Daily Tech Digest - June 15, 2022

Software Engineering - The Soft Parts

Transferable skills are those you can take with you from project to project. Let's talk about them in relation to the fundamentals. The fundamentals are the foundation of any software engineering career. There are two layers to them - macro and micro. The macro layer is the core of software engineering and the micro layer is the implementation (e.g. the tech stack, libraries, frameworks, etc.). At a macro level, you learn programming concepts that are largely transferable regardless of language. The syntax may differ, but the core ideas are still the same. This can include things like: data-structures (arrays, objects, modules, hashes), algorithms (searching, sorting), architecture (design patterns, state management) and even performance optimizations. These are concepts you'll use so frequently that knowing them backwards can have a lot of value. At a micro level, you learn the implementation of those concepts. This can include things like: the language you use (JavaScript, Python, Ruby, etc), the frameworks you use (e.g. React, Angular, Vue etc), the backend you use (e.g. Django, Rails, etc), and the tech stack you use (e.g. Google App Engine, Google Cloud Platform, etc).

Why young tech workers leave — and what you can do to keep them

When employees seek a raise, what they’re really doing is shopping around and comparing offers from other companies, according to Sethi. And when it comes to salaries, companies must keep up with inflation, which is running at about 8% a year. But retaining employees requires more than just pay. Workers also want more support in translating environmental, social, and governance (ESG) considerations to their work. “Fulfilling work and the opportunity to be one’s authentic self at work also matter to employees who are considering a job change," Sethi said. "Pay is table stakes, but I also want my job to be meaningful and fulfilling, and I want to work at a place where I can be myself." Employees also want workplace flexibility. That, and human-centric work policies, can reduce attrition and increase performance. In fact, Gartner found that 65% of IT employees said that whether they can work flexibly affects their decision to stay at an organization.

A neuromorphic computing architecture that can run some deep neural networks more efficiently

Researchers at Graz University of Technology and Intel have recently demonstrated the huge potential of neuromorphic computing hardware for running DNNs in an experimental setting. Their paper, published in Nature Machine Intelligence and funded by the Human Brain Project (HBP), shows that neuromorphic computing hardware could run large DNNs 4 to 16 times more efficiently than conventional (i.e., non-brain inspired) computing hardware. "We have shown that a large class of DNNs, those that process temporally extended inputs such as for example sentences, can be implemented substantially more energy-efficiently if one solves the same problems on neuromorphic hardware with brain-inspired neurons and neural network architectures," Wolfgang Maass, one of the researchers who carried out the study, told TechXplore. "Furthermore, the DNNs that we considered are critical for higher level cognitive function, such as finding relations between sentences in a story and answering questions about its content." In their tests, Maass and his colleagues evaluated the energy-efficiency of a large neural network running on a neuromorphic computing chip created by Intel.

Why Your Database Needs a Machine Learning Brain

By keeping the ML at the database level, you’re able to eliminate several of the most time-consuming steps — and in doing so, ensure sensitive data can be analyzed within the governance model of the database. At the same time, you’re able to reduce the timeline of the project and cut points of potential failure. Furthermore, by placing ML at the data layer, it can be used for experimentation and simple hypothesis testing without it becoming a mini-project that requires time and resources to be signed off. This means you can try things on the fly, and not only increase the amount of insight but the agility of your business planning. By integrating the ML models as virtual database tables, alongside common BI tools, even large datasets can be queried with simple SQL statements. This technology incorporates a predictive layer into the database, allowing anyone trained in SQL to solve even complex problems related to time series, regression or classification models. In essence, this approach "democratizes" access to predictive data-driven experiences.

Understanding Low-code Development

If you are interested in getting started with low-code development, you will need a few things. First, you will need a low-code development platform. There are many options for you to select the right platform for you. You should analyze your requirements and explore all such options before choosing one. Several different options are available, so you should explore them to find one that meets your requirements. Once you have chosen a platform, you will need to learn how to use it. This may require some training or reading documentation. Finally, you will need some ideas for what you want to build. You are now ready to start low-code development. ... Here are some of the downsides of using Low-Code platforms for software development: Lack of Customization – Even though the pre-built modules of the low-code platforms are incredibly handy to work with, you can’t customize your application with them. You can customize low-code platforms but only to a limited extent. In most cases, low-code components are generic and if you want to customize your app you should invest time and effort in custom app development. 

Authentic Allyship and Intentional Leadership

Enterprises and leaders have to be intentional about their allyship. It has to be authentic allyship, not just surface allyship. I mention intentional allyship because a lot of times people think they’re an ally, and support diversity hires, but they’re just checking a box. We want intentional and authentic allyship. We need you to understand it goes beyond the person you’re helping. You’re helping the generation, not just one person. You think you’re only affecting the employee right in front of you, but that individual has a family and the next generation after them. You’re not just checking a box; you’re impacting destiny. When you’re an intentional ally, you think beyond the person in front of you, beyond the job application, beyond what you see. It’s not about you but what you’re doing for that person and that person’s generation to come. You need to really think about the step you’ll take when it comes to allyship. Make an impact – a lot of times we talk but don’t implement. Activate, implement, follow up. Don’t just implement and leave them there. Follow up – ask them how they’re doing, and if they know anyone else you can bring in. 

Software engineering estimates are garbage

Garbage estimates don’t account for the humanity of the people doing the work. Worse, they imply that only the system and its processes matter. This ends up forcing bad behaviors that lead to inferior engineering, loss of talent, and ultimately less valuable solutions. Such estimates are the measuring stick of a dysfunctional culture that assumes engineers will only produce if they’re compelled to do so—that they don’t care about their work or the people they serve. Falling behind the estimate’s promises? Forget about your family, friends, happiness, or health. It’s time to hustle and grind. Can’t craft a quality solution in the time you’ve been allotted? Hack a quick fix so you can close out the ticket. Solving the downstream issues you’ll create is someone else’s problem. Who needs automated tests anyway? Inspired with a new idea of how this software could be built better than originally specified? Keep it to yourself so you don’t mess up the timeline. Bludgeon people with the estimate enough, and they’ll soon learn to game the system.

Return to the office or else? Why bosses' ultimatums are missing the point

Employers who insist their staff return to the office full time are heading into increasingly dangerous territory. Skilled professionals, tech workers included, have so many opportunities available to them right now that it's difficult to see why they would sacrifice job satisfaction for their bosses. The outlook has never been better for knowledge workers – and indeed, workers more generally – across all industries. Not only are employers paying more to get the skills they need, but the breadth of flexible-working options for employees fed up with office life continues to grow. People aren't just working from home – they're working from wherever they choose, and whenever they choose. At the same time, significant momentum is gathering behind the introduction of a four-day work week, which could push the dynamic even further in favour of worker wellbeing while benefitting employers too. Companies who offer 100% pay for 80% of the hours will have a seriously powerful bargaining chip to play in the war for talent, and no company – regardless of their brand, product or credentials – will be untouchable.

UK needs to upskill to achieve quantum advantage

Discussing the pilot, Stephen Till, fellow at the Defence Science and Technology Laboratory (Dstl), an executive agency of the MoD, said: “This work with ORCA Computing is a milestone moment for the MoD. Accessing our own quantum computing hardware will not only accelerate our understanding of quantum computing, but the computer’s room-temperature operation will also give us the flexibility to use it in different locations for different requirements. “We expect the ORCA system to provide significantly improved latency – the speed at which we can read and write to the quantum computer. This is important for hybrid algorithms, which require multiple handovers between quantum and classical systems.” Piers Clinton-Tarestad, a partner in EY’s technology risk practice, said there is a general consensus that quantum computing will start becoming a reality in 2030. But pilot projects, such as the one being conducted at the MoD, and proof-of-concept applications can help business leaders to understand where quantum technology can be applied. 

Using automation to improve employee experience

The possibilities to improve the employee experience through automation and integration are endless. If you want to pilot something in your organization, poll your employees about what would be the most impactful. Where are they seeing sludge that drags down morale and slows business velocity? You and your IT team can plot each idea on an impact and effort prioritization matrix. Some suggestions may be easier to implement than you think, as many cloud services are already API-enabled, making automation straightforward. Once your team implements an initial valuable and visible integration, more employee lightbulbs will go off, identifying additional ideas for automation and integration for your prioritization backlog. And don’t forget about the ROI calculators in your automation tooling, as they will help objectively refine your prioritization by analyzing your planned and actual savings. Not only will your employees benefit directly from the automation, but they will also feel heard when they see their ideas come to life.

Quote for the day:

"Uncertainty is a permanent part of the leadership landscape. It never goes away." -- Andy Stanley

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