Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
When repairing a motherboard and its components such as the CPU (Central Processing Unit) and RAM (Random Access Memory), it's important to note that glue is generally not used in these processes. Glue is not a suitable or recommended material for securing or repairing electronic components on a motherboard. Instead, specialized techniques and tools are employed to ensure proper connections and stability.
Here are some guidelines for repairing a motherboard, CPU, and RAM:
Proper Handling: When working with delicate electronic components, handle them with care to prevent damage from static electricity. Use an anti-static wrist strap or mat to discharge static electricity before handling the components.
Troubleshooting and Diagnosis: Identify the specific issue with the motherboard, CPU, or RAM by troubleshooting and diagnosing the problem. This may involve checking for loose connections, damaged components, or faulty circuitry.
Component Replacement: If a component is found to be faulty or damaged, it may need to be replaced. This typically involves carefully removing the old component and installing a new one, following proper installation techniques and ensuring compatibility with the motherboard.
Thermal Paste Application: When installing or reseating a CPU, it's important to apply a thin layer of thermal paste between the CPU and the heatsink. This helps facilitate proper heat transfer and cooling. Clean off the old thermal paste using isopropyl alcohol before applying a fresh layer.
Secure Mounting: Ensure that the CPU, RAM modules, and other components are securely and correctly mounted in their respective slots or sockets on the motherboard. Follow the manufacturer's instructions and guidelines to ensure proper alignment and seating.
Power and Testing: Once the repairs are completed, reconnect all necessary cables and power sources to the motherboard. Perform thorough testing to verify that the repaired components are functioning properly and that any previous issues have been resolved.
It's important to note that motherboard repairs can be complex and delicate tasks. If you are not experienced or comfortable with handling motherboard repairs, it is recommended to seek assistance from a professional technician or an authorized service center to avoid further damage or complications.
Sometimes you want to play around with those new fancy features of JDK21 (or even newer
JDK's) like preview features and maybe some classes from the incubator.
So how can you configure your Maven build to support such a play lesson? It's easier than you
think. Let's start the configuration. My assumption is that you would like to play around
with preview features of JDK21 for example String Templates (JEP430). I just selected this JEP for demonstration. You can select whatever JEP is in the preview.
The first thing is to know that you have to activate the preview features via:
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<enablePreview>true</enablePreview>
</configuration>
</plugin>In the world of cloud computing, virtual machines (VMs) have revolutionized the way businesses operate by providing scalable and flexible computing resources. Among the multitude of VM options available, Azure B Series Virtual Machines stand out as a cost-effective and efficient choice for various workloads. In this article, we will delve into the features, benefits, and use cases of Azure B Series VMs, shedding light on why they are gaining popularity in the cloud computing landscape.
Azure B Series Virtual Machines is a unique offering from Microsoft Azure that provides burstable performance at a budget-friendly price. These VMs are designed to handle workloads that have varying resource requirements and can adapt to changing demands dynamically. With the ability to accumulate and consume CPU credits during periods of low usage, B Series VMs offer the advantage of bursting to higher CPU performance levels when needed, enabling them to deliver optimal performance at lower costs.
In recent years, cloud computing has become the new standard for enterprise applications. Cloud-native architecture has become a key concept in the software industry, providing an efficient way to develop, deploy, and manage applications in the cloud. Cloud-native patterns are a set of best practices for building and deploying cloud-native applications.
As more and more applications are moved to the cloud, it becomes increasingly important to design and build them in a way that takes full advantage of cloud computing. One approach that is gaining traction is cloud-native design, where applications are built specifically for deployment in cloud environments. Cloud Native Patterns are the building blocks of this approach, providing a set of best practices and principles for designing and building cloud-native applications.
In the world of software development, Python has emerged as a powerhouse programming language renowned for its versatility, efficiency, and simplicity. With its clear syntax and extensive library support, Python has gained popularity among developers, making it one of the top choices for building a wide range of applications. This article delves into the realm of Python software development, exploring its features, benefits, and practical use cases.
Python is a high-level, interpreted programming language that was developed by Guido van Rossum and released in 1991. It emphasizes code readability and maintainability, making it a preferred choice for both beginners and seasoned developers. Python's design philosophy emphasizes the use of simple and clear syntax, enabling programmers to express concepts with fewer lines of code compared to other languages.
Traditionally, security was often an afterthought in the software development process. The security measures were implemented late in the cycle or even after deployment. DevSecOps aims to shift security to the left. In DevSecOps, security is incorporated from the earliest stages of development and remains an integral part of the entire process.
The goal of DevSecOps is to create a culture where security is treated as everyone's responsibility rather than solely the responsibility of security teams. It encourages developers, operations personnel, and security professionals to work together, collaborate and automate security processes.
Microservice is an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. It is difficult to talk about microservices without talking about containers. These services are containerized and deployed on a container platform such as Docker.
Before exploring various services provided by Microsoft Azure, let’s quickly learn about the container. A container image has the software and its dependencies packaged into an immutable artifact. Each change in the Image forms a layer. Containerization helps developers to create and deploy applications faster and more securely.
In today's digital landscape, businesses heavily rely on content delivery networks (CDNs) to ensure efficient and reliable delivery of their web content to users across the globe. CDNs play a crucial role in enhancing website performance and user experience. However, the extended infrastructure of CDNs requires diligent monitoring to ensure optimal performance and identify potential issues. In this article, we will explore the importance of CDN observability and how it contributes to the success of online businesses.
CDN observability refers to gaining insights into the CDN infrastructure's performance, availability, and reliability. It involves monitoring and analyzing various metrics and data points to ensure the CDN functions as expected. By closely observing the CDN, businesses can proactively identify and resolve issues before they impact end-user experience.
Do you ever have those mornings where you sit down with your coffee, open your code base, and wonder who wrote this mess? And then it dawns on you — it was probably you. But don't worry, because now you can finally take out your frustrations and just strangle them! Complex, outdated applications plague many enterprises, if not all. They're looking for ways to modernize their applications and infrastructure to improve performance, reduce costs, and increase innovation. One strategy that works well in many cases is the Strangler Fig Approach.
The Strangler Fig Approach is a modernization strategy that involves gradually replacing complex software with a new system while maintaining the existing one's functionality. Its name comes from, well, the strangler fig tree. It grows around an existing tree, eventually replacing it while taking on the same shape and function. When compared to other methods of modernization, this approach can save a significant amount of time and money.
PostgreSQL, a robust and open-source relational database system, is renowned for its native replication mechanisms, logical replication, and physical replication (also known as streaming replication). PostgreSQL 16 includes numerous improvements in logical replication designed to enhance performance, bolster data consistency, and advance compatibility.
In the context of PostgreSQL, logical replication employs a publisher/subscriber model to mirror changes between PostgreSQL servers. The primary node, where the database resides, is the publisher, while the stand-by node, which receives transaction copies, is the subscriber. Changes in the database are replicated from the publisher node to one or more subscriber node(s) identified by the subscription.
Maybe you’ve noticed the world has dumped the internet, mobile, social, cloud, and even crypto in favor of an obsession with generative AI.
But is there more to generative AI than a fancy demo on Twitter? And how will it impact data?
In today's digital age, mobile applications have become an integral part of our daily lives, revolutionizing the way we interact, work, and entertain ourselves. Behind every successful mobile app lies a well-defined and meticulous development process. In this article, we will unravel the stages involved in mobile app development, providing insights into each step and guiding you through the journey of transforming your app idea into a reality.
The first stage of mobile app development is brainstorming and conceptualizing the idea. Identify the problem your app aims to solve, define your target audience, and outline the key features and functionalities.
Access control is a critical aspect of any cloud environment, ensuring that only authorized users and entities have appropriate access to resources. Amazon Web Services (AWS) provides a robust access control mechanism called Attribute-Based Access Control (ABAC). ABAC allows organizations to implement fine-grained access control policies based on various attributes, providing flexibility and enhanced security. In this article, we will explore the concept of ABAC in AWS, its key components, its benefits, and how to implement it within your AWS infrastructure effectively.
A tag refers to a key-value pair that is assigned to a resource in order to store metadata related to that resource. Each tag comprises a label containing a key and value.
While the User Experience practice has been expanding and will continue to balloon in the coming years, so have its sub-disciplines such as content strategy, operations, and user research. As the practice of UX Research matures, scalability will continue to be important in order to meet the rapid needs of iterative product development.
While there are several effective ways to scale user research, such as increasing researcher-to-designer ratios, leveraging big data and real-time analytics, or research democratization, one of the most effective methods is developing a Rapid Research program. In a Rapid Research program, teams are provided quick insight into key problems at an unprecedented operational speed.
Rapid Research-type support has been around for a while and has taken different shapes across different organizations. What remains true, however, is the goal to provide actionable insights from end-users at a quick pace that fits within product sprints and maintains pace with agile development practices.
In this article, I’m going to unpack what a Rapid Research program is, how to build one in your organization, and underscore the unique benefits that a program like this can provide to your team. Given that there is no singular ‘right way’ to scale insights or mature a user research practice, this outline is intended to provide building blocks and considerations that you may take in the context of the culture, opportunities, and challenges of your organization.
What Is Rapid Research?Rapid research is a relatively recent program where typical user research practices and operations are standardized and templatized to provide a consistent, repeatable cadence of insights. As the name suggests, a core requirement of a rapid research program is that it delivers quicker-than-average insights. In many teams, this means delivering research on a weekly cadence where a confluence of guardrails, templates, and requirements work to ensure a smooth and consistent process.
Programs like Rapid Research may be created out of a necessity to keep up with the pace of development while freeing the bandwidth of expert researchers’ time for more complex discovery work that often takes longer. A rapid research program can be a crucial component of any team’s insight ecosystem, balanced against solving different business problems with flexible levels of support.
In order to make research more rapid, teams may consider some research methodologies out of the question in their Rapid Research program. Methods such as longitudinal diary studies, surveys, or long-form interviews might suffer from lower quality if done too quickly. When determining the scope of your rapid research program, ask yourself what methods you can easily templatize and, most importantly, which best support the needs of your experience teams.
For example, if your experience teams work on 2-week sprints and need insights in that time, then you will need to consider which research methods can reliably be conducted in 1–2 week increments.
Methods alone won’t ensure a successful implementation of a rapid research program. You will also need to consider sample size and session duration. Even if you decide usability tests are a reasonable methodology for your rapid research framework, you may be introducing too much complexity to run them with 15+ users within 60-min sessions and analyze all that data efficiently. This may require you to narrow your focus to fewer sessions with shorter duration.
While there may be fewer and shorter sessions for each study, you also need to consider your participant pool. Recruitment is one of the most difficult aspects of conducting any user research, and this effort must be considered when determining the scope of the program. Recruitment can jeopardize the pace of your program if you source highly specific participants or if they are harder to reach due to internal bureaucracy or compliance constraints.
In order to simplify recruitment, consider what types of participants are both the easiest to reach and who account for the most use cases or products you expect to be researching. Be careful with this, though, as you don’t want to broaden your customer profiles too much for fear of not getting the helpful feedback you need, as UserZoom says:
“Why is sourcing participants such a challenge? Well, you could probably find as many users as you like by spreading the net as wide as possible and offering generous incentives, but you won’t necessarily find the ‘right’ participants.”
— UserZoom, “Four top challenges UX teams face in 2020 and how to solve them”
Coupled tightly with scope, the timing of your rapid research end-to-end process will be paramount to the program’s success. Even if you have narrowed the scope to only a handful of research methods with limited sessions at shorter durations and with specific participant profiles, it won’t be ‘rapid’ if your end-to-end project timeline is as long as your average traditional study. Care must be taken to ensure that the project timelines of your rapid research studies are notably quicker than your average studies so that this program feels differentiating and adds value on top of the work your team is already doing.
If your timelines are about the same, or your rapid cadence is less than 50% more efficient than your average study, consider whether or not you’re being judicious enough in your scope above. Always monitor your timelines and identify where you can speed things up or limit the scope in order to reach a quick turnaround, which is acceptable. One way to support shorter project timelines is through compartmentalization.
One way to balance scope, timing, and consistency is by breaking up pieces of your average study process into smaller, separate efforts. Consider what your program would look like if you separated project intake from the study kick-off or if discussion guides were not dependent on recruitment or participant types. Splitting out your workflow into separate parts and templating them may eliminate typical dependencies and streamline your processes.
Once you’ve determined the set of research methods and ideal participants to include in your program, you may:
There is a myriad of opportunities to do things differently than your typical research study when you reconsider the relationships and interdependencies in the process.
While a quality rapid research program takes into consideration scope, timing, and compartmentalization, it also needs to consider consistency. It would be difficult to discern whether or not the program was ‘rapid’ if, on one week, a study takes one week, and on another week, a study takes 2.5 weeks. Both may be below your current study average. However, project stakeholders may blur the lines between the differences in your rapid studies and your typical studies due to the variability in approach. In addition, it may be difficult to operationalize compartmentalization or rapid recruitment without some form of expected cadence.
As you and your team get used to operating within your rapid cadence, you may identify additional opportunities to templatize, compartmentalize or focus scope. If the program is inconsistent from study to study, it may be more difficult to notice these opportunities for increased agility, hindering your program from becoming even more rapid over time.
A Rapid Research Case StudyWhile working at one of the largest telecommunications companies in the US, I had the privilege of witnessing the growth of the UX Research team from just four practitioners to over 25 by the time I left. During this time, the company had matured its user experience practice, including the standards, processes, and discipline of user research.
As we grew, human insight became a central part of the product development process, which meant an exponential increase in its demand. While this was a great thing and allowed our team to grow, the work we were doing was not sustainable — we were constantly trying to keep pace with product teams who brought us in too late in the process simply to validate their ideas. Not only did we always feel rushed, but we were stuck doing only evaluative work, which not only stifled innovation but also did not satisfy our more senior researchers who wished to do more generative research.
Once diagnosing this issue, our leadership initiated several new processes to build a more well-rounded research portfolio that supported iterative research while enabling generative research. This included a democratization program, quarterly planning, and my initiative: Rapid Research. We determined that we needed a program that would allow us to take on mid-sized projects at the pace of product development while providing a new opportunity to hire junior researchers who would be a great talent pool for our team and provide a meaningful way for those new to the field to grow their skills.
In order to build the rapid research program, I audited the previous year’s worth of research to determine our average timelines, the most common methodologies used for iterative and mid-sized projects, and to identify our primary customer who we do research with most often. My findings would be the bedrock of the program:
At first, we did not have the budget for hiring new junior researchers to staff the program team. What we did have, however, was a contract with a research vendor who we’ve worked with for years, so we decided to partner with researchers from their team to run our rapid research program.
We saw the incredible value and strong results from building our rapid research program, especially alongside the other processes our team was standing up to support varying insights needs.
The following steps outline a process for getting started with building your own rapid research program in your organization. Exactly which steps you choose to follow, or if you decide to add more or less to your process, will be entirely up to you and the unique needs of your team. Follow the proceeding steps while considering the above guidelines regarding scope, timing, compartmentalization, and consistency.
While seemingly counter-intuitive, the first step in building a rapid research program is considering whether you even need one in the first place. Every new initiative or tactic intended to mature user research practice should consider the available talent and capabilities of the team and the needs or opportunities of the organization it sits within. It would be unfortunate to invest time to build a robust, rapid research program only to find that nobody uses or needs it.
Start by documenting the needs of your experience teams or the organization you support by the different types of requests you receive.
Answering these questions should be your first step before any meaningful work is done to build a rapid research program. In addition, you might consider the following information-gathering activities:
These additional inputs will either help you refine your approach to building a program or to steer away from doing so.
Finally, when considering if you should build a rapid research program in the first place, you should consider what the program cannot do.
Once you’ve decided to pursue a rapid research program, you’ll need to understand what form your program should take in order to deliver the highest value to your team and those you support. As mentioned above, a right-sized scope should consider the research methods, requirements, session quantity & duration, and participant profiles, which you can confidently accommodate. And you will need to determine the end-to-end timing and program cadence that differentiates from current work while providing just enough time to still deliver sustainable quality.
Start building your scope backwards from the needs gaps you’re filling within your team based on the answers to the discovery questions above. You’ll want to identify the primary type(s) of end-users this program will research.
For example, if your audit revealed that your team had focused most frequently on current customers over the past year, and you also know that your business will soon focus on the acquisition of new customers, consider including both current customers and prospective customers in your rapid research scope.
Remember the important note about consistency above? Once you’ve identified potential participant profiles, make sure you can consistently recruit them. For example, if you use a research panel to source participants for research studies, test the incidence of your participant profiles. If you find they don’t have many panelists with the attributes you need, you might spend too much time in recruitment and jeopardize the speed of the program.
A balance should be struck between participant profiles that are specific enough to be useful for most projects and those broad enough to reach easily.
You can conduct the same audit and rough forecasting when determining the research methods your program ought to support but with two additional considerations:
User researchers tend to focus their work further upstream, where they’re driving product roadmaps or influencing business strategy. This can bode well for your rapid research program if it is focused on evaluative research projects, which are often quicker and cheaper to conduct.
The ultimate goal is for the rapid research program to be a complement to what your team provides or as an enabler for freeing up their bandwidth so that they can focus on the type of work they want to do more of.
Once you’ve determined which research methods you want to include in your rapid research program, consider the level of rigor you need to balance effort and complexity.
Project timelines within a rapid research cadence are directly affected by the above scope decisions for participant profiles and research methodology. Timelines can also compound in highly regulated industries such as healthcare or banking, where you may be required to gather legal & compliance approval on every moderation guide. In order to call this a rapid research program, the end-to-end project timelines need to be shorter than a typical project of a similar scope, or at least feel that way.
Once you’ve documented your current research process (steps, dependencies, timing) and the changes you need to make to build efficiencies or remove dependencies, document what ‘must be true’ in order to consistently deliver identified changes. Create a table to document all of these details, then sum up the total timelines to compare your typical end-to-end research project timeline with your potential new ‘rapid’ timeline.
Ask yourself if this seems ‘rapid’ when stacked against your average study duration.
It’s time to set the foundation. Return back to the tables you made above and create an action plan with the following steps and a timeline to build the infrastructure required to bring your program to life. As part of this, you’ll need to establish the rules and standards for communicating with partners. You might consider a playbook and formal scope document to inform others of the ins/outs of the program.
Prioritize any work that requires buy-in, generating understanding, or acquiring budget first before spending your time and energy building templates or documentation. You wouldn’t want to create a 20-page scope document outlining the bandwidth for two researchers, a limit to 1 round of stakeholder feedback, and a 24hr SLA for legal approval, only to find out others cannot commit to that.
You’ll need plenty of templates, tools, and processes specific to the scope of your program.
As mentioned above, even a drastically reduced version of your typical research processes still requires effort to support. You’ll need to determine, based on the expected scope and cadence of each rapid research project, how many researchers and/or research operations coordinators you’ll need to support the program. While all rapid research programs will require dedicated effort, there are creative ways of staffing the program, such as:
Work with your leadership, HR, and TA professionals on securing approval for any team restructure, needed headcount budget, or to onboard a new vendor. Then, take the appropriate steps to hire your next researcher or secure the staffing help you need to support your program.
Consider training, coaching, and check-in meetings as part of your infrastructure.
No matter how much preparation you do or how much time and effort you spend building the alliances, infrastructure, training, and support required to run your rapid research program effectively, you will learn that there are improvements you should make once you put it into practice.
There are many benefits to piloting a new program in an organization. One benefit is that it can mitigate risks and allow teams to learn quickly and early enough to make positive enhancements.
“Piloting offers a realistic preview experience for users at the earliest stages of development. It allows the organization and design team to gather real-time insights that can be used to shape and refine the product and prepare it for commercialization.”
— Entrepreneur, “Tasting As You Go: The 5 Benefits of ‘Piloting’”
This means setting expectations early. Consider your first few projects as pilots and expect them to be rocky and imperfect. Use this to your advantage by asking stakeholders you’re closest with to be your trial projects and let them know how important their honest feedback is throughout the process. Ensure that you have clear mechanisms to gather feedback at each project milestone so that you can track progress. It is especially important to capture what might be slowing you down along the way or putting your ‘rapid’ timelines at risk.
Program Evolutions, Impacts & ConsiderationsWhile I’ve outlined a process for getting started, there are many ways in which your rapid research program may evolve over time to meet the needs of your organization better.
However your rapid research program takes shape, revisit its goals, scope, and operations often in relation to your organizational needs and context so that it remains relevant and delivers the highest impact.
Building a rapid research program can have a big impact and can contribute positively toward your team’s long-term strategy. One impact of instituting a rapid research program could be that now your team is freed up to focus on more generative research, which unlocks your ability to deliver deep customer insights that pave the way for innovation or strategy. And due to your new rapid pace, you may be able to keep pace with agile development and conduct end-to-end research within 2-week sprints. Another impact is that you may catch more usability issues further upstream, saving you over 100x in overhead business cost. A final impact of a rapid research program is that it can double your team’s throughput, allowing your team to deliver more research, more frequently, to accommodate more organizational needs.
Be sure to track these impacts over time so that you not only get credit for the hard work you put into building the program but so that you can sustain and grow the program over time.
As mentioned in this article, there are many benefits to building a rapid research program. That being said, there are limitations to rapid research in regard to its pros and cons when it should be used, and if you have the available time to stand up a program yourself.
As with building any new program, one should consider both its benefits as well as drawbacks. Here are a few for rapid research programs:
Pros:
Cons:
Rapid Research programs are best for specific types of research which do not take a long time to complete or require rigorous expertise. You may want to educate your partners on when they should expect to use a rapid research program and when they should not.
While the exact timeline of building a rapid research program varies from team to team, it does take time to do it right. Make sure to plan out enough time to do the upfront work of identifying the appropriate scope, timing, and cadence, as well as gathering consensus from leadership and appropriate stakeholder groups. Standing up a Rapid Research program can take anywhere from 3 months to 1 year, depending on the following:
A rapid research program can be a fundamental part of your team’s UX Research strategy, enabling your team to take on new insight challenges and deliver efficient research at an unprecedented pace. Building a rapid research program with high intention by determining the goals, appropriate scope, and necessary infrastructure will set your team up for success and enable you to deliver more value for your organization as you scale your user research practice.
Don’t be afraid to try a rapid research program today!
As developers working with third-party REST APIs, it is often necessary to store the request and response details for potential future reference, especially when issues arise. This could serve as invaluable data to liaise with third-party vendors, providing a first-hand look at the raw interaction that occurred. In my role, I am constantly orchestrating API’s with multiple partner services. Therefore, I sought a generic solution to store these third-party API requests and responses seamlessly.
The Spring Framework's RestTemplate, a widely used synchronous HTTP client, proves to be a handy tool for consuming RESTful services. This framework provides an interface called ClientHttpRequestInterceptor, which allows us to take certain actions on the request and response.
Amazon Web Services (AWS) provides a robust and scalable cloud computing platform that is widely adopted by organizations. However, with the increasing reliance on AWS services, ensuring proper security measures is crucial to protect sensitive data and maintain the integrity of cloud environments. In this article, we will demystify AWS security and discuss key considerations to establish a secure cloud environment on AWS. Before we dive into the eight points, let's quickly discuss an interesting case study that will make what we will discuss later in this article more pertinent.
In 2019, Capital One experienced a significant data breach that exposed sensitive customer information. The breach affected approximately 100 million individuals in the United States and approximately 6 million in Canada, with a hacker illicitly accessing a total of about 140,000 Social Security numbers and approximately 80,000 linked bank account numbers of secured credit card customers.
This article aims to provide a reference for non-tech companies who are seeking to empower their business with data analytics. You will learn the basics about how to build an efficient and easy-to-use data system, and I will walk you through every aspect of it with a use case of Apache Doris, an MPP-based analytic data warehouse.
A prominent feature of ticketing services is the periodic spikes in ticket orders, you know before the shows go on. So, from time to time, the company has a huge amount of new data rushing in and requires real-time processing of it so that they can make timely adjustments during the short sales window. But at other times, they won't want to spend too much energy and funds on maintaining the data system. Furthermore, for a beginner in digital operation who only require basic analytic functions, it is better to have a data architecture that is easy to grasp and user-friendly. After research and comparison, they came to the Apache Doris community, and we help them build a Doris-based data architecture.