEARNING METHODS FOR JUNCTIONS AND TERMINALS DETECTION C. DEEP LEARNING METHODS FOR JUNCTIONS AND TERMINALS DETECTION Owing to the success of R-CNN based detection techniques, Pratt et al. [21] developed a pipeline for identifying junctions and crossings in retinal vascular structures. Their method involves two main steps: initially, detection regions are proposed centred along the blood vessels, which are then classified as junctions, crossings or background. To generate the detection regions, their approach requires a binary segmented version of the exam. Theses images undego a skeletonization process, with the resulting points serving as references for the centers of the blood vessels. Zhao et al. [29], addressing the same problem, proposed using a Mask R-CNN base model [30] for region proposal. This strategy enables inference without the need for binary segmented version of the exam. However, during training, the segmented images are still used in the Mask R-CNN model to enhance its learning capabilities. This approach surpassed the performance of previous techniques in the detection of junctions and crossings in retinal vascular images. III. METHODOLOGY A. DATASET A. DATASET A. DATASET In this study, we used films of a ferromagnetic material of recognized technological importance, Bi:YIG, and obtained magnetic images of the labyrinthine patterns using a microscope with polarized light [31]. We specifically focused on the evolution of the labyrinthine patterns under the demagnetization field protocol described below. First, we prepared the sample in the fully saturated state by applying a sufficiently large magnetic field in +z direction, which is perpendicular to the films. In this state, magnetic moments in the Bi:YIG film are forced to point in the field direction. Next, we instantaneously dropped the field to zero and hold it for 10 seconds to get the image. Since the magnetic field is zero, magnetic moments can point upward and downward, resulting in the labyrinthine patterns shown in Fig. 1; the bright and dark regions represent the domains with opposite directions of magnetic moments. Such a process involving switching on and off the magnetic fields is considered half of the demagnetization step. In the remaining half step, a magnetic field was applied with a reduced amplitude and oriented in the opposite direction. We again captured the magnetic domain image after reducing the magnetic field to zero. By repeating these protocols up to 18 steps, we investigated the evolution of the labyrinthine patterns in the demagnetization process step by step. The amplitude of the magnetic field was exponentially reduced with each step. We conducted a series of demagnetization processes from the fully saturated state, repeating this cycle six times. Furthermore, we explored another situation, where the magnetic field was initially applied in the −z direction, and its direction was alternated step by step. Consequently, a total of 12 demagnetization processes were performed, yielding a collection of 444 domain images. All measurements reported here were performed at room temperature. The experimentally obtained images covered an area of 2 mm × 1.8 mm. The original high-resolution color images were converted to grayscale and their resolutions were reduced to 1300×972 to facilitate processing. Furthermore, a median filter with kernel size 3 was applied to reduce noise. B. TM-CNN OVERVIEW B. TM-CNN OVERVIEW B. TM-CNN OVERVIEW Our approach to detect junctions and terminals in magnetic labyrinthine patterns consists of two sequential steps: proposal of potential detections and their classification between junction, terminal and false detections. It is inspired by other cascaded object detection techniques like Viola and Jones face detection [8], R-CNN [12], and scale and rotation invariant template matching [32]. Fig. 3 illustrates the overall structure of TM-CNN. In the first phase, the algorithm generates a preliminary set of potential detections. It must propose all true defects, even if it also generates many false positives. We achieve this by applying template matching detection with a low threshold, followed by a non-maximum suppression. In the second phase, in order to eliminate the false positives, each potential detection is filtered by a CNN classifier. C. TEMPLATE MATCHING C. TEMPLATE MATCHING C. TEMPLATE MATCHING The basic form of template matching finds instances of a smaller template T within a larger image I. This is done by calculating some similarity metric between the model T and the content of a moving window located at each possible position of I. We measured the similarity between the image and the template using the Normalized Cross Correlation (NCC). NCC is invariant to linear changes in brightness and/or contrast. NCC between template T and image I at pixel (x, y) is calculated as: This basic approach is not well suited for detecting junctions and terminals in the magnetic labyrinthine structures because a single template is not capable of modeling: All possible rotations;


All deformed shapes of defects. All possible rotations; All possible rotations; All deformed shapes of defects. All deformed shapes of defects. To solve problem (1), we employ a rotation-invariant template matching based on exhaustive evaluation of rotated templates. There are some alternative rotation-invariant techniques based on circular and radial projections [32], [34], and on Fourier coefficients of circular and radial projections [35]. These techniques can reduce computational requirements, but their implementations are complex and require parameter tuning. Furthermore, our application does not require exceptional computational performance, as the processing is offline. Thus, our technique uses the standard OpenCV[1] template matching implementation, which is highly optimized using FFT and special processor instructions. D. TEMPLATES AND MASKS USED IN THE EXPERIMENT D. TEMPLATES AND MASKS USED IN THE EXPERIMENT D. TEMPLATES AND MASKS USED IN THE EXPERIMENT We manually designed templates and masks, and empirically tuned them to capture various junction and terminal shapes. They have 21×21 resolutions and are generated at runtime. Templates represent magnetic strips as black lines radiating from their center drawn on a white background. Meanwhile, masks are created from black backgrounds with white areas defining relevant coordinates used in template matching. Their main purpose is to obscure the space between strips and background, addressing variations in widths and curvatures, and to limit the background regions to reduce interference from neighboring strips. Table 1 exemplifies the templates and masks used to detect junctions and terminals. Template matching is applied separately for each (template, mask) pair. Several template matchings are computed in parallel using the OpenMP library[2]. This process takes about 80 seconds to process an image on an i7-9750H processor. To obtain the final correlation map corr, we calculate the maximum value among all n = 3 × 439 + 5 × 120 = 1917 NCC maps for each position (x, y), that is: Pixels where corr(x, y) exceeds a predefined threshold t are considered potential detections. However, a single junction/terminal may encompass multiple neighboring points with correlation values greater than the threshold t. Therefore, it is necessary to perform some form of non-maximum suppression to eliminate duplicate detections and select only the true center of junction/terminal. Kim et al. [18] present a solution to this problem: Whenever two potential detection points p1 and p2 are separated by a distance smaller than a threshold c, the point with the lowest correlation value is discarded. In this work, we use a slightly different approach, but with the same practical result: Whenever the algorithm finds a potential detection, it executes a breadth-first search algorithm. This algorithm recursively searches adjacent pixels (x, y) where the correlation exceeds 80% of the threshold (that is, corr(x, y) > 0.8t) and saves the pixel with the highest correlation. Subsequently, the searched area has its correlation value set to zero to avoid re-detection. Fig. 3d highlights the searched area in red. The pixel with the highest correlation is chosen as the center of the junction/termination. This process performs detection in a single pass. E. DATASET ANNOTATION E. DATASET ANNOTATION E. DATASET ANNOTATION To classify potential detections into true or false using a CNN classifier, we must first create annotated training images. TM-CNN makes it easier to create training examples, as it allows one to annotate the examples semi-automatically. This process is divided in two phases. 1) Template matching-assisted annotation 1) Template matching-assisted annotation In this phase, only a small set of images are annotated. Initially, we apply template matching followed by nonmaximum suppression to identify the centers of possible detections, together with their probable labels (junction or terminal). Without this help from template matching, we would have to manually and precisely locate the centers of thousands of defects. Subsequently, a human reviewer makes corrections to ensure that the labels given by the template matching are correct. The reviewer may change the labels to junction, terminal or false detection. After all positive detections are annotated along with a small set of false detections, a larger set of false detections is created by lowering the template matching threshold and sampling new false detections. These images, now with positive and negative annotations, are used to train a preliminary version of the CNN classifier. 2) Deep learning-assisted annotation 2) Deep learning-assisted annotation Due to the small number of images in the initial training set, the preliminary CNN classifier cannot accurately classify all magnetic stripe defects. Nonetheless, this preliminary model is integrated into the annotation procedure to alleviate the required workload. In the second phase, we continue using template matching to generate the initial set of detections. However, the preliminary CNN classifier is employed to identify most of the template matching errors, thus speeding up the annotation process. As new images are annotated, more accurate models are trained to further simplify the annotation workload. The final training set consists of 17 images derived from a single annealing protocol, selected to cover varied experimental configurations of ascending and descending magnetic fields at different magnitudes. Out of these, 16 were selected from the quenched (unordered) state, as they represent a more diverse set of shapes and represent a greater challenge for classification. The training set encompasses a total of 33,772 detections, which includes 12,144 junctions, 12,777 terminals, and 8,851 false detections. F. CANDIDATE FILTERING BY CNN F. CANDIDATE FILTERING BY CNN F. CANDIDATE FILTERING BY CNN Our algorithm extracts small 50×50 patches centered around each detection point and a CNN classifies them into three classes: junction, terminal or false positive. The size of patches for CNN classification is larger than the size of template matching models (21×21), allowing CNN to use more contextual information than template matching. We use a simple CNN model to classify the small patches (Fig. 4). It has four convolutional layers with 32, 64, 128 and 256 filters, all using 3×3 kernels. The first three convolutional layers are followed by max pooling layers to downsample the feature maps and global max pooling is applied after the last convolutional layer. This is followed by dropout and two fully connected layers: the first with 128 nodes and the second with three output nodes. The ReLU activation function is used across the model, except at the output layer where the softmax is used. In total, this network has only 422,608 parameters. For comparison, VGG-16 and ResNet-50 (common backbones for detection) have 138 million and 23 million parameters, respectively. Thanks to its simplicity, our model is fast and can make predictions even without GPUs and takes around 30 seconds for filtering each image using an i7-9750H processor with 16 GB of RAM. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] Open Source Computer Vision Library, https://opencv.org [2] Open Multi-Processing, https://www.openmp.org/ [3] https://www.tensorflow.org/

Why TM-CNN Is a Material Scientist's New Best Friend

Think about the reasons why one mobile ad can be much more appealing than another. It is more complicated than one might think. Companies spend a lot on advertising and enhance their marketing with strategies like personalization, social proof, or even artificially creating a sense of urgency. Such companies can market products attractively using clickbait-type intensive advertisements or by appealing to emotions and leveraging action triggers. The Science Behind Clicks They use approaches called heuristics, which are mental shortcuts and more complex biases. One such heuristic is the frequency illusion, where after a person is introduced to something, such as a product, they seem to start noticing it everywhere. This raised awareness can lead the person to believe that the product is actually more abundant in the world than it really is. A large emotional aspect greatly impacts an individual’s interaction with advertisements. To most people, mobile marketing campaigns focusing on mobile devices, which invoke feelings elicited by happy, surprising, or nostalgic events, tend to do well. An emotional bond often makes recalling the ad a tad bit easier, which is good for advertising. The visuals, such as the imagery, color, and even the vocabulary, have great importance in attracting your attention. Humans assign meaning to images faster than they do to words, which shows how helpful visuals can be in aiding communication. Since meaning can be attached to imagery much faster than words, visually appealing advertisements can capture attention, convey messages, incite emotions, and make people feel engaged. Additionally, certain colors may invoke feelings and impact one’s behavior according to color psychology. Red, for instance, is related to a heightened sense of urgency that commands immediate action, whereas blue depicts trustworthiness and dependability. Such color associations, strategically applied in marketing, help elicit the intended response from you. Conversions Stimuli For businesses, understanding what drives consumers to make purchases is key in improving their marketing methods. Some important aspects of a business greatly affect the consumers' decision to buy, such as: 1. Personalization and Relevance People tend to buy items that are presented and packaged specifically for them. For instance, targeted ads are said to yield 2.7 times the revenue per advertisement compared to generic ads. 2. Social Proof and Associated Urgency People's behavior is potentially influenced by other people's behavior. Customer reviews and images help other consumers as they believe it to be the most authentic and meaningful to 60% of people. 3. Trust and Dependability Your level of trust in the credibility of a website affects your likelihood to convert. Customer reviews, user-generated content, or even social media influencer promotions act as trust indicators to the credibility of the website. There is no danger in considering a site you trust for information, so you trust the site to not fail you, and will, therefore, take the action that you so desire. Strategies for Effectively Optimizing Mobile Ads In the current era of advanced digital technology, optimizing mobile ads is critical, as it can increase user interaction, engagement, and overall conversion rates. Making use of location based targeting makes certain that the ads are placed in front of the people residing in a certain geographical region. Studies show that ads with region-specific details receive a better CTR and may increase up to 20% more than other ads that do not have location details. Adapting advertisements to user behavior and preferences has shown to create more personalized experiences. Studies show that targeted ads generate 2.7 times more revenue per ad than non-targeted ads and are twice as effective at converting ad clicks into sales. With the right strategy, relaunching campaigns for users with Android devices proves successful as they enjoy a higher 3.48% open rate compared to 1.77% on iOS devices. Re-targeting users through push notifications has a measured success rate that varies across platforms. Integrating advertisements within mobile games proves to be extremely effective, especially when using rewarded videos, as players increase their chance of purchasing by 23% and their chances of making in-app purchases by 18% when exposed to rewarded videos as opposed to advertisements. Not only is it important to promote your products, but it is also important to ensure your ads are set for mobile viewing. Ads specifically optimized for mobile devices further enhance the user mobile experience, thus improving engagement and conversion rates. Conclusion Having the right understanding of a customer’s psychology gives you an upper hand in creating working campaigns. Campaigns designed using personalized approaches have higher click rates, increased conversions, and have proven to be more effective. The approach is focusing on strongly enhancing elements such as social proof, per-established trust, and urgency, making relevant ads while ensuring ease of navigation. As mobile advertising marketing strategies grow, understanding consumer behavior, trends, and A/B testing keeps any campaign effective and competitive. Through the application of psychological techniques to advertising methods, campaigns can be designed to capture attention, resulting in interactions that are not only meaningful but also noteworthy. Think about the reasons why one mobile ad can be much more appealing than another. It is more complicated than one might think. Companies spend a lot on advertising and enhance their marketing with strategies like personalization, social proof, or even artificially creating a sense of urgency. Such companies can market products attractively using clickbait-type intensive advertisements or by appealing to emotions and leveraging action triggers. The Science Behind Clicks They use approaches called heuristics, which are mental shortcuts and more complex biases. One such heuristic is the frequency illusion, where after a person is introduced to something, such as a product, they seem to start noticing it everywhere. This raised awareness can lead the person to believe that the product is actually more abundant in the world than it really is. A large emotional aspect greatly impacts an individual’s interaction with advertisements. To most people, mobile marketing campaigns focusing on mobile devices, which invoke feelings elicited by happy, surprising, or nostalgic events, tend to do well. An emotional bond often makes recalling the ad a tad bit easier, which is good for advertising. mobile marketing mobile marketing The visuals, such as the imagery, color, and even the vocabulary, have great importance in attracting your attention. Humans assign meaning to images faster than they do to words, which shows how helpful visuals can be in aiding communication. Since meaning can be attached to imagery much faster than words, visually appealing advertisements can capture attention, convey messages, incite emotions, and make people feel engaged. Additionally, certain colors may invoke feelings and impact one’s behavior according to color psychology. Red, for instance, is related to a heightened sense of urgency that commands immediate action, whereas blue depicts trustworthiness and dependability. Such color associations, strategically applied in marketing, help elicit the intended response from you. Conversions Stimuli For businesses, understanding what drives consumers to make purchases is key in improving their marketing methods. Some important aspects of a business greatly affect the consumers' decision to buy, such as: 1. Personalization and Relevance People tend to buy items that are presented and packaged specifically for them. For instance, targeted ads are said to yield 2.7 times the revenue per advertisement compared to generic ads. 2.7 times 2.7 times 2. Social Proof and Associated Urgency People's behavior is potentially influenced by other people's behavior. Customer reviews and images help other consumers as they believe it to be the most authentic and meaningful to 60% of people. 60% 60% 3. Trust and Dependability Your level of trust in the credibility of a website affects your likelihood to convert. Customer reviews, user-generated content, or even social media influencer promotions act as trust indicators to the credibility of the website. There is no danger in considering a site you trust for information, so you trust the site to not fail you, and will, therefore, take the action that you so desire. Strategies for Effectively Optimizing Mobile Ads In the current era of advanced digital technology, optimizing mobile ads is critical, as it can increase user interaction, engagement, and overall conversion rates. Making use of location based targeting makes certain that the ads are placed in front of the people residing in a certain geographical region. Studies show that ads with region-specific details receive a better CTR and may increase up to 20% more than other ads that do not have location details. 20% 20% Adapting advertisements to user behavior and preferences has shown to create more personalized experiences. Studies show that targeted ads generate 2.7 times more revenue per ad than non-targeted ads and are twice as effective at converting ad clicks into sales. 2.7 times 2.7 times With the right strategy, relaunching campaigns for users with Android devices proves successful as they enjoy a higher 3.48% open rate compared to 1.77% on iOS devices. Re-targeting users through push notifications has a measured success rate that varies across platforms. 3.48% 3.48% Integrating advertisements within mobile games proves to be extremely effective, especially when using rewarded videos, as players increase their chance of purchasing by 23% and their chances of making in-app purchases by 18% when exposed to rewarded videos as opposed to advertisements. 23% 23% Not only is it important to promote your products, but it is also important to ensure your ads are set for mobile viewing. Ads specifically optimized for mobile devices further enhance the user mobile experience, thus improving engagement and conversion rates. Conclusion Having the right understanding of a customer’s psychology gives you an upper hand in creating working campaigns. Campaigns designed using personalized approaches have higher click rates, increased conversions, and have proven to be more effective. The approach is focusing on strongly enhancing elements such as social proof, per-established trust, and urgency, making relevant ads while ensuring ease of navigation. As mobile advertising marketing strategies grow, understanding consumer behavior, trends, and A/B testing keeps any campaign effective and competitive. Through the application of psychological techniques to advertising methods, campaigns can be designed to capture attention, resulting in interactions that are not only meaningful but also noteworthy.

The Psychology of Mobile Ads: What Drives Clicks and Conversions?

La psicología de los anuncios móviles: ¿Qué impulsa los clics y las conversiones?

Mobiilimainosten psykologia: mikä saa klikkauksia ja tuloksia?

La psychologie des publicités mobiles : qu’est-ce qui génère des clics et des conversions ?

モバイル広告の心理学: クリックとコンバージョンを促進するものは何ですか?

Mobiliųjų reklamų psichologija: kas skatina paspaudimus ir konversijas?

Психологија на мобилните реклами: Што предизвикува кликови и конверзии?

Thuto ya Saekoloji ya Dipapatšo tša Sellathekeng: Ke Eng Seo se Kgantšhago go Klika le Diphetolelo?

Psihologia anunțurilor mobile: ce determină clicurile și conversiile?

จิตวิทยาของโฆษณามือถือ: สิ่งที่ขับเคลื่อนคลิกและการแปลง?

ERROR: invalid target_lang (ti)

Jübi mahabatlarynyň psihologiýasy: Näme basýar we öwrülýär?

I've been a big fan of Mutation Testing since I discovered PIT. As I dive deeper into Rust, I wanted to check the state of mutation testing in Rust. Starting with cargo-mutants I found two crates for mutation testing in Rust: cargo-mutants
and mutagen mutagen hasn't been maintained for three years, while cargo-mutants is still under active development. I've ported the sample code from my previous Java code to Rust: struct LowPassPredicate {
    threshold: i32,
}

impl LowPassPredicate {
    pub fn new(threshold: i32) -> Self {
        LowPassPredicate { threshold }
    }

    pub fn test(&self, value: i32) -> bool {
        value < self.threshold
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn should_return_true_when_under_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(4), true);
    }

    #[test]
    fn should_return_false_when_above_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(6), false);
    }
} Using cargo-mutants is a two-step process: Install it, cargo install --locked cargo-mutants
Use it, cargo mutants Found 4 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
4 mutants tested in 1s: 4 caught I expected a mutant to survive, as I didn't test the boundary when the test value equals the limit. Strangely enough, cargo-mutants didn't detect it. Finding and Fixing the Issue I investigated the source code and found the place where it mutates operators: // We try replacing logical ops with == and !=, which are effectively
// XNOR and XOR when applied to booleans. However, they're often unviable
// because they require parenthesis for disambiguation in many expressions.
BinOp::Eq(_) => vec![quote! { != }],
BinOp::Ne(_) => vec![quote! { == }],
BinOp::And(_) => vec![quote! { || }],
BinOp::Or(_) => vec![quote! { && }],
BinOp::Lt(_) => vec![quote! { == }, quote! {>}],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}],
BinOp::Le(_) => vec![quote! {>}],
BinOp::Ge(_) => vec![quote! {<}],
BinOp::Add(_) => vec![quote! {-}, quote! {*}], Indeed, < is changed to == and >, but not to <=. I forked the repo and updated the code accordingly: BinOp::Lt(_) => vec![quote! { == }, quote! {>}, quote!{ <= }],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}, quote!{ => }], I installed the new forked version: cargo install --git https://github.com/nfrankel/cargo-mutants.git --locked I reran the command: cargo mutants The output is the following: Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
MISSED   src/lib.rs:11:15: replace < with <= in LowPassPredicate::test in 0.2s build + 0.2s test
5 mutants tested in 2s: 1 missed, 4 caught You can find the same information in the missed.txt file. I thought I fixed it and was ready to make a Pull Request to the cargo-mutants repo. I just needed to add the test at the boundary: #[test]
fn should_return_false_when_equals_limit() {
    let low_pass_predicate = LowPassPredicate::new(5);
    assert_eq!(low_pass_predicate.test(5), false);
} cargo test running 3 tests
test tests::should_return_false_when_above_limit ... ok
test tests::should_return_false_when_equals_limit ... ok
test tests::should_return_true_when_under_limit ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s cargo mutants And all mutants are killed! Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.2s test
 INFO Auto-set test timeout to 20s
5 mutants tested in 2s: 5 caught Conclusion Not many blog posts end with a Pull Request, but this one does: https://github.com/sourcefrog/cargo-mutants/pull/501?embedable=true Unfortunately, I couldn't manage to make the tests pass; fortunately, the repository maintainer helped me–a lot. The Pull Request is merged: enjoy this slight improvement. I learned more about cargo-mutants and could improve the code in the process. To go further: Mutation testing
Welcome to cargo-mutants
GitHub cargo-mutants Originally published at A Java Geek on March 30th, 2025 I've been a big fan of Mutation Testing since I discovered PIT . As I dive deeper into Rust, I wanted to check the state of mutation testing in Rust. Mutation Testing PIT Starting with cargo-mutants cargo-mutants I found two crates for mutation testing in Rust: cargo-mutants and mutagen cargo-mutants cargo-mutants and mutagen mutagen mutagen hasn't been maintained for three years, while cargo-mutants is still under active development. mutagen cargo-mutants I've ported the sample code from my previous Java code to Rust: struct LowPassPredicate {
    threshold: i32,
}

impl LowPassPredicate {
    pub fn new(threshold: i32) -> Self {
        LowPassPredicate { threshold }
    }

    pub fn test(&self, value: i32) -> bool {
        value < self.threshold
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn should_return_true_when_under_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(4), true);
    }

    #[test]
    fn should_return_false_when_above_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(6), false);
    }
} struct LowPassPredicate {
    threshold: i32,
}

impl LowPassPredicate {
    pub fn new(threshold: i32) -> Self {
        LowPassPredicate { threshold }
    }

    pub fn test(&self, value: i32) -> bool {
        value < self.threshold
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn should_return_true_when_under_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(4), true);
    }

    #[test]
    fn should_return_false_when_above_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(6), false);
    }
} Using cargo-mutants is a two-step process: cargo-mutants Install it, cargo install --locked cargo-mutants Use it, cargo mutants Install it, cargo install --locked cargo-mutants cargo install --locked cargo-mutants Use it, cargo mutants cargo mutants Found 4 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
4 mutants tested in 1s: 4 caught Found 4 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
4 mutants tested in 1s: 4 caught I expected a mutant to survive, as I didn't test the boundary when the test value equals the limit. Strangely enough, cargo-mutants didn't detect it. cargo-mutants Finding and Fixing the Issue I investigated the source code and found the place where it mutates operators: // We try replacing logical ops with == and !=, which are effectively
// XNOR and XOR when applied to booleans. However, they're often unviable
// because they require parenthesis for disambiguation in many expressions.
BinOp::Eq(_) => vec![quote! { != }],
BinOp::Ne(_) => vec![quote! { == }],
BinOp::And(_) => vec![quote! { || }],
BinOp::Or(_) => vec![quote! { && }],
BinOp::Lt(_) => vec![quote! { == }, quote! {>}],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}],
BinOp::Le(_) => vec![quote! {>}],
BinOp::Ge(_) => vec![quote! {<}],
BinOp::Add(_) => vec![quote! {-}, quote! {*}], // We try replacing logical ops with == and !=, which are effectively
// XNOR and XOR when applied to booleans. However, they're often unviable
// because they require parenthesis for disambiguation in many expressions.
BinOp::Eq(_) => vec![quote! { != }],
BinOp::Ne(_) => vec![quote! { == }],
BinOp::And(_) => vec![quote! { || }],
BinOp::Or(_) => vec![quote! { && }],
BinOp::Lt(_) => vec![quote! { == }, quote! {>}],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}],
BinOp::Le(_) => vec![quote! {>}],
BinOp::Ge(_) => vec![quote! {<}],
BinOp::Add(_) => vec![quote! {-}, quote! {*}], Indeed, < is changed to == and > , but not to <= . I forked the repo and updated the code accordingly: < == > <= BinOp::Lt(_) => vec![quote! { == }, quote! {>}, quote!{ <= }],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}, quote!{ => }], BinOp::Lt(_) => vec![quote! { == }, quote! {>}, quote!{ <= }],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}, quote!{ => }], I installed the new forked version: cargo install --git https://github.com/nfrankel/cargo-mutants.git --locked cargo install --git https://github.com/nfrankel/cargo-mutants.git --locked I reran the command: cargo mutants cargo mutants The output is the following: Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
MISSED   src/lib.rs:11:15: replace < with <= in LowPassPredicate::test in 0.2s build + 0.2s test
5 mutants tested in 2s: 1 missed, 4 caught Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
MISSED   src/lib.rs:11:15: replace < with <= in LowPassPredicate::test in 0.2s build + 0.2s test
5 mutants tested in 2s: 1 missed, 4 caught You can find the same information in the missed.txt file. I thought I fixed it and was ready to make a Pull Request to the cargo-mutants repo. I just needed to add the test at the boundary: missed.txt cargo-mutants #[test]
fn should_return_false_when_equals_limit() {
    let low_pass_predicate = LowPassPredicate::new(5);
    assert_eq!(low_pass_predicate.test(5), false);
} #[test]
fn should_return_false_when_equals_limit() {
    let low_pass_predicate = LowPassPredicate::new(5);
    assert_eq!(low_pass_predicate.test(5), false);
} cargo test cargo test running 3 tests
test tests::should_return_false_when_above_limit ... ok
test tests::should_return_false_when_equals_limit ... ok
test tests::should_return_true_when_under_limit ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s running 3 tests
test tests::should_return_false_when_above_limit ... ok
test tests::should_return_false_when_equals_limit ... ok
test tests::should_return_true_when_under_limit ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s cargo mutants cargo mutants And all mutants are killed! Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.2s test
 INFO Auto-set test timeout to 20s
5 mutants tested in 2s: 5 caught Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.2s test
 INFO Auto-set test timeout to 20s
5 mutants tested in 2s: 5 caught Conclusion Not many blog posts end with a Pull Request, but this one does : does https://github.com/sourcefrog/cargo-mutants/pull/501?embedable=true https://github.com/sourcefrog/cargo-mutants/pull/501?embedable=true Unfortunately, I couldn't manage to make the tests pass; fortunately, the repository maintainer helped me–a lot. The Pull Request is merged: enjoy this slight improvement. I learned more about cargo-mutants and could improve the code in the process. cargo-mutants To go further: To go further: Mutation testing Welcome to cargo-mutants GitHub cargo-mutants Mutation testing Mutation testing Welcome to cargo-mutants Welcome to cargo-mutants GitHub cargo-mutants GitHub cargo-mutants Originally published at A Java Geek on March 30th, 2025 Originally published at A Java Geek on March 30th, 2025 A Java Geek

Mutation Testing: How Does it Work in Rust?

Scientists Fuse Human Brain Cells With Chips: Is This the Birth of Living AI?

Let’s get something out of the way: yes, AI is impressive. It codes, writes, analyzes, and conducts PhD-level research. It’s helping developers write cleaner code, marketers generate endless content variations, and analysts crunch numbers with new insight and scale. But if you're anxiously refreshing LinkedIn to see if GPT-5 is coming for your job title—you're looking in the wrong direction. It’s not AI you need to worry about. It’s the AI natives. You know, the ones who don’t remember life before autocomplete. The ones who ask ChatGPT for a study guide and then feed the results into Notion, auto-tagged and summarized. The ones who graduated during the pandemic, taught themselves to prompt like pros, and now treat GPTs and AI copilots as just another browser tab. They graduate, onboard, and start deploying code faster than you can schedule a kickoff meeting. They’re not learning AI—they’re thinking with it. And here's the uncomfortable bit: behavioral science backs them up. Decades of research—from literacy studies to cognitive psychology research —tell us the same thing. Learning gets harder as we age. Neural plasticity decreases. Resistance to habit change increases. TL;DR: it’s not your fault you’re not jumping on every new AI tool. But it is your problem. is Most of us don’t hate change—we hate the effort that change demands. Changing your tooling and workflows takes time, and time is in short supply when you're dealing with legacy systems, corporate red tape, or just trying to close out the next sprint. Meanwhile, the AI natives are iterating. They're building entire MVPs with two people and a stack of AI-powered tools. They don’t need to know everything—they just need to know how to ask the right question. Let’s zoom in on software engineering. Google recently said AI helps generate about 20% of its code. Pause on that. One in five lines—written or assisted by a machine. That’s not hype. That’s infrastructure. This isn’t about marginal gains in efficiency—it’s a foundational shift. Yet, many senior engineers at legacy companies are still babysitting old codebases, skeptical of Copilot, or too deep in Jira tickets to explore new ways of working. Here’s the kicker: AI doesn’t need you to catch up. It just needs someone who knows how to use it. Give a junior engineer trained in AI-native tooling access to your legacy stack, and they’ll optimize it, refactor it, and deploy it before the next standup. Why? Because they’re not weighed down by the sacred cows of "how it’s always been done." They don’t see AI as optional—they see it as baseline. The same way you don’t question using Google Docs instead of a typewriter. They’re using tools like Codeium, Replit, Cursor, and even custom-trained LLMs to auto-document, auto-test, auto-deploy, and even suggest architectural changes. They’re not afraid of breaking things. They assume everything is fixable—especially when an AI assistant is watching their back. They’re also not hung up on the usual "buts"—you know, the excuses for not diving into AI: it’s not open source, it doesn’t run on our backend, it hallucinates, it’s too new, too risky, too unproven. Sure, those concerns are valid. But none of them are deal breakers. And the AI natives know that. They don’t see blockers; they see workarounds. This pattern isn’t unique to software. It’s playing out across every knowledge-based industry. In marketing, AI natives are using generative tools to create personalized content at scale, run A/B tests faster, and adapt to signals in real time. In law, they’re using AI to draft contracts, parse regulatory filings, and prep case briefs with a few smart prompts. In medicine, new doctors are learning to cross-reference diagnoses with LLMs trained on up-to-date medical literature. Even in finance, junior analysts are automating repetitive modeling and accelerating research cycles with the help of AI. AI tools are redefining the value of domain expertise. Institutional knowledge used to be your moat. Now it’s the thing slowing you down. Experience still matters—but only when it’s paired with adaptation. Knowing how things used to work is no longer enough. You need to understand how things could work now. The competitive edge has shifted from accumulated knowledge to the agility to integrate new tools. used to work could work And while most of the AI hype has been centered around job loss, the truth is more nuanced. We’re not headed toward an AI apocalypse. We’re entering a phase of accelerated displacement—where those who know how to wield AI outperform those who don’t. Where small teams with the right stack outperform big teams running on inertia. It’s not AI replacing jobs. It’s AI-savvy people doing the work of five. So if you’re worried about AI taking your job, you’re already a few steps behind. The real threat? The Gen-Z grad who prompt-engineers in their sleep, spins up custom GPTs for fun, builds automations on the weekend, and doesn’t remember how work functioned without a dozen AI tools in the stack. Your only defense right now is that most of them still don’t know how to work a Windows PC. This isn’t man vs. machine. It’s old habits vs. new leverage. And right now, leverage is compounding—fast. The gap between those who use AI and those who don’t isn’t just widening. It’s becoming a chasm. The AI natives aren’t waiting. They’re building, optimizing, and shipping. Adapt accordingly—or get left behind.

AI Isn’t Coming for Your Job—Natives Are

AI ne dolazi za vaš posao – domoroci jesu

La IA no viene a quitarte el trabajo: lo harán los nativos

AI ne vient pas pour votre travail - les autochtones sont

AI לא בא לעבודה שלך - הילידים הם

Az AI nem jön a munkádra - a bennszülöttek

AIがあなたの仕事を奪うのではなく、ネイティブが奪う

AI nenāk jūsu darbam - vietējie ir

AI komt niet voor je baan - Natives zijn

AI não está vindo para o seu trabalho - os nativos são

AI ඔබේ රැකියාවට එන්නේ නැහැ - ස්වදේශිකයෝත් එනවා

AI Haisi Kuuya Kubasa Rako - VeNative

AI kommer inte för ditt jobb - infödda är

Every year, C-suite executives need to wade through endless forecasts to find technology that actually moves the needle. ELEKS's tech experts have dissected predictions from Gartner, IDC, Deloitte, and KPMG to separate signal from noise. But we didn't just compile a list—we applied our 30+ years of engineering expertise to evaluate each trend's real deployment risk and business value. Artificial Intelligence: No Longer Just Hype AI is becoming as fundamental as electricity or the internet. Both Gartner and Deloitte agree that in 2025, it's only getting bigger, especially with generative AI. It's evolving from a mere automation tool to the cornerstone of business strategy. generative AI Generative AI: Beyond the Chatbot ELEKS' expert evaluation: deployment risk: low; business value: high ELEKS' expert evaluation: deployment risk: low; business value: high low high The most significant shift is how GenAI is maturing and embedding itself in enterprise operations. It's not just about shiny new systems; it's also about breathing life into expensive legacy systems. According to Gartner, by 2028, generative AI will reduce the cost of modernizing legacy applications by 30% compared to 2023 levels. What's most interesting is how GenAI is changing workplace knowledge flow. Instead of digging through docs or bothering colleagues, workers can get answers instantly. Gartner reports that 75% of developers will use generative AI for learning new skills. Business benefits you can actually bank on: Business benefits you can actually bank on: ⚡ Efficiency boost: Let AI handle the boring stuff so humans can do the creative thinking 💰 Cost optimization: Automate processes, modernise legacy systems, and manage resources better 😎 Customer experience: AI-driven personalisation and 24/7 support that doesn't suck 🚀 Faster innovation: Test concepts and build prototypes faster, reducing time-to-market AI Agents: The Next Evolution ELEKS' expert evaluation: deployment risk: low; business value: high ELEKS' expert evaluation: deployment risk: low; business value: high low high Agentic AI should be the bullseye of our focus in 2025. Multi-agent workflows in particular, which are highly complex and require governance, should present a rich opportunity. — Alex Shegda, ELEKS’ VP of Technology Agentic AI should be the bullseye of our focus in 2025. Multi-agent workflows in particular, which are highly complex and require governance, should present a rich opportunity. — Alex Shegda, ELEKS’ VP of Technology Agentic AI should be the bullseye of our focus in 2025. Multi-agent workflows in particular, which are highly complex and require governance, should present a rich opportunity. — Alex Shegda, ELEKS’ VP of Technology Forget basic chatbots—enterprise AI agents are becoming sophisticated business tools. Here's what's coming: Increased autonomy: AI systems working as teams to manage complex tasks like supply chains without constant human babysitting
Superagents: Advanced multiagents that coordinate other AI systems across industries
Multimodal capabilities: Systems that can understand text, images, video, and 3D simultaneously
Spatial computing integration: AI that understands our natural behaviors without requiring specialized commands Increased autonomy: AI systems working as teams to manage complex tasks like supply chains without constant human babysitting Superagents: Advanced multiagents that coordinate other AI systems across industries Multimodal capabilities: Systems that can understand text, images, video, and 3D simultaneously Spatial computing integration: AI that understands our natural behaviors without requiring specialized commands Investors have poured over $2 billion into agentic AI startups in the past two years, focusing their investment on companies that target the enterprise market, says Deloitte. According to IDC, 50% of organizations will use enterprise agents configured for specific business functions instead of focusing on individual copilot technologies to achieve faster business value from AI. Business benefits of agentic AI: Business benefits of agentic AI: 🤖 Next-level automation: These agents don't just respond—they act independently based on goals 🧠 Smarter decisions: They collaborate and make independent decisions while allowing human oversight 🔄 Enhanced coordination: Superagents optimize how AI systems work together 🌐 Environmental adaptability: Combined with spatial computing, they understand context naturally 📱 LLMs optimisation: Enable large language models to run on small devices and offline Small Language Models: When Bigger Isn't Better ELEKS' expert evaluation: deployment risk: low; business value: high ELEKS' expert evaluation: deployment risk: low; business value: high low high We're seeing a shift toward smaller, specialized AI models designed for specific business needs. It's not about massive AI projects anymore—it's about practical integration into existing operations. Why SLMs Matter: Why SLMs Matter: 🚄 Efficiency: LLMs are resource hogs and overkill for simple tasks 🎯 Specialisation: SLMs can be trained on specific data for specific problems 🔒 Data privacy: Run on-device, reducing cloud dependency 🌱 Sustainability: Lower computing needs = smaller carbon footprint One challenge stands out in ~80% of cases related to modern GenAI technologies: the i/o validation of LLM-based agents. This leads to problems like users bypassing system instructions or retrieving hidden data through indirect prompt injections. Unfortunately, there's no single, effective solution yet. — Volodymyr Getmanskyi, Head of Data Science at ELEKS One challenge stands out in ~80% of cases related to modern GenAI technologies: the i/o validation of LLM-based agents. This leads to problems like users bypassing system instructions or retrieving hidden data through indirect prompt injections. Unfortunately, there's no single, effective solution yet. — Volodymyr Getmanskyi, Head of Data Science at ELEKS One challenge stands out in ~80% of cases related to modern GenAI technologies: the i/o validation of LLM-based agents. This leads to problems like users bypassing system instructions or retrieving hidden data through indirect prompt injections. Unfortunately, there's no single, effective solution yet. — Volodymyr Getmanskyi, Head of Data Science at ELEKS Cloud Computing: Evolution, Not Revolution The cloud landscape is transforming rapidly, bringing both new opportunities and challenges. FinOps: The CFO's New Best Friend ELEKS' expert evaluation: deployment risk: medium; business value: medium ELEKS' expert evaluation: deployment risk: medium; business value: medium medium medium Global cloud spending is expected to exceed $825 billion in 2025, as stated by Gartner. However, tracking and understanding cloud costs remain difficult. That's where FinOps comes in—tools and strategies that give you control over cloud spending. According to Deloitte, businesses could save up to $21 billion in 2025 by adopting FinOps tools and practices, with potential reductions of up to 40% in some cases. What's notable for 2025 is the shift from reactive cost management to proactive value optimisation. When properly implemented, FinOps provides C-suite leaders with real-time visibility into cloud ROI—something notably absent in traditional approaches. — Mykola Orlov, Head of DevOps at ELEKS What's notable for 2025 is the shift from reactive cost management to proactive value optimisation. When properly implemented, FinOps provides C-suite leaders with real-time visibility into cloud ROI—something notably absent in traditional approaches. — Mykola Orlov, Head of DevOps at ELEKS What's notable for 2025 is the shift from reactive cost management to proactive value optimisation. When properly implemented, FinOps provides C-suite leaders with real-time visibility into cloud ROI—something notably absent in traditional approaches. — Mykola Orlov, Head of DevOps at ELEKS FinOps benefits: FinOps benefits: 📊 Optimize resources: Ensure maximum value from every cloud dollar 💵 Cost transparency: See spending across services, regions, and accounts 📜 Regulatory compliance: Easier management of rules and requirements 🏃 Business agility: Align infrastructure with business objectives Hybrid Clouds ELEKS' expert evaluation: deployment risk: medium; business value: medium ELEKS' expert evaluation: deployment risk: medium; business value: medium medium medium According to Gartner, by 2025, 90% of organizations are expected to implement a hybrid cloud strategy. This isn't just about having both on-premises and cloud systems—it's about creating a seamless environment, optimising performance, cost, and security. Hybrid cloud advantages: Hybrid cloud advantages: 🔐 Better security: Keep sensitive data private while leveraging the public cloud for everything else 🎮 More control: Choose where and how applications run 🗺️ Data sovereignty: Keep sensitive data within specific geographic boundaries 🔄 IT flexibility: Allocate resources between private and public infrastructure as needed Edge Computing: The Need for Speed ELEKS' expert evaluation: deployment risk: low; business value: high ELEKS' expert evaluation: deployment risk: low; business value: high Edge computing continues to gain traction as businesses need faster data processing and real-time analytics. By 2025, edge computing is expected to process 55% of enterprise-generated data, according to Deloitte. Edge computing payoffs: Edge computing payoffs: ⚡ Instant processing: No more waiting for data to travel to distant servers and back 🔌 Smarter device management: Factory sensors and retail cameras work better than ever 🔏 Data privacy compliance: When data stays local, you worry less about where it travels 🌱 Sustainability: Reduced energy consumption and waste The best part? Edge devices now come with built-in AI capabilities (NPUs), making decisions without always checking in with the cloud. Think of it as having mini-computers everywhere, handling their own tasks while staying connected when needed. Cybersecurity Cybersecurity isn't just about protecting data—it's about staying competitive. — Oleksandr Pluzhnikov, Head of Cyber Security at ELEKS Cybersecurity isn't just about protecting data—it's about staying competitive. — Oleksandr Pluzhnikov, Head of Cyber Security at ELEKS The Evolving Threat Landscape AI integration in cybersecurity is a double-edged sword. While businesses use it for defense, attackers use GenAI for increasingly sophisticated attacks, creating convincing phishing campaigns and adaptive malware. Deepfakes make it harder to distinguish authentic communications from fraudulent ones. Ransomware remains one of the most significant threats, evolving from simple encryption attacks to sophisticated operations combining data theft with encryption. Compromised credentials create ripple effects throughout interconnected networks, especially in environments without robust multi-factor authentication. Defense mechanisms that actually work Defense mechanisms that actually work 40% of businesses will adopt DIY security solutions through AI-enabled automation, according to IDC. Companies are implementing Zero Trust Network Access (ZTNA) while security operations evolve toward semi-autonomous models where AI assists with alerts and threat prioritization. Cloud-native security information and event management (SIEM) solutions address unique cloud challenges, and organizations prepare for quantum computing threats with post-quantum cryptography. Success requires: Developing robust AI impact assessments
Building crypto-agile infrastructures
Fostering collaboration between security, technology, and legal teams Developing robust AI impact assessments Building crypto-agile infrastructures Fostering collaboration between security, technology, and legal teams Sustainability: Green Is the New Black In 2025, sustainability will take center stage across sectors, driven by climate change urgency, regulatory pressures, and the energy demands of AI and other technologies. By 2026, 60% of organizations are expected to use generative AI to create more detailed and operationally focused strategies for sustainable transformation. By 2027, 25% of organizations will use AI platforms to create digital twins of their supply chains, enabling the analysis of potential climate and weather-related risks. – IDC By 2026, 60% of organizations are expected to use generative AI to create more detailed and operationally focused strategies for sustainable transformation. By 2027, 25% of organizations will use AI platforms to create digital twins of their supply chains, enabling the analysis of potential climate and weather-related risks. – IDC Carbon-Free Energy & Sustainable Operations The intersection of energy transformation and business sustainability in 2025 is remarkable. As renewable power gains traction and nuclear energy addresses AI's growing demands, we're seeing a fundamental shift in energy strategies. GreenOps exemplifies this—companies aren't just tracking costs, they're integrating energy efficiency and carbon metrics into core business decisions. — Lyubomyr Matsekh, Sustainability Practice Lead at ELEKS The intersection of energy transformation and business sustainability in 2025 is remarkable. As renewable power gains traction and nuclear energy addresses AI's growing demands, we're seeing a fundamental shift in energy strategies. GreenOps exemplifies this—companies aren't just tracking costs, they're integrating energy efficiency and carbon metrics into core business decisions. — Lyubomyr Matsekh, Sustainability Practice Lead at ELEKS The intersection of energy transformation and business sustainability in 2025 is remarkable. As renewable power gains traction and nuclear energy addresses AI's growing demands, we're seeing a fundamental shift in energy strategies. GreenOps exemplifies this—companies aren't just tracking costs, they're integrating energy efficiency and carbon metrics into core business decisions. — Lyubomyr Matsekh, Sustainability Practice Lead at ELEKS The energy sector is transforming. Wind and solar have surpassed coal in annual US electricity generation, driven by customer demand, government regulations, and strategic investments. Nuclear energy is experiencing a renaissance, particularly for powering AI data centers. Small Modular Reactors (SMRs) and Advanced Modular Reactors offer promising solutions for clean, reliable power. "GreenOps" has emerged as an evolution of FinOps, introducing sustainability metrics into cloud management. Companies measure not just costs but also carbon footprint, making environmental impact a key factor in technology decisions. Regulations With Real Teeth The regulatory environment in 2025 has tightened, with the EU leading. The Digital Product Passport requires transparent records of products' environmental impact. The Corporate Sustainability Reporting Directive (CSRD) integrates sustainability into financial reporting, while the Sustainable Finance Disclosure Regulation (SFDR) ensures environmental risks are properly accounted for in financial practices. Companies are responding globally by implementing AI-informed carbon accounting software and integrating sustainability requirements into procurement processes. Understanding which technology trends hold genuine potential for your business requires more than market research. Drawing on over 30 years of software industry experience, our certified experts help you evaluate emerging technologies against your specific business context. Book a strategy session with the expert! Book a strategy session with the expert! Book a strategy session with the expert!

A Pragmatic Look at 2025 Technology Trends: What You Need to Know

2025-ci ilin Texnologiya Trendlərinə Praqmatik Baxış: Nəyi Bilməlisiniz

Pragmatický pohled na technologické trendy 2025: Co potřebujete vědět

Una mirada pragmática a las tendencias tecnológicas de 2025: lo que necesita saber

Unha ollada pragmática ás tendencias tecnolóxicas de 2025: o que debes saber

Egy pragmatikus pillantás a 2025-ös technológiai trendekre: mit kell tudni

2025 年のテクノロジートレンドの実際的な考察: 知っておくべきこと

პრაგმატული მიმოხილვა 2025 წლის ტექნოლოგიების ტენდენციებზე: რა უნდა იცოდეთ

2050 жылы «Технологиялық тенденциясы» – бұл нәрсе

ERROR: invalid target_lang (qu)

2025 ٹیکنالوجی کے رجحانات پر ایک عملی نظر: آپ کو کیا جاننے کی ضرورت ہے۔

O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z

Cái nhìn thực tế về xu hướng công nghệ năm 2025: Những điều bạn cần biết

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