Daily Tech Digest - November 07, 2025

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Quote for the day:

"The best teachers are those who don't tell you how to get there but show the way." -- @Pilotspeaker

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AI spending may slow down as ROI remains elusive

Some AI experts agree with Forrester that an AI market correction is on the way. Microsoft founder Bill Gates recently talked about the existence of an AI bubble, and industry observers have noted that some AI excitement is dimming. Many don’t see an AI bubble that will burst in the near future, but it’s deflating a bit. Still others don’t see much of a slowdown in the near term. ... Some organizations are not achieving the accuracy they need from AI tools, and others are not finding their data to be easily accessible or properly structured, says Sam Ferrise, CTO of IT consulting firm Trinetix. “Many organizations are realizing that their expectations for AI accuracy and performance don’t always align with the level of investment they’re willing — or able — to make,” he says. “The key is calibrating expectations relative to both the investment and the use case.” In other cases, enterprises deploying AI are running into privacy or security problems, he adds. “Many teams successfully prove a use case with clear ROI, only to realize later that they must harden the solution before it can safely move into production,” Ferrise says. “When that alignment isn’t there, it’s natural for organizations to pause or delay spending until they can justify the value.” The prospect of a bubble bursting may be an overly dramatic scenario, although not impossible, he adds. It’s been easy for organizations to overlook intangible costs such as training, compliance, and governance.

Why can’t enterprises get a handle on the cloud misconfiguration problem?

“Microsoft, Google, and Amazon have handed us a problem,” says Andrew Wilder, CSO at Vetcor, a national network of more than 900 veterinary hospitals. “By default, everything is insecure, and you have to put security on top of it. It would be much better if they just gave us out-of-the-box secure stuff. Would you buy a car that doesn’t have locks? They wouldn’t even sell that car.” This security gap is what allows third-party vendors to exist, he says. “You should be building products — and I’m talking to you, Google, Microsoft, and Amazon — that are secure by design, so you don’t have to get a third-party tool. They should be out of the box secure.” ... When administrators or users make changes to cloud configurations in the cloud management consoles, it’s difficult to track those changes and to revert them if something goes wrong. Plus, humans can easily make mistakes. The solution experts advise is to adopt the principle of “infrastructure as code” and use configuration management tools so that all changes are checked against policies, tracked and audited, and can easily be rolled back. ... Companies will often have monitoring for major cloud services, but shadow IT deployments are left in the dark. This is less a technology problem than a management one and can be addressed by better communications with business units and a more disciplined approach to deploying technology on an enterprise-wide level. 

The Supply Chain Blind Spot: Protecting Data in Expanding IT Ecosystems

Data growth is no longer linear, it is exponential. The rise of AI, automation, and digital platforms has transformed how information is created, stored, and shared. In India, this acceleration is particularly visible. The country’s data centre industry has grown from 590 MW in 2019 to 1.4 GW in 2024, a 139% jump, and is projected to reach 3 GW by 2030, driven by cloud adoption, AI demand, and data localisation initiatives. This infrastructure boom, while positive, brings new operational realities. Most enterprises now operate across hybrid environments, combining on-premises, public cloud and SaaS-based data stores. Without unified oversight, these fragmented environments risk becoming silos. True resilience depends not just on protecting data but understanding where it lives, how it moves, and who controls it. ... Globally, enterprises are reframing resilience as a core business capability. This approach requires integrating resilience principles into decision-making: from procurement and architecture design to crisis response. Simulated attacks, failover testing and dependency audits are becoming part of daily operational culture, not annual exercises. For Indian organizations, this mindset shift is vital. RBI’s ICT risk management directives and the DPDP Act establish the baseline; the differentiator lies in how proactively organizations operationalize these expectations. 

The power of low-tech in a high-tech world

Our high-tech society is impressive in the collective. But it robs individuals of skills. Most kids now can’t write cursive. And they can’t read it, either. They can’t read an analog clock or a paper map. The acceleration of technological innovation also accelerates the rate at which we lose skills. Videogames, smartphones, and dating apps — aided and abetted by the trauma of the COVID-19 lockdowns a few years ago — have left many young people alone without the skills to meet and connect with anyone, leading to a loneliness epidemic among the young. But losing old-fashioned skills and old-school tech knowledge is a choice we don’t have to make. ... Thousands of scientific reports all lead us to the same conclusion: Over-reliance on advanced technologies dulls critical thinking, weakens memory, reduces problem-solving skills, limits creativity, erodes attention spans, and fosters passive dependence on automated systems. ... What all these old-school approaches have in common is that they’re harder and take longer — and they leave you smarter and better connected. In other words, if you strategically cultivate the skills, habits, discipline and practice of older tech, you’ll be much more successful in your career and your life. And here’s one final point: The more high-tech our culture becomes, the more impactful old-school tech will be. So yes, by all means become brilliantly skilled at AI chatbot prompt engineering.

Why Leaders Cannot Outsource Communication

When communication is delegated to a proxy, that signal weakens. Employees notice the gap between what the leader says or doesn’t say, and what the organization does. This is why communication has an outsized impact on engagement. Gallup finds that 70% of the variance in employee engagement is explained by managers and leaders, not perks or policies. When leaders own the message, they create psychological safety: the sense that it’s safe to commit, speak up and take risks. When they don’t, that safety erodes. ... Delegating communication is tempting. Leaders are busy. They hire communications officers and agencies to manage the message. These roles are valuable, but they can’t substitute for the leader’s voice. A speechwriter can shape phrasing and a PR team can guide timing, but only the leader can deliver authenticity. As Murphy has written, “Leaders are accountable to employees: Candor about bad news as well as the good, and feedback that aligns with expectations.” Authenticity requires candor, even when the message is difficult. When communication comes from anyone else, it’s interpreted as institutional rather than personal. And people follow people, not institutions. ... The Operator Economy demands a new kind of scale, one built not on capital or code, but on human alignment. Communication is infrastructure. The CEO becomes the signal source around which all systems calibrate. When leaders “scale themselves” through clarity and consistency, they convert trust into throughput. 

Breaking the Burnout Cycle: How Smart Automation and ASPM Can Restore Developer Joy

Smart automation can rescue developers from repetitive drudgery by using AI to handle routine tasks like test writing, bug fixing, and documentation. Modern application security posture management (ASPM) platforms exemplify this approach by providing contextualized risk assessments rather than overwhelming vulnerability dumps, helping security teams first understand which issues actually matter and then giving developers actionable info on the risk and how it should be fixed. These platforms excel at managing the volume and unpredictability of AI-generated code, turning what was once a blind spot into manageable, prioritized work. ... Technology alone isn't enough. Organizations must also prioritize developer growth by creating opportunities for experimentation, architectural decisions, and end-to-end project ownership while automation handles routine tasks. This means shifting from measuring output volume to focusing on meaningful metrics like code quality and developer satisfaction. AI represents an opportunity for developers to gain expertise in an emerging technology.  ... The developer talent crisis is solvable. While AI has introduced new complexities to the software development and security landscape, it also presents unprecedented opportunities for organizations willing to rethink how they support their development teams.

The CIO’s Role In Data Democracy: Empowering Teams Without Losing Control

The modern CIO is at a point where they can choose between innovation and control. In the past, IT departments were thought of as people who took care of infrastructure and enforced strict regulations about who could access data. The CIO needs to reassess this way of doing things today. They shouldn’t prohibit access; instead, they should make it safe by building frameworks. The job has changed from saying “no” to making sure that when the company says “yes,” it does it smartly. The CIO is now both an architect and a guardian. They create systems that make data easy to get to, understand, and act on, all while keeping security and compliance in mind. ... The CIO is no longer a gatekeeper; they are instead a designer of trust. The goal is to make governance a part of systems such that it is seamless, automatic, and easy to use. This change lets companies keep an eye on things and stay in control without making decisions take longer. Unified data taxonomies are the first step in building this framework. This means that all departments use the same naming standards and definitions. When everyone uses the same “data language,” there is less confusion and more cooperation. ... Effective governance demands collaboration between IT, compliance, and business leaders. The CIO must champion cross-functional alignment where all parties share responsibility for data integrity and use.

What keeps phishing training from fading over time

Employees who want to be helpful or appear responsive can become easier targets than those reacting to fear or haste. For CISOs, this reinforces the need to teach users about manipulation through trust and cooperation, not just the warning signs of urgent or threatening messages. ... Dubniczky said maintaining employee engagement over time is a major challenge for most organizations. “In contrast with other research in the area, a key contribution of ours was a mandatory training after each failed phishing attack,” he explained. “This strikes a good balance between not needlessly bothering careful employees with monthly or quarterly trainings while making sure that the highest risk individuals are constantly trained.” He recommended that organizations vary their phishing simulations to keep users alert. “We’d recommend performing monthly penetration tests on smaller groups of people in diverse departments of the organization with a seemingly random pattern, and making re-training mandatory in case of successful attacks,” he said. “It’s also difficult to generalize on this, but this approach seems much more effective than periodic presentation-style trainings.” ... One of the most striking findings involves the timing of feedback. When employees clicked a phishing link and then received an immediate explanation and training prompt, they were far less likely to repeat the behavior. Around seven in ten employees who failed once did not do so again.

The new QA playbook: Leveraging AI to amplify expertise, not replace it

Many quality teams have been part of the AI journey from the very beginning, contributing from concept to implementation and helping evaluate large language models to ensure quality and reliability. However, many AI features are not developed by QA practitioners, so it is essential to evaluate them through a QA lens. First, ensure the system can produce what your teams actually use, whether that is step lists, BDD-style scenarios, or free text that fits your templates and automation. Next, map the full data journey. Know whether prompts or results are kept, how encryption and minimization are applied, and where any content is stored. Finally, require fine-grained controls so you can limit usage by environment, project, and role. Regulated teams require an audit trail and clear accountability, which means governance must keep pace with adoption, or speed will outpace safety. Once review-first habits are in place, build on them. True oversight requires more than simply checking AI outputs; it demands deeper knowledge and understanding than the AI itself to spot gaps, inaccuracies, or misleading information. That’s what separates a passive reviewer from an effective human in the loop. ... Real gains from AI will not come from automation alone but from people who know how to guide it with clarity, context, and care. The future of testing depends on professionals who can combine technical fluency with critical thinking, ethical judgment, and a sense of ownership over quality.

Your outage costs more than you think – so design with resilience in mind

Service providers are under strain to deliver the rapid speeds and constant network uptime that modern life demands, with areas like remote working, financial transactions, cloud access and streaming services expected to work seamlessly as part of the daily lives of many end users. For many enterprises, their business depends on this connectivity. Even a single hour of network disruption can cost an organisation more than $300,000, and the long-term damage to customer trust often exceeds any immediate financial loss. Despite this, many organisations still rely on outdated infrastructure that cannot support the requirements of today’s end users. Legacy environments struggle with explosive data growth, the soaring demands of AI, and the complexity of distributed, cloud-first applications. At the same time, power limitations, infrastructure strain and inconsistent service levels put businesses at risk of falling behind. The gap between what service providers and enterprises need, and what their infrastructure can deliver, is widening. ... For service providers, investing in robust colocation and high-performance networking is not just about upgrading infrastructure, but enabling customers and partners worldwide to thrive in today’s fast-paced digital landscape. By offering resilient and scalable connectivity, providers can differentiate their service offering, attract high-value enterprise clients, and create new revenue streams based on reliability and performance.

Daily Tech Digest - October 04, 2025

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Quote for the day:

“What seems to us as bitter trials are often blessings in disguise.” -- Oscar Wilde

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Autonomous Agents – Redefining Trust and Governance in AI-Driven Software

Agents are no longer confined to code generation. They automate tasks across the full lifecycle: from coding and testing to packaging, deploying, and monitoring. This shift reflects a move from static pipelines to dynamic orchestration. A new developer persona is emerging: the Agentic Engineer. These professionals are not traditional coders or ML practitioners. They are system designers: strategic architects of intelligent delivery systems, fluent in feedback loops, agent behavior, and orchestration across environments. ... To scale agentic AI safely, enterprises must build more than pipelines – they must build platforms of accountability. This requires a System of Record for AI Agents: a unified, persistent layer that treats agents as first-class citizens in the software supply chain. This system must also serve as the foundation for regulatory compliance. As AI regulations evolve globally – covering everything from automated decision-making to data residency and sovereignty – enterprises must ensure that every agent action, dataset, and interaction complies with relevant laws. A well-architected System of Record doesn’t just track activity; it injects governance and compliance into the core of agent workflows, ensuring that AI operates within legal and ethical boundaries from the start.

New AI training method creates powerful software agents with just 78 examples

The problem is that current training frameworks assume that higher agentic intelligence requires a lot of data, as has been shown in the classic scaling laws of language modeling. The researchers argue that this approach leads to increasingly complex training pipelines and substantial resource requirements. Moreover, in many areas, data is not abundant, hard to obtain, and very expensive to curate. However, research in other domains suggests that you don’t necessarily require more data to achieve training objectives in LLM training. ... The LIMI framework demonstrates that sophisticated agentic intelligence can emerge from minimal but strategically curated demonstrations of autonomous behavior. Key to the framework is a pipeline for collecting high-quality demonstrations of agentic tasks. Each demonstration consists of two parts: a query and a trajectory. A query is a natural language request from a user, such as a software development requirement or a scientific research goal.  ... “This discovery fundamentally reshapes how we develop autonomous AI systems, suggesting that mastering agency requires understanding its essence, not scaling training data,” the researchers write. “As industries transition from thinking AI to working AI, LIMI provides a paradigm for sustainable cultivation of truly agentic intelligence.”

CISOs advised to rethink vulnerability management as exploits sharply rise

The widening gap between exposure and response makes it impractical for security teams to rely on traditional approaches. The countermeasure is not “patch everything faster,” but “patch smarter” by taking advantage of security intelligence, according to Lefkowitz. Enterprises should evolve beyond reactive patch cycles and embrace risk-based, intelligence-led vulnerability remediation. “That means prioritizing vulnerabilities that are remotely exploitable, actively exploited in the wild, or tied to active adversary campaigns while factoring in business context and likely attacker behaviors,” Lefkowitz says. ... Yüceel adds: “A risk-based approach helps organizations focus on the threats that will most likely affect their infrastructure and operations. This means organizations should prioritize vulnerabilities that can be considered exploitable, while de-prioritizing vulnerabilities that can be effectively mitigated or defended against, even if their CVSS score is rated critical.” ... “Smart organizations are layering CVE data with real-time threat intelligence to create more nuanced and actionable security strategies,” Rana says. Instead of abandoning these trusted sources, effective teams are getting better at using them as part of a broader intelligence picture that helps them stay ahead of the threats that actually matter to their specific environment.

Modernizing Security and Resilience for AI Threats

For IT leaders, there may be concerns about the complexity and the risks of downtime and data loss. Operational leaders typically think of the impacts it will have on staffing demands and disruptions to business continuity. And it’s easy for security and compliance leaders to be worried about meeting regulatory standards without exposing the company’s data to new attacks. Most importantly, executive leadership can tend to be hesitant due to concerns around the total investment costs and disruption to innovation and revenue growth. While each leader may have their valid concerns, the risk of inaction is much greater. ... Fortunately, modernization doesn’t mean you need to take on a massive overhaul of your organization’s operations. Modernizing in place is an alternative solution that can be a sustainable, incremental strategy that improves stability, security, and performance without putting mission-critical systems at risk. When leaders can align on business continuity needs and concerns, they can develop low-risk approaches that still move operations forward while achieving long-term organizational goals. ... A modernization journey can take many forms. From updates to your on-prem system or migrating to a hybrid-cloud environment, modernization is a strategic initiative that can improve and bolster your company’s strength against potential data breaches.

Navigating AI Frontier — Role of Quality Engineering in GenAI

In the GenAI era, the role of Quality Engineering (QE) is under the spotlight like never before. Some whisper that QE may soon be obsolete after all, if developer agents can code autonomously, why not let GenAI-powered QE agents generate test cases from user stories, synthesize test data, and automate regression suites with near-perfect precision? Playwright and its peers are already showing glimpses of this future. In corporate corridors, by the water coolers, and in smoke breaks, the question lingers: Are we witnessing the sunset of QE as a discipline? The reality, however, is far more nuanced. QE is not disappearing it is being reshaped, redefined, and elevated to meet the demands of an AI-driven world. ... if test scripts pose one challenge, test data is an even trickier frontier. For testers, data that mirrors production is a blessing; data that strays too far is a nightmare. Left to itself, a large language model will naturally try to generate test data that looks very close to production. That may be convenient, but here’s the real question: can it stand up to compliance scrutiny? ... What we’ve explored so far only scratches the surface of why LLMs cannot and should not be seen as replacements for Quality Engineering. Yes, they can accelerate certain tasks, but they also expose blind spots, compliance risks, and the limits of context-free automation. 

Are Unified Networks Key to Cyber Resilience?

Fragmentation usually stems from a mix of issues. It can start with well-meaning decisions to buy tools for specific problems. Over time, this creates siloed data, consoles and teams, and it can take a lot of additional work to manage all the information coming from different sources. Ironically, instead of improving security, it can introduce new risks. Another factor is the misalignment of business processes as needs change. As business needs evolve and grow, the pressure to address specific requirements can drive IT and security processes in different directions. And finally, there is shadow IT, where employees attach new devices and applications to the network that haven’t been approved. If IT and security teams can’t keep pace with business initiatives, other teams across the organisation may seek to find their own solutions, sometimes bypassing official processes and adding to fragmentation. ... The bigger issue is that security teams risk becoming the ‘department of no’ instead of business enablers. A unified approach can help address this. By consolidating networking, security and observability into one unified platform, organisations have a single source of truth for managing network security. They can even automate reporting in some platforms, eliminating hours of manual work. With a single view of the entire network instead of putting together puzzle pieces from various applications, security teams see the big picture instantly, allowing them to prioritise what matters, respond faster and avoid burnout.

How CIOs Balance Emerging Technology and Technical Debt

"Technical debt isn't just an IT problem -- it's an innovation roadblock." Briggs pointed to Deloitte data showing 70% of technology leaders cite technical debt as their number one productivity drain. His advice? Take inventory before you innovate. "Know what's working versus what's just barely hanging on, because adding AI to broken processes doesn't fix them, it just breaks them faster," he said. ... "Everything kind of boils down to how the organizations are structured, how your teams are structured, what the goals are per team and what you're delivering," Caiafa said. At SS&C, some teams focus solely on maintaining legacy systems, while others support the integration of newer technologies. But, Caliafa said, the dual structure doesn't eliminate the challenge: Technical debt still accumulates as newer technologies are adopted. He advised CIOs to stay disciplined about prioritizing value. At SS&C, the approach is straightforward: "If it's not going to help us or make a material impact on what we're doing day to day, then it's not going to be an area of focus," he said. ... "Technical debt isn't just legacy code -- it's the accumulation of decisions made without long-term clarity," he said. Profico urged CIOs to embed architectural thinking into every IT initiative, align with business strategy and adopt of new technologies in an incremental manner -- while avoiding "the urge to over-index on shiny tools."

For Banks and Credit Unions, AI Can Be Risky. But What’s Riskier? Falling Behind.

"Over the past 18 months, I have not encountered a single financial services organization that said ‘we don’t need to do anything'" when it comes to AI, said Ray Barata, Director of CX Strategy at TTEC Digital, a global customer experience technology and services company. That said, though many banks and credit unions are highly motivated, and some may have the beginnings of a strategy in mind, they are frozen in place. Conditioned by decades of "garbage-in-garbage-out" data-integration horror stories, these institutions’ leaders have come to believe they must wait until their data architectures are deemed "ready" — a state that never arrives. Meanwhile, compliance and security concerns add more friction. And doubts over return on investment complete the picture. ... Barata emphasized the critical role "sandboxing" plays in the low-risk / high-impact approach — setting up a controlled test environment that mirrors the real conditions operating within the institution, but walled off from its operating environment. This enables experimentation within guardrails. Referring to TTEC Digital’s Sandcastle CX approach, he described this as "building an entire ecosystem in which we can measure performance of individual platform components and data sets" — so that sensitive information stays protected while teams trial AI safely and prove value before scaling.

What is vector search and when should you use it?

Vector search uses specialized language models (not the large LLMs such as ChatGPT, but targeted embedding models) to convert text into numerical representations, known as vectors, which capture the meaning of the text. This enables search engines to make connections between different terminologies. If you search for “car,” the system can also find documents that mention “vehicle” or “motor vehicle,” even if those exact terms do not appear. ... If semantic meaning is crucial, vector search can be a good solution. This is the case when users search for the same information using different words, or when a better search query can lead to increased revenue. A large e-commerce platform could potentially achieve 1 or 2 percent more revenue by applying vector search. The application of vector search is therefore immediately measurable. ... Vector search does add extra complexity. Documents or texts must be divided into chunks, then run through embedding models, and finally indexed efficiently. Elastic uses HNSW (Hierarchical Navigable Small World) indexing for this. To keep things from getting too complex, Elastic has chosen to integrate it into its existing search solution. It is an additional data type that can be stored in a column alongside existing data. This also makes hybrid search much easier. However, this is not so simple with every vector search provider.

Digital friction is where most AI initiatives fail

While the link between digital maturity and AI outcomes plays out across the enterprise, it is clearest in employee-facing use cases. Many AI tools being introduced into the workplace are designed to assist with routine tasks, surface relevant knowledge, or to summarise documents and automate repetitive workflows. ... With DEX maturity, organisations begin to change how they understand and deliver technology. Early efforts often focus narrowly on devices or support tickets. More mature organisations shift their focus toward employees, designing services around user personas, mapping full task journeys across tools and monitoring how those journeys perform in real time. Telemetry moves beyond technical diagnostics, becoming a strategic input for decision-making, investment planning and continuous improvement. Experience data becomes a foundation for IT operations and transformation. ... Where maturity is lacking, AI tends to be misapplied. Automation is aimed at the wrong processes. Recommendations appear in the wrong context. Systems respond to incomplete or misleading signals. The result is friction, not transformation. Organisations that have meaningful visibility into how work actually happens, and where it slows down, can identify where AI would make a measurable difference.
What it means for you

Daily Tech Digest - July 27, 2025

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Quote for the day:

"The only way to do great work is to love what you do." -- Steve Jobs

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Amazon AI coding agent hacked to inject data wiping commands

The hacker gained access to Amazon’s repository after submitting a pull request from a random account, likely due to workflow misconfiguration or inadequate permission management by the project maintainers. ... On July 23, Amazon received reports from security researchers that something was wrong with the extension and the company started to investigate. Next day, AWS released a clean version, Q 1.85.0, which removed the unapproved code. “AWS is aware of and has addressed an issue in the Amazon Q Developer Extension for Visual Studio Code (VSC). Security researchers reported a potential for unapproved code modification,” reads the security bulletin. “AWS Security subsequently identified a code commit through a deeper forensic analysis in the open-source VSC extension that targeted Q Developer CLI command execution.” “After which, we immediately revoked and replaced the credentials, removed the unapproved code from the codebase, and subsequently released Amazon Q Developer Extension version 1.85.0 to the marketplace.” AWS assured users that there was no risk from the previous release because the malicious code was incorrectly formatted and wouldn’t run on their environments.

How to migrate enterprise databases and data to the cloud

Migrating data is only part of the challenge; database structures, stored procedures, triggers and other code must also be moved. In this part of the process, IT leaders must identify and select migration tools that address the specific needs of the enterprise, especially if they’re moving between different database technologies (heterogeneous migration). Some things they’ll need to consider are: compatibility, transformation requirements and the ability to automate repetitive tasks.  ... During migration, especially for large or critical systems, IT leaders should keep their on-premises and cloud databases synchronized to avoid downtime and data loss. To help facilitate this, select synchronization tools that can handle the data change rates and business requirements. And be sure to test these tools in advance: High rates of change or complex data relationships can overwhelm some solutions, making parallel runs or phased cutovers unfeasible. ... Testing is a safety net. IT leaders should develop comprehensive test plans that cover not just technical functionality, but also performance, data integrity and user acceptance. Leaders should also plan for parallel runs, operating both on-premises and cloud systems in tandem, to validate that everything works as expected before the final cutover. They should engage end users early in the process in order to ensure the migrated environment meets business needs.

Researchers build first chip combining electronics, photonics, and quantum light

The new chip integrates quantum light sources and electronic controllers using a standard 45-nanometer semiconductor process. This approach paves the way for scaling up quantum systems in computing, communication, and sensing, fields that have traditionally relied on hand-built devices confined to laboratory settings. "Quantum computing, communication, and sensing are on a decades-long path from concept to reality," said Miloš Popović, associate professor of electrical and computer engineering at Boston University and a senior author of the study. "This is a small step on that path – but an important one, because it shows we can build repeatable, controllable quantum systems in commercial semiconductor foundries." ... "What excites me most is that we embedded the control directly on-chip – stabilizing a quantum process in real time," says Anirudh Ramesh, a PhD student at Northwestern who led the quantum measurements. "That's a critical step toward scalable quantum systems." This focus on stabilization is essential to ensure that each light source performs reliably under varying conditions. Imbert Wang, a doctoral student at Boston University specializing in photonic device design, highlighted the technical complexity.

Product Manager vs. Product Owner: Why Teams Get These Roles Wrong

While PMs work on the strategic plane, Product Owners anchor delivery. The PO is the guardian of the backlog. They translate the product strategy into epics and user stories, groom the backlog, and support the development team during sprints. They don’t just manage the “what” — they deeply understand the “how.” They answer developer questions, clarify scope, and constantly re-evaluate priorities based on real-time feedback. In Agile teams, they play a central role in turning strategic vision into working software. Where PMs answer to the business, POs are embedded with the dev team. They make trade-offs, adjust scope, and ensure the product is built right. ... Some products need to grow fast. That’s where Growth PMs come in. They focus on the entire user lifecycle, often structured using the PIRAT funnel: Problem, Insight, Reach, Activation, and Trust (a modern take on traditional Pirate Metrics, such as Acquisition, Activation, Retention, Referral, and Revenue). This model guides Growth PMs in identifying where user friction occurs and what levers to pull for meaningful impact. They conduct experiments, optimize funnels, and collaborate closely with marketing and data science teams to drive user growth. 

Ransomware payments to be banned – the unanswered questions

With thresholds in place, businesses/organisations may choose to operate differently so that they aren’t covered by the ban, such as lowering turnover or number of employees. All of this said, rules like this could help to get a better picture of what’s going on with ransomware threats in the UK. Arda Büyükkaya, senior cyber threat intelligence analyst at EclecticIQ, explains more: “As attackers evolve their tactics and exploit vulnerabilities across sectors, timely intelligence-sharing becomes critical to mounting an effective defence. Encouraging businesses to report incidents more consistently will help build a stronger national threat intelligence picture something that’s important as these attacks grow more frequent and become sophisticated. To spare any confusion, sector-specific guidance should be provided by government on how resources should be implemented, making resources clear and accessible. “Many victims still hesitate to come forward due to concerns around reputational damage, legal exposure, or regulatory fallout,” said Büyükkaya. “Without mechanisms that protect and support victims, underreporting will remain a barrier to national cyber resilience.” Especially in the earlier days of the legislation, organisations may still feel pressured to pay in order to keep operations running, even if they’re banned from doing so.

AI Unleashed: Shaping the Future of Cyber Threats

AI optimizes reconnaissance and targeting, giving hackers the tools to scour public sources, leaked and publicly available breach data, and social media to build detailed profiles of potential targets in minutes. This enhanced data gathering lets attackers identify high-value victims and network vulnerabilities with unprecedented speed and accuracy. AI has also supercharged phishing campaigns by automatically crafting phishing emails and messages that mimic an organization’s formatting and reference real projects or colleagues, making them nearly indistinguishable from genuine human-originated communications. ... AI is also being weaponized to write and adapt malicious code. AI-powered malware can autonomously modify itself to slip past signature-based antivirus defenses, probe for weaknesses, select optimal exploits, and manage its own command-and-control decisions. Security experts note that AI accelerates the malware development cycle, reducing the time from concept to deployment. ... AI presents more than external threats. It has exposed a new category of targets and vulnerabilities, as many organizations now rely on AI models for critical functions, such as authentication systems and network monitoring. These AI systems themselves can be manipulated or sabotaged by adversaries if proper safeguards have not been implemented.

Agile and Quality Engineering: Building a Culture of Excellence Through a Holistic Approach

Agile development relies on rapid iteration and frequent delivery, and this rhythm demands fast, accurate feedback on code quality, functionality, and performance. With continuous testing integrated into automated pipelines, teams receive near real-time feedback on every code commit. This immediacy empowers developers to make informed decisions quickly, reducing delays caused by waiting for manual test cycles or late-stage QA validations. Quality engineering also enhances collaboration between developers and testers. In a traditional setup, QA and development operate in silos, often leading to communication gaps, delays, and conflicting priorities. In contrast, QE promotes a culture of shared ownership, where developers write unit tests, testers contribute to automation frameworks, and both parties work together during planning, development, and retrospectives. This collaboration strengthens mutual accountability and leads to better alignment on requirements, acceptance criteria, and customer expectations. Early and continuous risk mitigation is another cornerstone benefit. By incorporating practices like shift-left testing, test-driven development (TDD), and continuous integration (CI), potential issues are identified and resolved long before they escalate. 

Could Metasurfaces be The Next Quantum Information Processors?

Broadly speaking, the work embodies metasurface-based quantum optics which, beyond carving a path toward room-temperature quantum computers and networks, could also benefit quantum sensing or offer “lab-on-a-chip” capabilities for fundamental science Designing a single metasurface that can finely control properties like brightness, phase, and polarization presented unique challenges because of the mathematical complexity that arises once the number of photons and therefore the number of qubits begins to increase. Every additional photon introduces many new interference pathways, which in a conventional setup would require a rapidly growing number of beam splitters and output ports. To bring order to the complexity, the researchers leaned on a branch of mathematics called graph theory, which uses points and lines to represent connections and relationships. By representing entangled photon states as many connected lines and points, they were able to visually determine how photons interfere with each other, and to predict their effects in experiments. Graph theory is also used in certain types of quantum computing and quantum error correction but is not typically considered in the context of metasurfaces, including their design and operation. The resulting paper was a collaboration with the lab of Marko Loncar, whose team specializes in quantum optics and integrated photonics and provided needed expertise and equipment.

New AI architecture delivers 100x faster reasoning than LLMs with just 1,000 training examples

When faced with a complex problem, current LLMs largely rely on chain-of-thought (CoT) prompting, breaking down problems into intermediate text-based steps, essentially forcing the model to “think out loud” as it works toward a solution. While CoT has improved the reasoning abilities of LLMs, it has fundamental limitations. In their paper, researchers at Sapient Intelligence argue that “CoT for reasoning is a crutch, not a satisfactory solution. It relies on brittle, human-defined decompositions where a single misstep or a misorder of the steps can derail the reasoning process entirely.” ... To move beyond CoT, the researchers explored “latent reasoning,” where instead of generating “thinking tokens,” the model reasons in its internal, abstract representation of the problem. This is more aligned with how humans think; as the paper states, “the brain sustains lengthy, coherent chains of reasoning with remarkable efficiency in a latent space, without constant translation back to language.” However, achieving this level of deep, internal reasoning in AI is challenging. Simply stacking more layers in a deep learning model often leads to a “vanishing gradient” problem, where learning signals weaken across layers, making training ineffective. 

For the love of all things holy, please stop treating RAID storage as a backup

Although RAID is a backup by definition, practically, a backup doesn't look anything like a RAID array. That's because an ideal backup is offsite. It's not on your computer, and ideally, it's not even in the same physical location. Remember, RAID is a warranty, and a backup is insurance. RAID protects you from inevitable failure, while a backup protects you from unforeseen failure. Eventually, your drives will fail, and you'll need to replace disks in your RAID array. This is part of routine maintenance, and if you're operating an array for long enough, you should probably have drive swaps on a schedule of several years to keep everything operating smoothly. A backup will protect you from everything else. Maybe you have multiple drives fail at once. A backup will protect you. Lord forbid you fall victim to a fire, flood, or other natural disaster and your RAID array is lost or damaged in the process. A backup still protects you. It doesn't need to be a fire or flood for you to get use out of a backup. There are small issues that could put your data at risk, such as your PC being infected with malware, or trying to write (and replicate) corrupted data. You can dream up just about any situation where data loss is a risk, and a backup will be able to get your data back in situations where RAID can't.