Why DevOps Is Backward and How We Can Solve It
Perhaps the term “DevOps” simply rolls off the tongue better than “OpDev,” but
the argument could be made that since development comes first, operations will
follow. But if we look under the hood, most shops actually do run “OpDev”
pipelines, even though they do not recognize how that came about within the
organization. ... Without a very strict CI/CD pipeline and (usually) many team
members keeping infrastructure safe and cost efficient, operations is a
Sisyphean task, and most importantly it’s slow. ... So we need a better way to
handle infrastructure without turning the ops team into firefighters rather
than cooperative team members. Correspondingly we want to enable the devs to
build unencumbered by strict rule sets as well as preserve the agile nature
and fast pace of development. ... More realistic and easily workable methods
like Nitric abstract away the platform as a service SDKs from the codebase and
replace the developers’ infra requirements with a library of tools that can be
referenced exactly the same, no matter where the finalized code is deployed.
The operations teams can easily maintain the needed infra patterns in a
centralized location, reducing the need to solve issues after code PRs.
5 dead-end IT skills — and how to avoid becoming obsolete
In software development today, automated testing is already well established
and accelerating. But new opportunities in QA will appear focused on what to
test and how, he says, along with the skills necessary to identify security
risks and other issues with code that’s created by AI. Jobs for experienced
software test engineers won’t disappear overnight, but understanding what AI
brings to the equation and making use of it could be key to stay relevant this
area. “In order to survive and extend their career — whatever the job role —
humans should master the art of leveraging AI as an assistant and embrace it,”
Palaniappan says. ... “With the growth of cloud-native and serverless
databases, employers are now more interested in your understanding of database
architecture and data governance in cloud environments,” Lloyd-Townshend says.
“To keep moving in the right direction in your career, it’s important to
develop adaptive problem-solving skills and not just rely solely on specific
technical expertise.” Hafez agrees activities around database management will
be a casualty of technological evolution, especially ones focused on
“repetitive activities such as backups, maintenance, and optimization.”
The dangers of fashion-driven tech decisions
The fact that some companies are having success with generative AI, or
Kubernetes, or whatever, doesn’t mean that you will. Our technology
decisions should be driven by what we need, not necessarily by what we read.
... Google created Kubernetes to handle cluster orchestration at massive
scale. It’s a microservices-based architecture, and its complexity is only
worth it at scale. For many applications, it’s overkill because, let’s face
it, most companies shouldn’t pretend to run their IT like Google. So why do
so many keep using it even though it clearly is wrong for their needs? ...
Andrej Karpathy, part of OpenAI’s founding team and previously director of
AI at Tesla, notes that when you prompt an LLM with a question, “You’re not
asking some magical AI. You’re asking a human data labeler,” one “whose
average essence was lossily distilled into statistical token tumblers that
are LLMs.” The machines are good at combing through lots of data to surface
answers, but it’s perhaps just a more sophisticated spin on a search engine.
... That might be exactly what you need, but it also might not be. Rather
than defaulting to “the answer is generative AI,” regardless of the
question, we’d do well to better tune how and when we use generative AI.
The race is on to make AI agents do your online shopping for you
Just as AI chatbots have proven somewhat useful for surfacing information
that’s hard to find through search engines, AI shopping agents have the
potential to find products or deals that you might not otherwise have
found on your own. In theory, these tools could save you hours when you
need to book a cheap flight, or help you easily locate a good birthday
present for your brother-in-law. ... If AI shopping agents really take
off, it could mean fewer people going to online storefronts, where
retailers have historically been able to upsell them or promote impulse
purchases. It also means that advertisers may not get valuable information
about shoppers, so they can be targeted with other products. For that
reason, those very advertisers and retailers are unlikely to let AI agents
disrupt their industries without a fight. That’s part of why companies
like Rabbit and Anthropic are training AI agents to use the ordinary user
interface of a website — that is, the bot would use the site just like you
do, clicking and typing in a browser in a way that’s largely
indistinguishable from a real person. That way, there’s no need to ask
permission to use an online service through a back end — permission that
could be rescinded if you’re hurting their business.
2025 will be a bad year for remote work
CEOs don’t trust their employees to work hard at home and fear they’re
watching daytime TV in their pajamas while on the clock. They intuit
office presence and the supervision of employees who appear to be working
as a metric for productivity. They can feel personally more comfortable
when they can walk around, interact with employees, and manage and
supervise in person. Some CEOs also feel the need to justify their
spending on office space, office equipment, and other costs associated
with office work. Whatever the reasons, there’s a general disagreement
between employees, who mostly want the option to work from home, and CEOs,
who mostly want to require employees to come into the office. ... The
remote work revolution will take a serious hit next year, both in
government and business. Then, with new generations of workers and leaders
gradually rising in the workforce in the coming decade, plus remote
work-enabling technologies like AI (specifically agentic AI) and augmented
reality growing in capability, remote work will make a slow, inevitable,
and permanent comeback. In the meantime, 2025 will be a rough year for
remote workers. Bu it also represents a huge opportunity for startups and
even established companies to hire the very best employees who are turned
away elsewhere because they insist on working remotely.
Japan’s Next Step With Open-Source Software: Global Strategy
Japanese open-source developers are renowned for their skill, dedication,
and meticulous focus on quality and detail. Their contributions have
shaped global projects and produced standout achievements, such as the
Ruby programming language, which exemplifies Japan's influence in
open-source development. However, corporate policies in Japan have often
been cautious regarding open source, particularly concerning licensing,
lack of resources for future development, security worries, and other
perceived limitations. While large Japanese corporations contribute
significantly to open-source projects, they lag behind their U.S. and
European counterparts in leveraging open-source as a core component of
their products and services. This is now beginning to change. Open source
is increasingly recognized as a way to accelerate development and expand
global reach. Japanese companies are looking to open-source as a tool for
increasing the speed of development, not just as a way to get projects up
and running. ... It's true that when developing something, you should
spend time-solving your own unique problems, and there is a tendency to
use tools that can be combined with other existing tools to solve problems
that can be solved.
7 Critical Education Trends That Will Define Learning In 2025
As machines become more efficient at analyzing trends, crunching numbers
and generating reports, the value of the skills that they still can’t
replicate will grow. This means that educators should increasingly focus
on nurturing these soft, "human" skills, like critical thinking,
big-picture strategy, communication, emotional intelligence, leadership
and teamwork. Expect to see greater integration of these into mainstream
education as we train to become more effective at high-value tasks
involving person-to-person interactions and navigation of complex and
chaotic real-world situations. ... All learners are different – we take in
information at different speeds; while some of us absorb knowledge better
from videos, some benefit more from group discussions or activity-based
learning. Personalized learning promises to deliver education in a way
that's tailored to the specific strengths of individual students. This
means tailored lesson plans, assessments and learning materials. In 2025
we will see experiments and pilot projects involving using AI to
accomplish this begin to move into the mainstream, as well as the
emergence of AI tutoring aids that are able to track the progress of
students in real time and adjust the delivery of learning on-the-fly to
create dynamic and engaging learning environments.
How an Effective AppSec Program Shifts Your Teams From Fixing to Building
While tools and processes are critical, they only address the technical
side of the challenge. Ensuring a cohesive culture of cooperation between
development and security teams is just as important. There must be a solid
partnership between both sides for efforts to succeed. Implementing a
security mentorship program can be an effective way to deliver this
collaboration. By appointing senior engineers as mentors, organizations
can leverage existing expertise to guide developers through secure coding
practices. These mentors provide real-time support, offering just-in-time
advice when critical vulnerabilities arise. This not only helps resolve
security issues faster but also ensures developers can remain focused on
delivering high-performance code. Such mentorships are a great opportunity
for individual engineers too, offering the chance to broaden their skills
and further their careers. ... Effective AppSec doesn’t have to
come at the cost of speed and innovation. Fostering collaboration between
development and security teams and integrating security seamlessly into
workflows will make lives easier — while ensuring there is minimal impact
to production schedules.
The Evolution of Time-Series Models: AI Leading a New Forecasting Era
The power of machine learning (ML) methods in time series forecasting
first gained prominence during the M4 and M5 forecasting competitions,
where ML-based models significantly outperformed traditional statistical
methods for the first time. In the M5 competition (2020), advanced models
like LightGBM, DeepAR, and N-BEATS demonstrated the effectiveness of
incorporating exogenous variables—factors like weather or holidays that
influence the data but aren’t part of the core time series. This approach
led to unprecedented forecasting accuracy. These competitions highlighted
the importance of cross-learning from multiple related series and paved
the way for developing foundation models specifically designed for time
series analysis. ... Looking ahead, combining time series models with
language models is unlocking exciting innovations. Models like Chronos,
Moirai, and TimesFM are pushing the boundaries of time series forecasting,
but the next frontier is blending traditional sensor data with
unstructured text for even better results. Take the automobile
industry—combining sensor data with technician reports and service notes
through NLP to get a complete view of potential maintenance issues.
Treat AI like a human: Redefining cybersecurity
Treating AI like a human is a perspective shift that will fundamentally
change how cybersecurity leaders operate. This shift encourages security
teams to think of AI as a collaborative partner with human failings. For
example, as AI becomes increasingly autonomous, organizations will need to
focus on aligning its use with the business’ goals while maintaining
reasonable control over its sovereignty. However, organizations will also
need to consider in policy and control design AI’s potential to manipulate
the truth and produce inadequate results, much like humans do. ...
Effective human oversight should include policies and processes for
mapping, managing, and measuring AI risk. It also should include
accountability structures, so teams and individuals are empowered,
responsible, and trained. Organizations should also establish the context
to frame risks related to an AI system. AI actors in charge of one part of
the process rarely have full visibility or control over other parts. ...
Performance indicators include analyzing, assessing, benchmarking, and
ultimately monitoring AI risk and related effects. Measuring AI risks
includes tracking metrics for trustworthy characteristics, social impact,
and human-AI dependencies.
Quote for the day:
"The distance between insanity and genius is measured only by success."
-- Bruce Feirstein
