May 05, 2021

Why Every Developer Should Start Programming With C

Almost all programs offer built-in methods or library methods to solve coding problems that need logical algorithms. For example, if you need to copy specific items into a second array, you can use the built-in filter method in JavaScript. If you’re using Java, you may use the filter method from the java.util.stream package. Literally, any popular programming language has a built-in method or library method to filter an array. But if you’re using C, you have to implement it yourselves — because there’s no built-in or library filter method in C. When you find those scenarios, you’ll face problems that need to be solved. Practicing algorithmic questions is a great way to become a good problem solver. We don’t always work with straightforward tasks that involve only the standard library and built-in features of your favorite enterprise programming language. We often work with tasks involved with problem-solving skills. So, writing your initial codes with C makes you a better problem solver. Moreover, developers who participate in competitive programming hackathons often use C to solve problems.

Communication Design for Avoiding Uncertainty

Cultural diversity, if not handled well, can be a source of inefficiency, confusion, frustration, anxiety, and stress at work. When we don’t appreciate the cultural differences in how others convey and interpret information, we are more likely to misunderstand them. What happens when people from other countries and cultures don’t behave according to our cultural norms? We fall for the fundamental attribution error and attribute their behavior to their personality, assuming their behavior is representative of who they are. We also find it convenient to go with cultural stereotypes without making an attempt to understand how culture plays a role in their style of communication. Techniques that made us successful with a group of people from one country may not work with another group from a different country. To be collaborative, we need to embrace cultural diversity and open ourselves to learning different communication approaches and making adjustments along the way. We need to learn, unlearn, and relearn new strategies to communicate effectively with different groups of people.

Multi-Gov Task Force Plans to Take Down the Ransomware Economy

“Ransomware attackers require little risk or effort to launch attacks, so a prohibition on ransom payments would not necessarily lead them to move into other areas,” according to the report. “Rather, they would likely continue to mount attacks and test the resolve of both victim organizations and their regulatory authorities. To apply additional pressure, they would target organizations considered more essential to society, such as healthcare providers, local governments and other custodians of critical infrastructure.” So instead, “Updating breach disclosure laws to include a ransom-payment disclosure requirement would help increase the understanding of the scope and scale of the crime, allow for better estimates of the societal impact of these payments, and enable better targeting of disruption activities.” The Framework would require ransomware victims to report details about the incident prior to paying the ransom. ... As a corollary to this, the Framework would also have cyber-insurance companies establish a common pool of money “to evaluate and pursue strategies aimed at restitution, recovery or civil asset seizures, on behalf of victims and in conjunction with law-enforcement efforts.”

Why a Serverless Data API Might be Your Next Database

DBaaS hasn’t historically been considered serverless, but that has been changing. An increasing number of public cloud services are either offering serverless alternatives, or moving to this architecture altogether. As one might expect, stateless services like compute are a bit more straightforward to make serverless, relative to stateful ones. It’s only recently that databases and data services have begun to move in this direction. As you might expect, a serverless DBaaS scales to meet the immediate needs of the application it is servicing. When more demand is high, it scales up the number of nodes to meet the need, and when demand drops, it scales back down. This is particularly useful in microservice, FaaS and other distributed architectures. Right-sizing your database in concert with the microservice fleet in those scenarios is tedious, often resulting in paging already busy ops people. You are basically only paying by the operation instead of being charged for idle instances of “always on” virtual hardware. Read and write, storage and network out - done. This simplicity can result in tremendous idle time savings and a dramatically more cost-effective profile for everything from dev/test to production.

How modern workflows can benefit from pentesting

One of the things you’re going to need, especially in a tech company, you’re going to need a program that is unique to the company and takes into consideration the customers, the attack space they live in, the tech stack they’re using and the unique challenges they have. There is of course a standard menu that we each bring in our back pocket of things you want to make sure you’re checking off the list. When we dig into the application security space you think about the people you want to hire, at what level do they need to be, do they need a coding background, are they comfortable with developers, counseling and teaching developers how to code securely, etc. So, you have this people component and a teaching component. There is also an operational rigor that the public and customers expect. It’s great that you do this internally but what does a third party say about your program and how effective your program is. And while you’re building up these processes and you build out your application security department you have these engineers working with engineers all over the company, DevOps, infrastructure, product engineers

What Exactly Is Artificial Intelligence? (Hint: It’s All About The Datasets)

Datasets for machine learning are the main commodity in the world right now. Everybody is talking about AI and AI applications but a few are focusing on how accurate the data is and if the data is actually correct. Data collection needs to be deliberate—the success of its intended application depends on it. As those in data science know, datasets are necessary to build a machine learning project. The dataset is used to train the machine learning model and is an integral part of creating an efficient and accurate system. If your dataset is noise-free (noisy data is meaningless or corrupt) and standard, your system will be more reliable. But the most critical part is identifying datasets that are relevant to your project. So your company has decided to make the jump into data science and needs to collect data. But if you don't have any, where do you start? The answer is twofold. One option is to rely on open source datasets. Companies like Google, Amazon, and Twitter have a ton of data they’re willing to give away. And many online sites dedicated to AI and AI applications have compiled free categorized lists which make finding a good dataset even easier.

Read more here ...