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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