Quote for the day:
"Let today be the day you start something new and amazing." -- Unknown
Convergence of Technologies Reshaping the Enterprise Network
"We are now at the epicenter of the transformation of IT, where AI and
networking are converging," said Antonio Neri, president and CEO of HPE. "In
addition to positioning HPE to offer our customers a modern network architecture
alternative and an even more differentiated and complete portfolio across hybrid
cloud, AI and networking, this combination accelerates our profitable growth
strategy as we deepen our customer relevance and expand our total addressable
market into attractive adjacent areas." Naresh Singh, senior director analyst at
Gartner, told Information Security Media Group that the merger of two networking
heavyweights would make the networking landscape interesting in the near future.
... Security vendors have long tackled cyberthreats through robust portfolios,
including next-generation firewalls, endpoint security, secure access service
edge, intrusion detection system or intrusion prevention system,
software-defined wide area network and network security management. But the rise
of AI and large language models has introduced new risks that demand a deeper
transformation across people, processes and technology. As organizations
recognize the need for a secure foundation, many are accelerating their AI
adoption initiatives.Blind spots at the top: Why leaders fail
You’ve stopped learning. Not because there’s nothing left to learn, but because
your ego can’t handle starting from scratch again. You default to what worked
five years ago. Meanwhile, your environment has moved on, your competitors have
pivoted, and your team can smell the stagnation. Ultimately, you are an
architect of resilience and trust. As Alvin Toffler warned, “The illiterate of
the 21st century will not be those who cannot read and write, but those who
cannot learn, unlearn, and relearn.” ... Believing you’re always right is a
shortcut to irrelevance. When you stop listening, you stop leading. You confuse
confidence with competence and dominance with clarity. You bulldoze feedback and
mistake silence for agreement. That silence? It’s fear. ... Stress is part of
the job. But if every challenge sends you into a spiral, your people will spend
more time managing your mood than solving real problems. Fragile leaders don’t
scale. Their teams shrink. Their influence dries up. Strong leadership isn’t
about acting tough. It’s about staying grounded when things go sideways. ... You
think you’re empowering, but you’re micromanaging. You think you’re a visionary,
but your team sees a control freak. You think you’re a mentor, but you dominate
every meeting. The gap between intent and impact? That’s where teams disengage.
The worst part? No one will tell you unless you build a culture where they
can.9 habits of the highly ineffective vibe coder
It’s easy to think that one large language model is the same as any other. The
interfaces are largely identical, after all. In goes some text and out comes a
magic answer, right? LLMs even tend to give similar answers to easy questions.
And their names don’t even tell us much, because most LLM creators choose
something cute rather than descriptive. But models have different internal
structures, which can affect how well they unpack and understand problems that
involve complex logic, like writing code. ... Many developers don’t realize how
much LLMs are affected by the size of their input. The model must churn through
all the tokens in your prompt before it can generate something that might be
useful to you. More input tokens require more resources. Habitually dumping big
blocks of code on the LLM can start to add up. Do it too much and you’ll end up
overwhelming the hardware and filling up the context window. Some developers
even talk about just uploading their entire source folder “just in case.” ... AI
assistants do best when they’re focusing our attention on some obscure corner of
the software documentation. Or maybe they’re finding a tidbit of knowledge about
some feature that isn’t where we expected it to be. They’re amazing at searching
through a vast training set for just the right insight. They’re not always so
good at synthesizing or offering deep insight, though.How to Eliminate Deployment Bottlenecks Without Sacrificing Application Security
As organizations embrace DevOps to accelerate innovation, the traditional approach of treating security as a checkpoint begins to break down. The result? Security either slows releases or, even worse, gets bypassed altogether amidst the need to deliver as quickly as possible. ... DevOps has reshaped software delivery, with teams now expected to deploy applications at high velocity, using continuous integration and delivery (CI/CD), microservices architectures, and container orchestration platforms like Kubernetes. But as development practices evolved, many security tools have not kept pace. While traditional Web Application Firewalls (WAFs) remain effective for many use cases, their operational models can become challenging when applied to highly dynamic, modern development environments. In such scenarios, they often introduce delays, limit flexibility, and add operational burden instead of enabling agility. ... Modern architectures introduce constant change. New microservices, APIs, and environments are deployed daily. Traditional WAFs, built for stable applications, rely on domain-first onboarding models that treat each application as an isolated unit. Every new domain or service often requires manual configuration, creating friction and increasing the risk of unprotected assets.Anthropic wants to stop AI models from turning evil - here's how
In a paper released Friday, the company explores how and why models exhibit
undesirable behavior, and what can be done about it. A model's persona can
change during training and once it's deployed, when user inputs start
influencing it. This is evidenced by models that may have passed safety checks
before deployment, but then develop alter egos or act erratically once they're
publicly available ... Anthropic admitted in the paper that "shaping a model's
character is more of an art than a science," but said persona vectors are
another arm with which to monitor -- and potentially safeguard against --
harmful traits. In the paper, Anthropic explained that it can steer these
vectors by instructing models to act in certain ways -- for example, if it
injects an evil prompt into the model, the model will respond from an evil
place, confirming a cause-and-effect relationship that makes the roots of a
model's character easier to trace. "By measuring the strength of persona vector
activations, we can detect when the model's personality is shifting towards the
corresponding trait, either over the course of training or during a
conversation," Anthropic explained. "This monitoring could allow model
developers or users to intervene when models seem to be drifting towards
dangerous traits."
From Aspiration to Action: The State of DevOps Automation Today
One of the report's clearest findings is the advantage of engaging QA teams earlier in the development cycle. Teams practicing shift-left testing — bringing QA into planning, design, and early build phases — report higher satisfaction rates and stronger results overall. In fact, 88% of teams with early QA involvement reported satisfaction with their quality processes, and those teams also experienced fewer escaped defects and more comprehensive test coverage. Rather than testing at the end of the development cycle, early QA involvement enables faster feedback loops, better test design, and tighter alignment with user requirements. It also improves collaboration between developers and testers, making it easier to catch potential issues before they escalate into expensive fixes. ... While more DevOps teams recognize the importance of integrating security into the software development lifecycle (SDLC), sizable gaps remain. ... Many organizations still treat security as a separate function, disconnected from their routine QA and DevOps processes. This separation slows down vulnerability detection and remediation. These findings show the need for teams to better integrate security practices earlier in the SDLC, leveraging AI-driven tools that facilitate proactive threat detection and management.Why the AI era is forcing a redesign of the entire compute backbone
Traditional fault tolerance relies on redundancy among loosely connected systems
to achieve high uptime. ML computing demands a different approach. First, the
sheer scale of computation makes over-provisioning too costly. Second, model
training is a tightly synchronized process, where a single failure can cascade
to thousands of processors. Finally, advanced ML hardware often pushes to the
boundary of current technology, potentially leading to higher failure rates. ...
As we push for greater performance, individual chips require more power, often
exceeding the cooling capacity of traditional air-cooled data centers. This
necessitates a shift towards more energy-intensive, but ultimately more
efficient, liquid cooling solutions, and a fundamental redesign of data center
cooling infrastructure. ... One important observation is that AI will, in the
end, enhance attacker capabilities. This, in turn, means that we must ensure
that AI simultaneously supercharges our defenses. This includes end-to-end data
encryption, robust data lineage tracking with verifiable access logs,
hardware-enforced security boundaries to protect sensitive computations and
sophisticated key management systems. ... The rise of gen AI marks not just an
evolution, but a revolution that requires a radical reimagining of our computing
infrastructure.
Industry Leaders Warn MSPs: Rolling Out AI Too Soon Could Backfire
“The biggest risk actually out there is deploying this stuff too soon,” he said. “If you push it really, really hard, your customers are going to be like, ‘This is terrible. I hate it. Why did you do this?’ That will change their opinion on AI for everything moving forward.” The message resonated with other leaders on the panel, including Heddy, who likened AI adoption to on-boarding a new employee. “I would not put my new employees in front of customers until I have educated them,” he said. “And so yes, you should roll [AI] out to your customers only when you are sure that what it is delivering is going to be good.” ... “Everybody’s just sort of siloed in their own little chat box. Wherever this agentic future is, we can all see that’s where it’s going, but at what point do we trust an agent to actually do something? ... “So what are the steps? What is the training that has to happen? How do we have all this information in context for the individual, the team, the entire organization? Where we’re headed is clear. Just … how long does that take?” ... “Don’t wait until you think you have it nailed and are the expert in the world on this to go have a conversation because those who are not experts on it are going to go have conversations with your customers about AI. We should consume it to make ourselves a better company, and then once we understand it well enough to sell it, only then should we go and try to sell it.”Why Standards and Certification Matter More Than Ever
A major obstacle for enterprise IT teams is the lack of interoperability.
Today's networked services span multiple clouds, edge locations and on-premises
systems. Each environment brings unique security and compliance needs, making
cohesive service delivery difficult. Lifecycle Service Orchestration (LSO),
developed and advanced by Mplify, formerly MEF, offers a path through this
complexity. With standardized and certified APIs and consistent service
definitions, LSO supports automated provisioning and service management across
environments and enables seamless interoperability between providers and
platforms. ... In a world of constant change, standards and certification are
strategic necessities. ... By reuniting around proven frameworks, organizations
can modernize more confidently. Certification provides a layer of trust,
ensuring solutions meet real-world requirements and work across the environments
that enterprises rely on most. ... Standards and certification offer a way to
cut through the complexity so networks, services and AI deployments can evolve
without introducing new risks. Enterprises that succeed won't be the ones asking
whether to adopt LSO, SASE or GPUaaS, but rather finding smart, swift ways to
put them into practice.
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