Quote for the day:
"Courage doesn't mean you don't get afraid. Courage means you don't let fear stop you." -- Bethany Hamilton
From Coder to Catalyst: What They Don’t Teach About Technical Leadership
The best technical leaders don’t just solve harder problems – they multiply
their impact by solving different kinds of problems. What follows is the
three-tier evolution most engineers never see coming, and the skills you’ll
need that no computer science program ever taught you. ... You’ll have moments
of doubt. When you’re starting out, if a junior engineer falls behind, your
instinct is to jump in and solve the problem yourself. You might feel like a
hero, but this is bad leadership. You’re not holding the junior engineer
accountable, and worse, you’re breaking trust—signaling that you don’t believe
they can handle the challenge. ... When projects drift off track, you’re
cutting scope, reallocating people, and making key decisions at crossroads.
But there’s something more critical: risk management. You need to think one
step ahead of the projects, identify key risks before they materialize, and
mitigate them proactively. ... Additionally, there’s one more thing nobody
mentions: managing stakeholders. Not just your team, but peers across the
organization and leaders above you. Technical leadership isn’t just downward –
it’s omnidirectional. ... The learning curve never ends. You never stop
feeling like you’re figuring it out as you go, and that’s the point. Technical
leadership is continuous adaptation. The best leaders stay humble enough to
admit they’re still learning. The real measure of success isn’t in your commit
history. You’re succeeding when your team can execute without you. When people
you hired are better than you at things you used to do.In an AI-perfect world, it’s time to prove you’re human
Being yourself in all communication is not only about authenticity, but
individuality. By communicating in a way that only you can communicate, you
increase your appeal and value in a world of generic, faceless,
zero-personality AI content. For marketing communications, this goes double.
The public will increasingly assume what they see is AI-generated, and
therefore cheap garbage. ... Not only will the public reject what they assume
to be AI, the social algorithms will increasingly reward and boost content
offering the signals of authenticity. In fact, Mosseri said that within Meta
there is a push to prioritize “original content” over “templated“ or “generic“
AI content that is easy to churn out at a massive scale. ... Rather than
thinking of AI as a tool that replaces work and workers, we should think of it
as a “scaffolding for human potential,” a way to magnify our cognitive
capabilities, not replace them. In other words, instead of viewing AI as
something that writes and creates pictures so we don’t have to or writes code
so we don’t have to — meaning we don’t even have to learn how to code — we
need to use AI to become great at writing, creating images and coding. From
now on, everyone will assume everyone else has and uses AI. Content and
communications will always exist on a spectrum from fully AI-generated to
zero-AI human communication. The further toward the human any bit of content
gets, the more valuable it will feel to both the receivers of the content and
to the gatekeepers. How to Build a Robust Data Architecture for Scalable Business Growth
As early in the process as possible, you should begin engaging with
stakeholders like IT teams, business and data analysts, executives,
administrators, and any other group within your organization that regularly
interacts with data. Get to know their data practices and goals, which will
provide insight into the requirements for your new data architecture, ensuring you have a deep well of
information to draw from. ... After communicating with stakeholders and
researching your organization’s current data landscape, you can determine
exactly what your data architecture will need now and into the future. Some
requirements you will need to precisely define the volume of data your
architecture will handle, how fast data needs to move through your
organization, and how secure the data needs to be. All this data about your
data will guide you toward better decisions in designing and building your
data architecture. ... The exact construction of your data architecture will
depend largely upon the needs you outlined during the previous step, but some
solutions are more advantageous for businesses looking to expand. ... While
there is plenty of healthy debate regarding the merits of horizontal scaling
versus vertical scaling, the truth is that the best database architectures use
both. Horizontal scaling, or using multiple servers to distribute data and
processes, allows an organization to have many nodes within a system so the
system can dedicate resources to specific data tasks. The Quiet Shift Changing UX
What does the drought at Stack Overflow teach us?
“AI developer tools seem to be taking attention away from static
question-and-answer solutions, replacing Stack Overflow with generated code
without the middleman… and without waiting for a question to be answered,” said
Walls. “Interestingly, AI tools lack the reputational metadata that Stack
Overflow relied on: i.e. when was this solution posted and who posted it… and do
they have a lot of prior answers? Developers are conferring trust to LLMs that
human-sourced sites had to build over years and fight to retain. It’s much
easier for developers to ask an agent for some code to accomplish a task and
click accept, regardless of the provenance of that code.” ... “Today we know
that LLMs like ChatGPT are already pretty good at answering common questions,
which are the bulk of the questions asked at StackOverflow. Additionally, LLMs
can respond in real time, so it is not a surprise that people were shifting away
from StackOverflow. It might be not the only reason though – some people also
reported StackOverflow moderators being rather hostile and unwelcoming towards
new users, which had additional impact,” said Zaitsev. “Why would you deal with
what you see as bad treatment, if an alternative exists?” ... “With AI now
available directly in IDEs, engineers naturally turn to quick, contextual
support as they work,” said Jackson.
Ready or Not, AI is Rewriting the Rules for Software Testing
Etan Lightstone, a product design leader at Domino Data Lab, argues that building trust in agents requires applying familiar operational principles. He suggests that for an enterprise with mature MLOps capabilities, trusting an agent is not enormously different from trusting a human user, because the same pillars of governance are in place: Robust logging of every action, complete auditability to trace what happened and the critical ability to roll back any action if something goes wrong. This product-centric mindset also extends to how we design and test the MCP tools before they ever reach production. Lightstone proposes a novel approach he calls “usability testing for AI.” Just as a product team would run usability tests with human beings to uncover design flaws before a release, he advises that MCP servers should be tested with sample AI agents. This is an effective way to discover issues in how a tool’s functions are documented and described — which is critical, since this documentation effectively becomes part of the prompt that the AI agent uses. Furthermore, he suggests we need to build “support links” for AI agents acting on our behalf. When a user gets stuck, they can often click a link to get help or submit feedback. Lightstone argues that AI agents need similar recovery mechanisms. This could be an MCP-exposed feedback tool that an agent can call if it cannot recover from an error or a dedicated function to get help from a documentation search.Defending at Scale: The Importance of People in Data Center Security
In the tech world, the mantra of “move fast and break things” has become a badge
of innovation. For cases like social platforms or mobile apps, where “breaking
things” translates to inconveniences rather than catastrophes, it can work quite
well. But when it comes to building critical infrastructure that supports
essential functions and drives the future of society, companies must take the
time to ensure they build safely and sustainably. Establishing robust physical
security is already challenging, and implementing strong policies and processes
to support those controls is even more difficult. Often, the core risk lies in
the human layer that determines whether controls are applied consistently. ...
With the promise of AI-powered efficiency gains, there’s increased pressure to
move faster. When organizations take shortcuts in the name of speed, however,
those shortcuts often come at the cost of consistent and thorough security. This
could include gaps in training for guards, technicians, and vendors, unclear
policies for after-hours access, frequent contractor changes, poorly defined
emergency protocols, or procedures that only exist on paper. ... As businesses
rush to meet the demand for AI, the data center boom is expected to continue
rising. In all this rush, it's easy to overlook that moving fast without first
establishing and reliably executing proper processes increases risk. Building
too quickly without a strong security culture can lead to expensive problems
down the line.
Industrial cyber governance hits inflection point, shifts toward measurable resilience and executive accountability
For industrial operators, the harder task is converting cyber exposure into defensible investment decisions. Quantified risk approaches, promoted by the World Economic Forum, are gaining traction by linking potential downtime, safety impact, and financial loss to capital planning and insurance strategy. ... “Governance should shift to a unified IT/OT risk council where safety engineers and CISOs share a common language of operational impact,” Paul Shaver, global practice leader at Mandiant’s Industrial Control Systems/Operational Technology Security Consulting practice, told Industrial Cyber. “Organizations should integrate OT-specific safety metrics into the standard IT risk framework to ensure cybersecurity decisions are made with production uptime in mind. This evolution requires aligning IT’s data confidentiality goals with OT’s requirement for high availability and human safety. ... Organizations need to move from siloed governance to a risk-first model that prioritizes the most critical threats, whether cyber or operational, and updates policies dynamically based on risk assessments, Jacob Marzloff, president and co-founder at Armexa, told Industrial Cyber. “A shared risk matrix across teams enables consistent trade-offs for safety and cybersecurity. Oversight should be centralized through a cross-functional Risk Committee rather than a single leader, ensuring expertise from IT, engineering, and operations. This committee creates a feedback loop between real-world risks and governance, building resilience.”A Reality Check on Global AI Adoption
"AI is diffusing at extraordinary speed, but not evenly," the report said.
Advanced digital economies are integrating AI into everyday work far faster than
emerging markets. The findings underscore a shift in the AI race from model
development to real-world deployment in which diffusion, not innovation alone,
determines who benefits most. Microsoft CEO Satya Nadella in a recent blog said,
"The next phase of the AI will be defined by execution at scale rather than
discovery. The industry is moving from model breakthroughs to the harder work of
building systems that deliver real-world value." ... Microsoft defines AI
diffusion as the proportion of working-age individuals who have used generative
AI tools within a defined period. This usage-based measurement shifts attention
from venture funding, compute ownership or research output to real-world
interaction including how AI is entering daily workflows, from coding and
analysis to communication and content creation. ... Infrastructure gaps persist,
language limitations reduce the effectiveness of many generative AI systems, and
skills shortages constrain adoption when education and workforce training have
not kept pace. Institutional capacity also plays a role, influencing trust,
governance and public-sector deployment. At the same time, the diffusion metric
captures breadth, not depth. A one-time interaction with a chatbot is measured
the same as embedding AI into mission-critical enterprise systems.
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