Tesla, the visionary leader in the electric vehicle (EV) industry, continues to push the boundaries of technology, setting new benchmarks in artificial intelligence (AI) and energy innovation. As we look toward 2025, Tesla’s impact is becoming more profound across multiple sectors. From self-driving cars to transforming the global energy landscape, Tesla is at the forefront of creating a smarter, cleaner, and more sustainable future. Here’s a closer look at how Tesla is revolutionizing AI and energy this year, shaping industries, and paving the way for the future.Tesla’s Groundbreaking Advancements in AI1. Enhanced Autonomous Driving and Full Self-Driving (FSD) CapabilitiesTesla’s Full Self-Driving (FSD) technology has taken a monumental leap forward in 2025. With more refined algorithms and advanced neural networks, Tesla’s vehicles are now capable of handling more complex driving situations. The latest developments include:Improved Contextual Awareness: Tesla’s AI can now understand and navigate through intricate urban environments, including intersections, construction zones, and pedestrian-heavy areas, making the cars safer and more efficient in city driving.Advanced Decision-Making: Tesla’s self-driving system has become far more adept at making real-time decisions in high-stress situations, such as emergency braking, lane merging, and adaptive speed control.Safety and Redundancy: With multiple layers of redundancy and enhanced safety protocols, Tesla’s AI reduces the chances of accidents, even under unusual driving conditions. The company’s FSD technology boasts an accident rate significantly lower than that of human drivers.Predictive Path Management: Tesla’s vehicles now predict the paths of other vehicles and pedestrians with remarkable accuracy, reducing accidents caused by sudden movements or obstacles.2. Tesla’s Dojo Supercomputer: Powering the Future of AITesla’s Dojo, a powerful AI training supercomputer, has achieved new levels of performance and efficiency in 2025. This breakthrough system is designed to process the massive amounts of data generated by Tesla’s fleet of vehicles, enabling faster, more accurate AI model training.Data Processing at Scale: Dojo’s neural networks are capable of processing petabytes of driving data collected globally, accelerating the development of Tesla’s self-driving capabilities.Energy Efficiency: While Dojo is one of the most powerful supercomputers in existence, it is also optimized for energy efficiency, ensuring that the training of AI models has a smaller environmental footprint compared to traditional supercomputing methods.Beyond Automotive: In addition to advancing Tesla’s autonomous driving technology, Dojo’s computational power is being applied to other industries, including robotics, healthcare AI, and energy systems, further expanding Tesla’s influence in the AI space.3. Tesla AI in Robotics and AutomationTesla’s AI technology isn’t just limited to vehicles; it has also expanded into robotics. The Tesla Bot, initially a prototype, has now become a practical, functional solution in 2025.Industrial and Household Use: The Tesla Bot is already being employed in Tesla’s factories for various assembly and maintenance tasks, and early versions are being tested in homes for tasks like cleaning, assistance with daily chores, and even elderly care.AI-Driven Productivity: Tesla’s AI-driven robots are transforming industries by performing tasks with greater precision, efficiency, and safety than ever before. As AI technology evolves, the Tesla Bot could become a common presence in workplaces and homes around the world.Tesla’s Impact on the Global Energy Revolution1. Energy Storage Solutions: Powering a Clean FutureTesla’s commitment to sustainable energy solutions continues to expand, and the company’s energy storage products have seen significant improvements in 2025. The Powerwall, Powerpack, and Megapack are at the heart of Tesla’s mission to revolutionize energy storage and distribution.Powerwall 4.0: The latest iteration of Tesla’s residential battery storage system offers even more capacity and energy efficiency, helping homeowners become completely energy-independent. The Powerwall 4.0 seamlessly integrates with solar systems, providing constant power even during grid outages.Megapack Deployments: Tesla’s Megapack, an industrial-scale battery solution, is being deployed globally to help stabilize power grids and reduce the reliance on fossil fuels. The storage capacity of the Megapack has been significantly expanded, allowing utilities to store excess energy generated from renewable sources and release it during peak demand times.2. Solar Energy: Tesla’s Push for a Sustainable FutureTesla’s Solar Roof technology is reshaping the way people generate and use solar energy. In 2025, the adoption of solar energy is reaching new heights, as Tesla continues to innovate with:Enhanced Solar Efficiency: Tesla’s solar panels are now more efficient than ever, absorbing more sunlight and converting it into usable electricity with minimal loss. This results in greater energy production, making solar a more viable option for homeowners and businesses alike.Aesthetic Integration: Tesla’s Solar Roof tiles are designed to seamlessly integrate into the aesthetics of a home, providing clean energy without sacrificing style. The latest models offer improved durability and a sleeker, more streamlined appearance.Global Solar Growth: Tesla’s solar division has expanded into more international markets, including emerging economies, where access to clean energy solutions is critical. With competitive pricing and efficient systems, Tesla is driving the global shift toward renewable energy.3. Supercharger Network and Renewable Energy IntegrationTesla’s Supercharger Network continues to grow and evolve, further solidifying Tesla’s leadership in electric mobility and clean energy solutions.Supercharger V5: The newest version of Tesla’s Superchargers can charge vehicles at speeds of up to 350 kW, reducing charge times to under 10 minutes. These chargers are powered by 100% renewable energy, making them an eco-friendly solution for EV owners.Universal Charging Compatibility: Tesla’s Supercharger V5 stations are now compatible with a wide range of electric vehicles, ensuring that the transition to electric mobility is smoother for all users.Global Expansion: Tesla’s Supercharger network has expanded significantly across North America, Europe, and Asia, providing Tesla owners with access to fast, reliable charging infrastructure wherever they go.Tesla’s Vision for the Future1. AI and Clean Energy ConvergenceWhat sets Tesla apart is its ability to combine cutting-edge AI with sustainable energy solutions. By leveraging AI in its energy storage systems, EVs, and grid management, Tesla is creating a closed-loop ecosystem where clean energy generation, storage, and consumption are seamlessly integrated.2. Scalability for Global ImpactTesla’s technologies are designed for scalability. From small residential solar installations to large-scale battery systems for utilities, Tesla’s solutions can be deployed globally to meet the growing demand for clean, reliable energy. The widespread adoption of Tesla’s technologies will help mitigate the effects of climate change and reduce global dependence on fossil fuels.3. A Visionary Leader Driving ChangeUnder the leadership of Elon Musk, Tesla continues to innovate and challenge the status quo. With a vision focused on solving humanity’s greatest challenges—climate change, sustainable transportation, and AI safety—Tesla is positioning itself as a leader in the technological revolution of the 21st century.Conclusion: A Bright Future AheadTesla’s innovations in AI and energy in 2025 are setting the stage for a transformative future. From autonomous vehicles and AI-driven robotics to groundbreaking advancements in energy storage and solar technology, Tesla’s efforts are creating a more sustainable, intelligent world. As the company continues to push the boundaries of what is possible, it is clear that Tesla’s influence will only grow, helping to shape the future of technology and energy for years to come.Stay tuned to Techlistic.com for more insights into how companies like Tesla are leading the way in the ever-evolving world of technology and sustainability.This blog post reflects the latest trends in Tesla’s advancements and its significant impact on both AI and energy in 2025, ensuring your audience stays up-to-date with the most relevant and exciting information.
Software Engineering
The blog discusses how AI is reshaping financial risk management by enhancing efficiency, decision-making, and fraud detection. From market risk analysis to AML compliance, AI offers data-driven insights and automation to anticipate risks and secure operations. Despite challenges like costs and privacy concerns, AI-powered solutions drive innovation in risk management.
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In today’s digital world, leveraging the best Third Party Integration Service is critical for businesses and software development. These services help streamline operations, enhance user experiences, and promote growth. By incorporating external APIs and tools, businesses and developers can implement robust features such as payment gateways, customer relationship management (CRM), and analytics tools without building
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Implementing Microsoft Dynamics 365 can be transformative yet challenging for businesses. From data migration to integration with legacy systems, each step requires meticulous Quality Assurance (QA) to ensure smooth functionality, data integrity, and compliance. The blog discusses how QA in MS Dynamics 365 implementation is a crucial and continuous process critical to reducing risks and maximizing system performance.
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A Payroll Management System (PMS) is an indispensable asset for modern businesses, ensuring employee payments are accurate, timely, and fully compliant with both legal and organizational policies. These systems streamline complex processes such as salary calculations, tax deductions, benefits management, and adherence to labor laws, significantly reducing manual efforts and minimizing the risk of costly
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Artificial intelligence (AI) is revolutionizing the way we conduct software testing. ContentAutomation Testing with Selenium AI Testing has become an essential tool for ensuring the reliability and efficiency of web application testing. By combining AI with Selenium using Java, you can automate and enhance your testing process, making it smarter, faster, and more reliable. In
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The blog discusses how autonomous testing redefines software QA by leveraging AI and ML to enhance adaptability, scalability, and efficiency. Unlike traditional QA methods, autonomous testing minimizes human intervention and accelerates testing processes. With intelligent decision-making, self-healing capabilities, and broader test coverage, autonomous testing ensures flawless applications while reducing costs and time to market.
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What solutions do you use to avoid tight coupling between classes when using utility classes like waithelpers, data formatting, selenium utils etc? I am using these classes having static methods, which is not ideal in terms of coupling
Human Resource Management Systems, or HRMS, are essential for organizations. They make HR processes and hr tasks easier, especially during the hiring process. They also help keep everything compliant and manage employee data management well. This includes things like performance evaluations. An effective HRMS software application is important for testing. It makes sure the system
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I’ve got a C# framework using Playwright.NET and Reqnroll (formerly SpecFlow).
I’ve moved common steps across multiple projects into a separate project within the same solution to enable easy reuse.
I’ve referenced the binding assembly in the reqnroll.json file and the tests run fine.
All projects have the Reqnroll.NUnit NuGet package installed. I’d like to remove the package from all but the shared project – the dependency should mean that the other projects get the package from my shared project.
I’ve been able to do this with other packages, like Playwright – that’s only installed in the shared project and the other projects all get at it via the project dependencies.
But when I remove Reqnroll.NUnit, code I have in a [BeforeTestRun] hook does not run. [BeforeScenario] does but I need [BeforeTestRun] too. If I re-add the package to the project then [BeforeTestRun] works as normal. But the moment I remove it (keeping it in the shared project) then [BeforeTestRun] gets skipped.
Any idea why?
Testing an algorithm is really important. It ensures the algorithm works correctly. It also looks at how well it performs in various situations. Whether you are dealing with a sorting algorithm, a machine learning model, or a more complex one, a good testing process can find any issues before you start using it. Here’s a
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Exception in thread “main” org.openqa.selenium.SessionNotCreatedException: Could not start a new session. Possible causes are invalid address of the remote server or browser start-up failure.
Host info: host: ‘XXXXX’, ip: ‘XXXXX’
Build info: version: ‘4.26.0’, revision: ’69f9e5e’
System info: os.name: ‘Windows 11’, os.arch: ‘amd64’, os.version: ‘10.0’, java.version: ‘23.0.1’
Driver info: io.appium.java_client.android.AndroidDriver
Command: [null, newSession {capabilities=[Capabilities {app: C:UsersxxxxxxOneDriveDoc…, appActivity: app.superssmart.ui.MainActi…, appPackage: app.superssmart, browserName: , deviceName: emulator-5554, noReset: true, platformName: ANDROID}]}]
Capabilities {app: C:UsersxxxxOneDriveDoc…, appActivity: app.superssmart.ui.MainActi…, appPackage: app.superssmart, browserName: , deviceName: emulator-5554, noReset: true, platformName: ANDROID}
at org.openqa.selenium.remote.RemoteWebDriver.execute(RemoteWebDriver.java:563)
at io.appium.java_client.AppiumDriver.startSession(AppiumDriver.java:270)
at org.openqa.selenium.remote.RemoteWebDriver.<init>(RemoteWebDriver.java:174)
at io.appium.java_client.AppiumDriver.<init>(AppiumDriver.java:91)
at io.appium.java_client.AppiumDriver.<init>(AppiumDriver.java:103)
at io.appium.java_client.android.AndroidDriver.<init>(AndroidDriver.java:109)
at appium.test.App_Main.main(App_Main.java:29)
Caused by: java.lang.IllegalArgumentException: Illegal key values seen in w3c capabilities: [app, appActivity, appPackage, deviceName, noReset]
at org.openqa.selenium.remote.NewSessionPayload.lambda$validate$5(NewSessionPayload.java:163)
at java.base/java.util.stream.ReferencePipeline$15$1.accept(ReferencePipeline.java:580)
at java.base/java.util.stream.ReferencePipeline$15$1.accept(ReferencePipeline.java:581)
at java.base/java.util.stream.ReferencePipeline$3$1.accept(ReferencePipeline.java:215)
at java.base/java.util.stream.ReferencePipeline$15$1.accept(ReferencePipeline.java:581)
at java.base/java.util.ArrayList$ArrayListSpliterator.forEachRemaining(ArrayList.java:1709)
at java.base/java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:570)
at java.base/java.util.stream.AbstractPipeline.wrapAndCopyInto(AbstractPipeline.java:560)
at java.base/java.util.stream.ForEachOps$ForEachOp.evaluateSequential(ForEachOps.java:151)
at java.base/java.util.stream.ForEachOps$ForEachOp$OfRef.evaluateSequential(ForEachOps.java:174)
at java.base/java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:265)
at java.base/java.util.stream.ReferencePipeline.forEach(ReferencePipeline.java:636)
at org.openqa.selenium.remote.NewSessionPayload.validate(NewSessionPayload.java:167)
at org.openqa.selenium.remote.NewSessionPayload.<init>(NewSessionPayload.java:70)
at org.openqa.selenium.remote.NewSessionPayload.create(NewSessionPayload.java:99)
at org.openqa.selenium.remote.NewSessionPayload.create(NewSessionPayload.java:84)
at org.openqa.selenium.remote.ProtocolHandshake.createSession(ProtocolHandshake.java:60)
at io.appium.java_client.remote.AppiumCommandExecutor.createSession(AppiumCommandExecutor.java:176)
at io.appium.java_client.remote.AppiumCommandExecutor.execute(AppiumCommandExecutor.java:237)
at org.openqa.selenium.remote.RemoteWebDriver.execute(RemoteWebDriver.java:545)
… 6 more
Code:
public static AndroidDriver driver;
public static void main(String[] args) throws MalformedURLException, InterruptedException {
File appDir = new File(“C:\Users\keert\OneDrive\Documents\App”);
File app=new File(appDir,”app.apk”);
DesiredCapabilities cap=new DesiredCapabilities();
cap.setCapability(CapabilityType.BROWSER_NAME, “”);
cap.setCapability(“platformName”, “Android”);
cap.setCapability(“app”, app.getAbsolutePath());
cap.setCapability(“deviceName”, “emulator-5554”);
cap.setCapability(“appPackage”, “app.superssmart”);
cap.setCapability(“appActivity”, “app.superssmart.ui.MainActivity”);
cap.setCapability(“noReset”, true);
driver=new AndroidDriver(new URL(“http://127.0.0.1:4723/”),cap);
In today’s world, where mobile devices are becoming increasingly popular, ensuring your website and its various user interfaces and elements are accessible and functional on all screen sizes is more important than ever. Responsive web design (RWD) allows your website to adapt its layout to the size of the screen it is being viewed on,
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The blog discusses how predictive data analytics transforms quality assurance by enabling businesses to anticipate software issues and optimize testing. Leveraging AI, ML, and robust data models empowers QA teams to detect patterns, improve test case efficiency, and prioritize tasks for faster releases and superior outcomes.
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I’m currently working on a project that involves multiple services (some distributed across a cluster like Elasticsearch) running inside Docker containers. The whole system can be started through a docker-compose file with custom setup if required. To validate these services against specific requirements, I’ve set up a separate test project.
We’ve developed a comprehensive test suite to ensure the system functions as expected. However, one challenge remains: collecting logs from individual or multiple Docker services for a given test case. Ideally, I’d like to save the logs generated during each test run in the corresponding test results.
Upon researching common testing frameworks and practices, I identified similar scenarios:
Before running a test, reset the service’s logs
After completing a test, collect the logs
I’ve explored various possible solutions, including:
Using Docker Testcontainers: While this approach is promising, it requires custom-built services and integrating them, which seems overly complex.
Resetting all containers to clear their logs: This method is impractical due to the significant time it takes, given the large number of different services involved.
Truncating logs: Stopping a container, truncating its logs, and then restarting it works but can be slow.
Rotating logs: Could this be achieved through Docker or by utilizing a central log service?
I’m confident that others have encountered similar issues in the past. I would greatly appreciate any suggestions or ideas on how to address this problem.
The code review process is crucial in Software Development, guided by a checklist to ensure the software is high quality and meets standards. During the review, one or more team members examine the source code closely, looking for defects and verifying that it follows coding guidelines. This practice improves the entire codebase by catching bugs
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Generative AI is becoming the new norm, widely used and more accessible to the public via platforms like ChatGPT or Meta AI, which appear on social media platforms like WhatsApp and Instagram Messenger.
Despite its being fundamentally a transformers that break sentences into tokens and predict the next word, the implications and applications are vast. However, these GPT models currently lack human-like understanding. Which might cause reliability issues and others, but considering its capabilities the new trend of agentic AI is on rise this highlights the importance of having a well-defined testing approach.
I wanted to ask:
What are the patterns or testing strategies you are following beyond basic testing strategies?
What’s your approach to identify and fix, do you follow any checkmarks ?
AI Hallucination
Fairness and Bias
Security & Ethical Issue
Coherence and relevance
Robustness and Reliability
Explainability and Interpretability
Include others you have Identified
Here are some of my observations:
Example 1: AI Hallucination
Issue: Generating factually incorrect or nonsensical outputs, The response provided has data that is not reliable however its sounds plausible or true.
Solution: Fact-checking, Human-in-the-loop, Prompt engineering, Training data quality, Model fine-tuning, Post-processing
Example 2: Bias and Fairness
Issue: Based on the data, Generating outputs that unfairly favor certain groups.
Solution: Bias audits, Fairness metrics, Diverse training data
Example 3: Adherence to Instructions
Issue: With tools like Meta AI Agents and similar others in Salesforce, we need to check if the response adheres to the instructions, as sometimes it fails to follow the guidelines and guardrails.
Solution: It might be an issue with the instruction, but we need to go back to basics and test against each instruction to check if it is followed or not.
This might become hectic any alternate
Example 4: Not in Coherence Knowledge Article Boundaries
Issue: GPT models used as chatbots with a set of knowledge articles sometimes provide results outside the set of knowledge articles as a reference.
Solution: Coherence metrics, Prompt design, Feedback
Example 5: Chain of Thought
Issue: In some cases, the generative AI assumes continuity with earlier conversations within the window period, which might cause unnecessary references.
Solution: There should be instructions to cross-verify and provide a note.
Most of these issues can be addressed with effective prompt engineering. However, I am curious about your methods for breaking these issues and any observations you have identified.
The blog discusses the transformative capabilities of DevOps consulting services in accelerating product development, enhancing collaboration, and improving software quality. Learn how customized DevOps pipelines, automation, and continuous improvement drive digital evolution and keep organizations competitive in a fast-paced landscape.
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I DLed a password-protection supposed-.exe from FH — FolderLock — and it was flagged by Jotti with a good deal of malware, adware, then for giggles with VirusTotal and about half flagged it, including with ‘FileHippo’ in the name. I say ‘supposed’ because the DL ends with .exe, and File Explorer does say it’s an application, but the description is “FH Manager.”
I am testing application using screen reader NVDA. there is a checkbox, if i click on checkbox there will be drop down will be display and it has 2 option 1.Accept 2. Reject.
if I select Accept and tab on to next element and come back to current check box then if i click enter the there will be drop down with option 1. Reject 2. clear
My question when screen reader focus to checkbox what should be announcement ? and tab on to each option and when selecting accept then what should be the announcement ?