Daily Tech Digest - July 27, 2025


Quote for the day:

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


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. 

Daily Tech Digest - July 26, 2025


Quote for the day:

"Small daily improvements over time lead to stunning results." -- Robin Sharma


Data Engineering in the Age of AI: Skills To Master Now

Streaming requires a new mindset. You must reason about event time compared to processing time, manage watermarking and windowing and guarantee exactly-once semantics even when things change midstream. These design patterns must be built into your pipelines from the beginning. ... Agentic AI stretches the typical data engineer’s streaming data skill set because it is no longer about a single model running in isolation. Today, we see networks of perception agents, reasoning agents and execution agents working together, each handling tasks and passing insights to the next in real time. If you know only how to schedule batch ETL jobs or deploy an inference server, you’re missing a core skill: how to build high-throughput, low-latency pipelines that keep these agents reliable and responsive in production. ... A single slow or broken stream can cause cascading failures in multiagent systems. Use schema registries, enforce data contracts and apply exactly-once semantics to maintain trust in your streaming infrastructure. ... Communication presents another challenge. Data scientists often discuss “precision” as a metric that data engineers must translate into reality. Implement evaluation scores like factual consistency checks, entity precision comparisons and human-in-the-loop review pipelines.


Anthropic unveils ‘auditing agents’ to test for AI misalignment

Anthropic said the first environment it developed tests an agent’s ability to complete an alignment audit for an intentionally misaligned model. For this test, the researchers brought the investigator agent to the environment and equipped it with similar tools that human teams use. The researchers instructed the agent to identify the root cause of the misalignment and determine the specific behaviors that were misaligned. According to the researchers, the investigator agent successfully found the root cause of the issues 10-13% of the time. Still, this performance improved to 42% “when using a super-agent approach that aggregates findings across multiple investigations.” ... The next step in most alignment audits is typically a behavioral evaluation, which determines whether the model consistently misbehaves in various settings. To test its evaluation agent, the researchers designed 10 models “given system prompt instructions to exhibit a specific behavior, such as excessive deference to the user.” They ran the agent five times per model and saw that the agent correctly finds and flags at least one quirk of the model. However, the agent sometimes failed to identify specific quirks consistently. It had trouble evaluating subtle quirks, such as self-promotion and research-sandbagging, as well as quirks that are difficult to elicit, like the Hardcode Test Cases quirk.


The agentic experience: Is MCP the right tool for your AI future?

As enterprises race to operationalize AI, the challenge isn't only about building and deploying large language models (LLMs), it's also about integrating them seamlessly into existing API ecosystems while maintaining enterprise level security, governance, and compliance. Apigee is committed to lead you in this journey. Apigee streamlines the integration of gen AI agents into applications by bolstering their security, scalability, and governance. While the Model Context Protocol (MCP) has emerged as a de facto method of integrating discrete APIs as tools, the journey of turning your APIs into these agentic tools is broader than a single protocol. This post highlights the critical role of your existing API programs in this evolution and how ... Leveraging MCP services across a network requires specific security constraints. Perhaps you would like to add authentication to your MCP server itself. Once you’ve authenticated calls to the MCP server you may want to authorize access to certain tools depending on the consuming application. You may want to provide first class observability information to track which tools are being used and by whom. Finally, you may want to ensure that whatever downstream APIs your MCP server is supplying tools for also has minimum guarantees of security like already outlined above


AI Innovation: 4 Steps For Enterprises To Gain Competitive Advantage

A skill is a single ability, such as the ability to write a message or analyze a spreadsheet and trigger actions from that analysis. An agent independently handles complex, multi-step processes to produce a measurable outcome. We recently announced an expanded network of Joule Agents to help foster autonomous collaboration across systems and lines of business. This includes out-of-the-box agents for HR, finance, supply chain, and other functions that companies can deploy quickly to help automate critical workflows. AI front-runners, such as Ericsson, Team Liquid, and Cirque du Soleil, also create customized agents that can tackle specific opportunities for process improvement. Now you can build them with Joule Studio, which provides a low-code workspace to help design, orchestrate, and manage custom agents using pre-defined skills, models, and data connections. This can give you the power to extend and tailor your agent network to your exact needs and business context. ... Another way to become an AI front-runner is to tackle fragmented tools and solutions by putting in place an open, interoperable ecosystem. After all, what good is an innovative AI tool if it runs into blockers when it encounters your other first- and third-party solutions? 


Hard lessons from a chaotic transformation

The most difficult part of this transformation wasn’t the technology but getting people to collaborate in new ways, which required a greater focus on stakeholder alignment and change management. So my colleague first established a strong governance structure. A steering committee with leaders from key functions like IT, operations, finance, and merchandising met biweekly to review progress and resolve conflicts. This wasn’t a token committee, but a body with authority. If there were any issues with data exchange between marketing and supply chain, they were addressed and resolved during the meetings. By bringing all stakeholders together, we were also able to identify discrepancies early on. For example, when we discovered a new feature in the inventory system could slow down employee workflows, the operations manager reported it, and we immediately adjusted the rollout plan. Previously, such issues might not have been identified until after the full rollout and subsequent finger-pointing between IT and business departments. The next step was to focus on communication and culture. From previous failed projects, we knew that sending a few emails wasn’t enough, so we tried a more personal approach. We identified influential employees in each department and recruited them as change champions.


Benchmarks for AI in Software Engineering

HumanEval and SWE-bench have taken hold in the ML community, and yet, as indicated above, neither is necessarily reflective of LLMs’ competence in everyday software engineering tasks. I conjecture one of the reasons is the differences in points of view of the two communities! The ML community prefers large-scale, automatically scored benchmarks, as long as there is a “hill climbing” signal to improve LLMs. The business imperative for LLM makers to compete on popular leaderboards can relegate the broader user experience to a secondary concern. On the other hand, the software engineering community needs benchmarks that capture specific product experiences closely. Because curation is expensive, the scale of these benchmarks is sufficient only to get a reasonable offline signal for the decision at hand (A/B testing is always carried out before a launch). Such benchmarks may also require a complex setup to run, and sometimes are not automated in scoring; but these shortcomings can be acceptable considering a smaller scale. For exactly these reasons, these are not useful to the ML community. Much is lost due to these different points of view. It is an interesting question as to how these communities could collaborate to bridge the gap between scale and meaningfulness and create evals that work well for both communities.


Scientists Use Cryptography To Unlock Secrets of Quantum Advantage

When a quantum computer successfully handles a task that would be practically impossible for current computers, this achievement is referred to as quantum advantage. However, this advantage does not apply to all types of problems, which has led scientists to explore the precise conditions under which it can actually be achieved. While earlier research has outlined several conditions that might allow for quantum advantage, it has remained unclear whether those conditions are truly essential. To help clarify this, researchers at Kyoto University launched a study aimed at identifying both the necessary and sufficient conditions for achieving quantum advantage. Their method draws on tools from both quantum computing and cryptography, creating a bridge between two fields that are often viewed separately. ... “We were able to identify the necessary and sufficient conditions for quantum advantage by proving an equivalence between the existence of quantum advantage and the security of certain quantum cryptographic primitives,” says corresponding author Yuki Shirakawa. The results imply that when quantum advantage does not exist, then the security of almost all cryptographic primitives — previously believed to be secure — is broken. Importantly, these primitives are not limited to quantum cryptography but also include widely-used conventional cryptographic primitives as well as post-quantum ones that are rapidly evolving.


It’s time to stop letting our carbon fear kill tech progress

With increasing social and regulatory pressure, reluctance by a company to reveal emissions is ill-received. For example, in Europe the Corporate Sustainability Reporting Directive (CSRD) currently requires large businesses to publish their emissions and other sustainability datapoints. Opaque sustainability reporting undermines environmental commitments and distorts the reference points necessary for net zero progress. How can organisations work toward a low-carbon future when its measurement tools are incomplete or unreliable? The issue is particularly acute regarding Scope 3 emissions. Scope 3 emissions often account for the largest share of a company’s carbon footprint and are those generated indirectly along the supply chain by a company’s vendors, including emissions from technology infrastructure like data centres. ... It sounds grim, but there is some cause for optimism. Most companies are in a better position than they were five years ago and acknowledge that their measurement capabilities have improved. We need to accelerate the momentum of this progress to ensure real action. Earth Overshoot Day is a reminder that climate reporting for the sake of accountability and compliance only covers the basics. The next step is to use emissions data as benchmarks for real-world progress.


Why Supply Chain Resilience Starts with a Common Data Language

Building resilience isn’t just about buying more tech, it’s about making data more trustworthy, shareable, and actionable. That’s where global data standards play a critical role. The most agile supply chains are built on a shared framework for identifying, capturing, and sharing data. When organizations use consistent product and location identifiers, such as GTINs (Global Trade Item Numbers) and GLNs (Global Location Numbers) respectively, they reduce ambiguity, improve traceability, and eliminate the need for manual data reconciliation. With a common data language in place, businesses can cut through the noise of siloed systems and make faster, more confident decisions. ... Companies further along in their digital transformation can also explore advanced data-sharing standards like EPCIS (Electronic Product Code Information Services) or RFID (radio frequency identification) tagging, particularly in high-volume or high-risk environments. These technologies offer even greater visibility at the item level, enhancing traceability and automation. And the benefits of this kind of visibility extend far beyond trade compliance. Companies that adopt global data standards are significantly more agile. In fact, 58% of companies with full standards adoption say they manage supply chain agility “very well” compared to just 14% among those with no plans to adopt standards, studies show.


Opinion: The AI bias problem hasn’t gone away you know

When we build autonomous systems and allow them to make decisions for us, we enter a strange world of ethical limbo. A self-driving car forced to make a similar decision to protect the driver or a pedestrian in a case of a potentially fatal crash will have much more time than a human to make its choice. But what factors influence that choice? ... It’s not just the AI systems shaping the narrative, raising some voices while quieting others. Organisations made up of ordinary flesh-and-blood people are doing it too. Irish cognitive scientist Abeba Birhane, a highly-regarded researcher of human behaviour, social systems and responsible and ethical artificial intelligence was asked to give a keynote recently for the AI for Good Global Summit. According to her own reports on Bluesky, a meeting was requested just hours before presenting her keynote: “I went through an intense negotiation with the organisers (for over an hour) where we went through my slides and had to remove anything that mentions ‘Palestine’ ‘Israel’ and replace ‘genocide’ with ‘war crimes’…and a slide that explains illegal data torrenting by Meta, I also had to remove. In the end, it was either remove everything that names names (Big Tech particularly) and remove logos, or cancel my talk.” 

Daily Tech Digest - July 25, 2025


 Quote for the day:

"Technology changes, but leadership is about clarity, courage, and creating momentum where none exists." -- Inspired by modern digital transformation principles


Why foundational defences against ransomware matter more than the AI threat

The 2025 Cyber Security Breaches Survey paints a concerning picture. According to the study, ransomware attacks doubled between 2024 and 2025 – a surge less to do with AI innovation and more about deep-rooted economic, operational and structural changes within the cybercrime ecosystem. At the heart of this growth in attacks is the growing popularity of the ransomware-as-a-service (RaaS) business model. Groups like DragonForce or Ransomhub sell ready-made ransomware toolkits to affiliates in exchange for a cut of the profits, enabling even low-skilled attackers to conduct disruptive campaigns. ... Breaches often stem from common, preventable issues such as poor credential hygiene or poorly configured systems – areas that often sit outside scheduled assessments. When assessments happen only once or twice a year, new gaps may go unnoticed for months, giving attackers ample opportunity. To keep up, organisations need faster, more continuous ways of validating defences. ... Most ransomware actors follow well-worn playbooks, making them frequent visitors to company networks but not necessarily sophisticated ones. That’s why effective ransomware prevention is not about deploying cutting-edge technologies at every turn – it’s about making sure the basics are consistently in place. 


Subliminal learning: When AI models learn what you didn’t teach them

“Subliminal learning is a general phenomenon that presents an unexpected pitfall for AI development,” the researchers from Anthropic, Truthful AI, the Warsaw University of Technology, the Alignment Research Center, and UC Berkeley, wrote in their paper. “Distillation could propagate unintended traits, even when developers try to prevent this via data filtering.” ... Models trained on data generated by misaligned models, where AI systems diverge from their original intent due to bias, flawed algorithms, data issues, insufficient oversight, or other factors, and produce incorrect, lewd or harmful content, can also inherit that misalignment, even if the training data had been carefully filtered, the researchers found. They offered examples of harmful outputs when student models became misaligned like their teachers, noting, “these misaligned responses are egregious far beyond anything in the training data, including endorsing the elimination of humanity and recommending murder.” ... Today’s multi-billion parameter models are able to discern extremely complicated relationships between a dataset and the preferences associated with that data, even if it’s not immediately obvious to humans, he noted. This points to a need to look beyond semantic and direct data relationships when working with complex AI models.


Why people-first leadership wins in software development

It frequently involves pushing for unrealistic deadlines, with project schedules made without enough input from the development team about the true effort needed and possible obstacles. This results in ongoing crunch periods and mandatory overtime. ... Another indicator is neglecting signs of burnout and stress. Leaders may ignore or dismiss signals such as team members consistently working late, increased irritability, or a decline in productivity, instead pushing for more output without addressing the root causes. Poor work-life balance becomes commonplace, often without proper recognition or rewards for the extra effort. ... Beyond the code, there’s a stifled innovation and creativity. When teams are constantly under pressure to just “ship it,” there’s little room for creative problem-solving, experimentation, or thinking outside the box. Innovation, often born from psychological safety and intellectual freedom, gets squashed, hindering your company’s ability to adapt to new trends and stay competitive. Finally, there’s damage to your company’s reputation. In the age of social media and employer review sites, news travels fast. ... It’s vital to invest in team growth and development. Provide opportunities for continuous learning, training, and skill enhancement. This not only boosts individual capabilities but also shows your commitment to their long-term career paths within your organization. This is a crucial retention strategy.


Achieving resilience in financial services through cloud elasticity and automation

In an era of heightened regulatory scrutiny, volatile markets, and growing cybersecurity threats, resilience isn’t just a nice-to-have—it’s a necessity. A lack of robust operational resilience can lead to regulatory penalties, damaged reputations, and crippling financial losses. In this context, cloud elasticity, automation, and cutting-edge security technologies are emerging as crucial tools for financial institutions to not only survive but thrive amidst these evolving pressures. ... Resilience ensures that financial institutions can maintain critical operations during crises, minimizing disruptions and maintaining service quality. Efficient operations are crucial for maintaining competitive advantage and customer satisfaction. ... Effective resilience strategies help institutions manage diverse risks, including cyber threats, system failures, and third-party vulnerabilities. The complexity of interconnected systems and the rapid pace of technological advancement add layers of risk that are difficult to manage. ... Financial institutions are particularly susceptible to risks such as system failures, cyberattacks, and third-party vulnerabilities. ... As financial institutions navigate a landscape marked by heightened risk, evolving regulations, and increasing customer expectations, operational resilience has become a defining imperative.


Digital attack surfaces expand as key exposures & risks double

Among OT systems, the average number of exposed ports per organisation rose by 35%, with Modbus (port 502) identified as the most commonly exposed, posing risks of unauthorised commands and potential shutdowns of key devices. The exposure of Unitronics port 20256 surged by 160%. The report cites cases where attackers, such as the group "CyberAv3ngers," targeted industrial control systems during conflicts, exploiting weak or default passwords. ... The number of vulnerabilities identified on public-facing assets more than doubled, rising from three per organisation in late 2024 to seven in early 2025. Critical vulnerabilities dating as far back as 2006 and 2008 still persist on unpatched systems, with proof-of-concept code readily available online, making exploitation accessible even to attackers with limited expertise. The report also references the continued threat posed by ransomware groups who exploit such weaknesses in internet-facing devices. ... Incidents involving exposed access keys, including cloud and API keys, doubled from late 2024 to early 2025. Exposed credentials can enable threat actors to enter environments as legitimate users, bypassing perimeter defenses. The report highlights that most exposures result from accidental code pushes to public repositories or leaks on criminal forums.


How Elicitation in MCP Brings Human-in-the-Loop to AI Tools

Elicitation represents more than an incremental protocol update. It marks a shift toward collaborative AI workflows, where the system and human co-discover missing context rather than expecting all details upfront. Python developers building MCP tools can now focus on core logic and delegate parameter gathering to the protocol itself, allowing for a more streamlined approach. Clients declare an elicitation capability during initialization, so servers know they may elicit input at any time. That standardized interchange liberates developers from generating custom UIs or creating ad hoc prompts, ensuring coherent behaviour across diverse MCP clients. ... Elicitation transforms human-in-the-loop (HITL) workflows from an afterthought to a core capability. Traditional AI systems often struggle with scenarios that require human judgment, approval, or additional context. Developers had to build custom solutions for each case, leading to inconsistent experiences and significant development overhead. With elicitation, HITL patterns become natural extensions of tool functionality. A database migration tool can request confirmation before making irreversible changes. A document generation system can gather style preferences and content requirements through guided interactions. An incident response tool can collect severity assessments and stakeholder information as part of its workflow.


Cognizant Agents Gave Hackers Passwords, Clorox Says in Lawsuit

“Cognizant was not duped by any elaborate ploy or sophisticated hacking techniques,” the company says in its partially redacted 19-page complaint. “The cybercriminal just called the Cognizant Service Desk, asked for credentials to access Clorox’s network, and Cognizant handed the credentials right over. Cognizant is on tape handing over the keys to Clorox’s corporate network to the cybercriminal – no authentication questions asked.” ... The threat actors made multiple calls to the Cognizant help desk, essentially asking for new passwords and getting them without any effort to verify them, Clorox wrote. They then used those new credentials to gain access to the corporate network, launching a “debilitating” attack that “paralyzed Clorox’s corporate network and crippled business operations. And to make matters worse, when Clorox called on Cognizant to provide incident response and disaster recovery support services, Cognizant botched its response and compounded the damage it had already caused.” In statement to media outlets, a Cognizant spokesperson said it was “shocking that a corporation the size of Clorox had such an inept internal cybersecurity system to mitigate this attack.” While Clorox is placing the blame on Cognizant, “the reality is that Clorox hired Cognizant for a narrow scope of help desk services which Cognizant reasonably performed. Cognizant did not manage cybersecurity for Clorox,” the spokesperson said.


Digital sovereignty becomes a matter of resilience for Europe

Open-source and decentralized technologies are essential to advancing Europe’s strategic autonomy. Across cybersecurity, communications, and foundational AI, we’re seeing growing support for open-source infrastructure, now treated with the same strategic importance once reserved for energy, water and transportation. The long-term goal is becoming clear: not to sever global ties, but to reduce dependencies by building credible, European-owned alternatives to foreign-dominated systems. Open-source is a cornerstone of this effort. It empowers European developers and companies to innovate quickly and transparently, with full visibility and control, essential for trust and sovereignty. Decentralized systems complement this by increasing resilience against cyber threats, monopolistic practices and commercial overreach by “big tech”. While public investment is important, what Europe needs most is a more “risk-on” tech environment, one that rewards ambition, accelerated growth and enables European players to scale and compete globally. Strategic autonomy won’t be achieved by funding alone, but by creating the right innovation and investment climate for open technologies to thrive. Many sovereign platforms emphasize end-to-end encryption, data residency, and open standards. Are these enough to ensure trust, or is more needed to truly protect digital independence?



Building better platforms with continuous discovery

Platform teams are often judged by stability, not creativity. Balancing discovery with uptime and reliability takes effort. So does breaking out of the “tickets and delivery” cycle to explore problems upstream. But the teams that manage it? They build platforms that people want to use, not just have to use. Start by blocking time for discovery in your sprint planning, measuring both adoption and friction metrics, and most importantly, talking to your users periodically rather than waiting for them to come to you with problems. Cultural shifts like this take time because you're not just changing the process; you're changing what people believe is acceptable or expected. That kind of change doesn't happen just because leadership says it should, or because a manager adds a new agenda to planning meetings. It sticks when ICs feel inspired and safe enough to work differently and when managers back that up with support and consistency. Sometimes a C-suite champion helps set the tone, but day-to-day, it's middle managers and senior ICs who do the slow, steady work of normalizing new behavior. You need repeated proof that it's okay to pause and ask why, to explore, to admit uncertainty. Without that psychological safety, people just go back to what they know: deliverables and deadlines. 


AI-enabled software development: Risk of skill erosion or catalyst for growth?

We need to reframe AI not as a rival, but as a tool—one that has its own pros and cons and can extend human capability, not devalue it. This shift in perspective opens the door to a broader understanding of what it means to be a skilled engineer today. Using AI doesn’t eliminate the need for expertise—it changes the nature of that expertise. Classical programming, once central to the developer’s identity, becomes one part of a larger repertoire. In its place emerge new competencies: critical evaluation, architectural reasoning, prompt literacy, source skepticism, interpretative judgment. These are not hard skills, but meta-cognitive abilities—skills that require us to think about how we think. We’re not losing cognitive effort—we’re relocating it. This transformation mirrors earlier technological shifts. ... Some of the early adopters of AI enablement are already looking ahead—not just at the savings from replacing employees with AI, but at the additional gains those savings might unlock. With strategic investment and redesigned expectations, AI can become a growth driver—not just a cost-cutting tool. But upskilling alone isn’t enough. As organizations embed AI deeper into the development workflow, they must also confront the technical risks that come with automation. The promise of increased productivity can be undermined if these tools are applied without adequate context, oversight, or infrastructure.

Daily Tech Digest - July 23, 2025


Quote for the day:

“Our chief want is someone who will inspire us to be what we know we could be.” -- Ralph Waldo Emerson


AI in customer communication: the opportunities and risks SMBs can’t ignore

To build consumer trust, businesses must demonstrate that AI genuinely improves the customer experience, especially by enhancing the quality, relevance and reliability of communication. With concerns around data misuse and inaccuracy, businesses need to clearly explain how AI supports secure, accurate and personalized interactions, not just internally but in ways customers can understand and see. AI should be positioned as an enabler of human service, taking care of routine tasks so employees can focus on complex, sensitive or high-value customer needs. A key part of gaining long-term trust is transparency around data. Businesses must clearly communicate how customer information is handled securely and show that AI is being used responsibly and with care. This could include clearly labelling AI-generated communications such as emails or text messages, or proactively informing customers about what data is being used and for what purpose.  ... As conversations move beyond why AI should be used to how it must be used responsibly and effectively, companies have entered a make-or-break “audition phase” for AI. In customer communications, businesses can no longer afford to just talk about AI’s benefits, they must prove them by demonstrating how it enhances quality, security, and personalization.


The Expiring Trust Model: CISOs Must Rethink PKI in the Era of Short-Lived Certificates and Machine Identity

While the risk associated with certificates applies to all companies, it is a greater challenge for businesses operating in regulated sectors such as healthcare, where certificates must often be tied to national digital identity systems. In several countries, healthcare providers and services are now required to issue certificates bound to a National Health Identifier (NHI). These certificates are used for authentication, e-signature and encryption in health data exchanges and must adhere to complex issuance workflows, usage constraints and revocation processes mandated by government frameworks. Managing these certificates alongside public TLS certificates introduces operational complexity that few legacy PKI solutions were designed to handle in today’s dynamic and cloud-native environments. ... The urgency of this mandate is heightened by the impending cryptographic shift driven by the rise of quantum computing. Transitioning to post-quantum cryptography (PQC) will require organizations to implement new algorithms quickly and securely. Frequent certificate renewal cycles, which once seemed a burden, could now become a strategic advantage. When managed through automated and agile certificate lifecycle management, these renewals provide the flexibility to rapidly replace compromised keys, rotate certificate authorities or deploy quantum-safe algorithms as they become standardized.


The CISO code of conduct: Ditch the ego, lead for real

The problem doesn’t stop at vendor interactions. It shows up inside their teams, too. Many CISOs don’t build leadership pipelines; they build echo chambers. They hire people who won’t challenge them. They micromanage strategy. They hoard influence. And they act surprised when innovation dries up or when great people leave. As Jadee Hanson, CISO at Vanta, put it, “Ego builds walls. True leadership builds trust. The best CISOs know the difference.” That distinction matters, especially when your team’s success depends on your ability to listen, adapt, and share the stage. ... Security isn’t just a technical function anymore. It’s a leadership discipline. And that means we need more than frameworks and certifications; we need a shared understanding of how CISOs should show up. Internally, externally, in boardrooms, and in the broader community. That’s why I’m publishing this. Not because I have all the answers, but because the profession needs a new baseline. A new set of expectations. A standard we can hold ourselves, and each other, to. Not about compliance. About conduct. About how we lead. What follows is the CISO Code of Conduct. It’s not a checklist, but a mindset. If you recognize yourself in it, good. If you don’t, maybe it’s time to ask why. Either way, this is the bar. Let’s hold it. ... A lot of people in this space are trying to do the right thing. But there are also a lot of people hiding behind a title.


Phishing simulations: What works and what doesn’t

Researchers conducted a study on the real-world effectiveness of common phishing training methods. They found that the absolute difference in failure rates between trained and untrained users was small across various types of training content. However, we should take this with caution, as the study was conducted within a single healthcare organization and focused only on click rates as the measure of success or failure. It doesn’t capture the full picture. Matt Linton, Google’s security manager, said phishing tests are outdated and often cause more frustration among employees than actually improving their security habits. ... For any training program to work, you first need to understand your organization’s risk. Which employees are most at risk? What do they already know about phishing? Next, work closely with your IT or security teams to create phishing tests that match current threats. Tell employees what to expect. Explain why these tests matter and how they help stop problems. Don’t play the blame game. If someone fails a test, treat it as a chance to learn, not to punish. When you do this, employees are less likely to hide mistakes or avoid reporting phishing emails. When picking a vendor, focus on content and realistic simulations. The system should be easy to use and provide helpful reports.


Reclaiming Control: How Enterprises Can Fix Broken Security Operations

Asset management is critical to the success of the security operations function. In order to properly defend assets, I first and foremost need to know about them and be able to manage them. This includes applying policies, controls, and being able to identify assets and their locations when necessary, of course. With the move to hybrid and multi-cloud, asset management is much more difficult than it used to be. ... Visibility enables another key component of security operations – telemetry collection. Without the proper logging, eventing, and alerting, I can’t detect, investigate, analyze, respond to, and mitigate security incidents. Security operations simply cannot operate without telemetry, and the hybrid and multi-cloud world has made telemetry collection much more difficult than it used to be. ... If a security incident is serious enough, there will need to be a formal incident response. This will involve significant planning, coordination with a variety of stakeholders, regular communications, structured reporting, ongoing analysis, and a post-incident evaluation once the response is wrapped up. All of these steps are complicated by hybrid and multi-cloud environments, if not made impossible altogether. The security operations team will not be able to properly engage in incident response if they are lacking the above capabilities, and having a complex environment is not an excuse.


Legacy No More: How Generative AI is Powering the Next Wave of Application Modernization in India

Choosing the right approach to modernise your legacy systems is a task. Generative AI helps overcome the challenges faced in legacy systems and accelerates modernization. For example, it can be used to understand how legacy systems function through detailed business requirements. The resulting documents can be used to build new systems on the cloud in the second phase. This can make the process cheaper, too, and thus easier to get business cases approved. Additionally, generative AI can help create training documents for the current system if the organization wants to continue using its mainframes. In one example, generative AI might turn business models into microservices, API contracts, and database schemas ready for cloud-native inclusion. ... You need to have a holistic assessment of your existing system to implement generative AI effectively. Leaders must assess obsolete modules, interdependencies, data schemas, and throughput constraints to pinpoint high-impact targets and establish concrete modernization goals. Revamping legacy applications with generative AI starts with a clear understanding of the existing system. Organizations must conduct a thorough evaluation, mapping performance bottlenecks, obsolete modules, entanglements, and intricacies of the data flow, to create a modernization roadmap.


A Changing of the Guard in DevOps

Asimov, a newcomer in the space, is taking a novel approach — but addressing a challenge that’s as old as DevOps itself. According to the article, the team behind Asimov has zeroed in on a major time sink for developers: The cognitive load of understanding deployment environments and platform intricacies. ... What makes Asimov stand out is not just its AI capability but its user-centric focus. This isn’t another auto-coder. This is about easing the mental burden, helping engineers think less about YAML files and more about solving business problems. It’s a fresh coat of paint on a house we’ve been renovating for over a decade. ... Whether it’s a new player like Asimov or stalwarts like GitLab and Harness, the pattern is clear: AI is being applied to the same fundamental problems that have shaped DevOps from the beginning. The goals haven’t changed — faster cycles, fewer errors, happier teams — but the tools are evolving. Sure, there’s some real innovation here. Asimov’s knowledge-centric approach feels genuinely new. GitLab’s AI agents offer a logical evolution of their existing ecosystem. Harness’s plain-language chat interface lowers the barrier to entry. These aren’t just gimmicks. But the bigger story is the convergence. AI is no longer an outlier or an optional add-on — it’s becoming foundational. And as these solutions mature, we’re likely to see less hype and more impact.


Data Protection vs. Cyber Resilience: Mastering Both in a Complex IT Landscape

Traditional disaster recovery (DR) approaches designed for catastrophic events and natural disasters are still necessary today, but companies must implement a more security-event-oriented approach on top of that. Legacy approaches to disaster recovery are insufficient in an environment that is rife with cyberthreats as these approaches focus on infrastructure, neglecting application-level dependencies and validation processes. Further, threat actors have moved beyond interrupting services and now target data to poison, encrypt or exfiltrate it. ... Cyber resilience is now essential. With ransomware that can encrypt systems in minutes, the ability to recover quickly and effectively is a business imperative. Therefore, companies must develop an adaptive, layered strategy that evolves with emerging threats and aligns with their unique environment, infrastructure and risk tolerance. To effectively prepare for the next threat, technology leaders must balance technical sophistication with operational discipline as the best defence is not solely a hardened perimeter, it’s also having a recovery plan that works. Today, companies cannot afford to choose between data protection and cyber resilience, they must master both.


Anthropic researchers discover the weird AI problem: Why thinking longer makes models dumber

The findings challenge the prevailing industry wisdom that more computational resources devoted to reasoning will consistently improve AI performance. Major AI companies have invested heavily in “test-time compute” — allowing models more processing time to work through complex problems — as a key strategy for enhancing capabilities. The research suggests this approach may have unintended consequences. “While test-time compute scaling remains promising for improving model capabilities, it may inadvertently reinforce problematic reasoning patterns,” the authors conclude. For enterprise decision-makers, the implications are significant. Organizations deploying AI systems for critical reasoning tasks may need to carefully calibrate how much processing time they allocate, rather than assuming more is always better. ... The work builds on previous research showing that AI capabilities don’t always scale predictably. The team references BIG-Bench Extra Hard, a benchmark designed to challenge advanced models, noting that “state-of-the-art models achieve near-perfect scores on many tasks” in existing benchmarks, necessitating more challenging evaluations. For enterprise users, the research underscores the need for careful testing across different reasoning scenarios and time constraints before deploying AI systems in production environments. 


How to Advance from SOC Manager to CISO?

Strategic thinking demands a firm grip on the organization's core operations, particularly how it generates revenue and its key value streams. This perspective allows security professionals to align their efforts with business objectives, rather than operating in isolation. ... This is related to strategic thinking but emphasizes knowledge of risk management and finance. Security leaders must factor in financial impacts to justify security investments and manage risks effectively. Balancing security measures with user experience and system availability is another critical aspect. If security policies are too strict, productivity can suffer; if they're too permissive, the company can be exposed to threats. ... Effective communication is vital for translating technical details into language senior stakeholders can grasp and act upon. This means avoiding jargon and abbreviations to convey information in a simplistic manner that resonates with multiple stakeholders, including executives who may not have a deep technical background. Communicating the impact of security initiatives in clear, concise language ensures decisions are well-informed and support company goals. ... You will have to ensure technical services meet business requirements, particularly in managing service delivery, implementing change, and resolving issues. All of this is essential for a secure and efficient IT infrastructure.

Daily Tech Digest - July 22, 2025


Quote for the day:

“Being responsible sometimes means pissing people off.” -- Colin Powell


It might be time for IT to consider AI models that don’t steal

One option that has many pros and cons is to use genAI models that explicitly avoid training on any information that is legally dicey. There are a handful of university-led initiatives that say they try to limit model training data to information that is legally in the clear, such as open source or public domain material. ... “Is it practical to replace the leading models of today right now? No. But that is not the point. This level of quality was built on just 32 ethical data sources. There are millions more that can be used,” Wiggins wrote in response to a reader’s comment on his post. “This is a baseline that proves that Big AI lied. Efforts are underway to add more data that will bring it up to more competitive levels. It is not there yet.” Still, enterprises are investing in and planning for genAI deployments for the long term, and they may find in time that ethically sourced models deliver both safety and performance. ... Tipping the scales in the other direction is the big model makers’ promises of indemnification. Some genAI vendors have said they will cover the legal costs for customers who are sued over content produced by their models. “If the model provides indemnification, this is what enterprises should shoot for,” Moor’s Andersen said. 


The unique, mathematical shortcuts language models use to predict dynamic scenarios

One go-to pattern the team observed, called the “Associative Algorithm,” essentially organizes nearby steps into groups and then calculates a final guess. You can think of this process as being structured like a tree, where the initial numerical arrangement is the “root.” As you move up the tree, adjacent steps are grouped into different branches and multiplied together. At the top of the tree is the final combination of numbers, computed by multiplying each resulting sequence on the branches together. The other way language models guessed the final permutation was through a crafty mechanism called the “Parity-Associative Algorithm,” which essentially whittles down options before grouping them. It determines whether the final arrangement is the result of an even or odd number of rearrangements of individual digits. ... “These behaviors tell us that transformers perform simulation by associative scan. Instead of following state changes step-by-step, the models organize them into hierarchies,” says MIT PhD student and CSAIL affiliate Belinda Li SM ’23, a lead author on the paper. “How do we encourage transformers to learn better state tracking? Instead of imposing that these systems form inferences about data in a human-like, sequential way, perhaps we should cater to the approaches they naturally use when tracking state changes.”


Role of AI in fortifying cryptocurrency security

In the rapidly expanding realm of Decentralised Finance (DeFi), AI will play a critical role in optimising complex lending, borrowing, and trading protocols. AI can intelligently manage liquidity pools, optimise yield farming strategies for better returns and reduced impermanent loss, and even identify subtle arbitrage opportunities across various platforms. Crucially, AI will also be vital in identifying and mitigating novel types of exploits that are unique to the intricate and interconnected world of DeFi. Looking further ahead, AI will be crucial in developing Quantum-Resistant Cryptography. As quantum computing advances, it poses a theoretical threat to the underlying cryptographic methods that secure current blockchain networks. AI can significantly accelerate the research and development of “post-quantum cryptography” (PQC) algorithms, which are designed to withstand the immense computational power of future quantum computers. AI can also be used to simulate quantum attacks, rigorously testing existing and new cryptographic designs for vulnerabilities. Finally, the concept of Autonomous Regulation could redefine oversight in the crypto space. Instead of traditional, reactive regulatory approaches, AI-driven frameworks could provide real-time, proactive oversight without stifling innovation. 


From Visibility to Action: Why CTEM Is Essential for Modern Cybersecurity Resilience

CTEM shifts the focus from managing IT vulnerabilities in isolation to managing exposure in collaboration, something that’s far more aligned with the operational priorities of today’s organizations. Where traditional approaches center around known vulnerabilities and technical severity, CTEM introduces a more business-driven lens. It demands ongoing visibility, context-rich prioritization, and a tighter alignment between security efforts and organizational impact. In doing so, it moves the conversation from “What’s vulnerable?” to “What actually matters right now?” – a far more useful question when resilience is on the line. What makes CTEM particularly relevant beyond security teams is its emphasis on continuous alignment between exposure data and operational decision-making. This makes it valuable not just for threat reduction, but for supporting broader resilience efforts, ensuring resources are directed toward the exposures most likely to disrupt critical operations. It also complements, rather than replaces, existing practices like attack surface management (ASM). CTEM builds on these foundations with more structured prioritization, validation, and mobilization, turning visibility into actionable risk reduction. 


Driving Platform Adoption: Community Is Your Value

Remember that in a Platform as a Product approach, developers are your customers. If they don’t know what’s available, how to use it or what’s coming next, they’ll find workarounds. These conferences and speaker series are a way to keep developers engaged, improve adoption and ensure the platform stays relevant.There’s a human side to this, too often left out of focusing on “the business value” and outcomes in corporate-land: just having a friendly community of humans who like to spend time with each other and learn. ... Successful platform teams have active platform advocacy. This requires at least one person working full time to essentially build empathy with your users by working with and listening to the people who use your platforms. You may start with just one platform advocate who visits with developer teams, listening for feedback while teaching them how to use the platform and associated methodologies. The advocate acts as both a councilor and delegate for your developers.  ... The journey to successful platform adoption is more than just communicating technical prowess. Embracing systematic approaches to platform marketing that include clear messaging and positioning based on customers’ needs and a strong brand ethos is the key to communicating the value of your platform.


9 AI development skills tech companies want

“It’s not enough to know how a transformer model works; what matters is knowing when and why to use AI to drive business outcomes,” says Scott Weller, CTO of AI-powered credit risk analysis platform EnFi. “Developers need to understand the tradeoffs between heuristics, traditional software, and machine learning, as well as how to embed AI in workflows in ways that are practical, measurable, and responsible.” ... “In AI-first systems, data is the product,” Weller says. “Developers must be comfortable acquiring, cleaning, labeling, and analyzing data, because poor data hygiene leads to poor model performance.” ... AI safety and reliability engineering “looks at the zero-tolerance safety environment of factory operations, where AI failures could cause safety incidents or production shutdowns,” Miller says. To ensure the trust of its customers, IFS needs developers who can build comprehensive monitoring systems to detect when AI predictions become unreliable and implement automated rollback mechanisms to traditional control methods when needed, Miller says. ... “With the rapid growth of large language models, developers now require a deep understanding of prompt design, effective management of context windows, and seamless integration with LLM APIs—skills that extend well beyond basic ChatGPT interactions,” Tupe says.


Why AI-Driven Logistics and Supply Chains Need Resilient, Always-On Networks

Something worth noting about increased AI usage in supply chains is that as AI-enabled systems become more complex, they also become more delicate, which increases the potential for outages. Something as simple as a single misconfiguration or unintentional interaction between automated security gates can lead to a network outage, preventing supply chain personnel from accessing critical AI applications. During an outage, AI clusters (interconnected GPU/TPU nodes used for training and inference) can also become unavailable. .. Businesses must increase network resiliency to ensure their supply chain and logistics teams always have access to key AI applications, even during network outages and other disruptions. One approach that companies can take to strengthen network resilience is to implement purpose-built infrastructure like out of band (OOB) management. With OOB management, network administrators can separate and containerize functions of the management plane, allowing it to operate freely from the primary in-band network. This secondary network acts as an always-available, independent, dedicated channel that administrators can use to remotely access, manage, and troubleshoot network infrastructure.


From architecture to AI: Building future-ready data centers

In some cases, the pace of change is so fast that buildings are being retrofitted even as they are being constructed. Once CPUs are installed, O'Rourke has observed data center owners opting to upgrade racks row by row, rather than converting the entire facility to liquid cooling at once – largely because the building wasn’t originally designed to support higher-density racks. To accommodate this reality, Tate carries out in-row upgrades by providing specialized structures to mount manifolds, which distribute coolant from air-cooled chillers throughout the data halls. “Our role is to support the physical distribution of that cooling infrastructure,” explains O'Rourke. “Manifold systems can’t be supported by existing ceilings or hot aisle containment due to weight limits, so we’ve developed floor-mounted frameworks to hold them.” He adds: “GPU racks also can’t replace all CPU racks one-to-one, as the building structure often can’t support the added load. Instead, GPUs must be strategically placed, and we’ve created solutions to support these selective upgrades.” By designing manifold systems with actuators that integrate with the building management system (BMS), along with compatible hot aisle containment and ceiling structures, Tate has developed a seamless, integrated solution for the white space. 


Weaving reality or warping it? The personalization trap in AI systems

At first, personalization was a way to improve “stickiness” by keeping users engaged longer, returning more often and interacting more deeply with a site or service. Recommendation engines, tailored ads and curated feeds were all designed to keep our attention just a little longer, perhaps to entertain but often to move us to purchase a product. But over time, the goal has expanded. Personalization is no longer just about what holds us. It is what it knows about each of us, the dynamic graph of our preferences, beliefs and behaviors that becomes more refined with every interaction. Today’s AI systems do not merely predict our preferences. They aim to create a bond through highly personalized interactions and responses, creating a sense that the AI system understands and cares about the user and supports their uniqueness. The tone of a chatbot, the pacing of a reply and the emotional valence of a suggestion are calibrated not only for efficiency but for resonance, pointing toward a more helpful era of technology. It should not be surprising that some people have even fallen in love and married their bots. The machine adapts not just to what we click on, but to who we appear to be. It reflects us back to ourselves in ways that feel intimate, even empathic. 


Microsoft Rushes to Stop Hackers from Wreaking Global Havoc

Multiple different hackers are launching attacks through the Microsoft vulnerability, according to representatives of two cybersecurity firms, CrowdStrike Holdings, Inc. and Google's Mandiant Consulting. Hackers have already used the flaw to break into the systems of national governments in Europe and the Middle East, according to a person familiar with the matter. In the US, they've accessed government systems, including ones belonging to the US Department of Education, Florida's Department of Revenue and the Rhode Island General Assembly, said the person, who spoke on condition that they not be identified discussing the sensitive information. ... The breaches have drawn new scrutiny to Microsoft's efforts to shore up its cybersecurity after a series of high-profile failures. The firm has hired executives from places like the US government and holds weekly meetings with senior executives to make its software more resilient. The company's tech has been subject to several widespread and damaging hacks in recent years, and a 2024 US government report described the company's security culture as in need of urgent reforms. ... "There were ways around the patches," which enabled hackers to break into SharePoint servers by tapping into similar vulnerabilities, said Bernard. "That allowed these attacks to happen."