Daily Tech Digest by Kannan Subbiah: wireless

Showing posts with label wireless. Show all posts

Daily Tech Digest - February 06, 2026

Quote for the day:

"When you say my team is no good, all I hear is that I failed as a leader." -- Gordon Tredgold

Everyone works with AI agents, but who controls the agents?

Over the past year, there has been a lot of talk about MCP and A2A, protocols that allow agents to communicate with each other. But more and more agents that are now becoming available support and use them. Agents will soon be able to easily exchange information and transfer tasks to each other to achieve much better results. Currently, 50 percent of AI agents in organizations still work as a silo. This means that no context or data from external systems is added. The need for context is now clear to many organizations. 96 percent of IT decision-makers understand that success depends on seamless integration. This puts renewed pressure on data silos and integrations. ... For IT decision-makers wondering what they really need to do in 2026, doing nothing is definitely not the right answer, as your competitors who do invest in AI will quickly overtake you. On the other hand, you don’t have to go all-in and blow your entire IT budget on it. ... You need to start now, so start small. Putting the three or five most frequently asked questions to your customer service or HR team into an AI agent can take a huge workload off those teams. There are now several case studies showing that this has reduced the number of tickets by as much as 50-60 percent. AI can also be used for sales reports or planning, which currently takes employees many hours each week.

Mobile privacy audits are getting harder

Many privacy reviews begin with static analysis of an Android app package (APK). This can reveal permissions requested by the app and identify embedded third-party libraries such as advertising SDKs, telemetry tools, or analytics components. Requested permissions are often treated as indicators of risk because they can imply access to contacts, photos, location, camera, or device identifiers. Library detection can also show whether an app includes known trackers. Yet, static results are only partial. Permissions may never be used in runtime code paths, and libraries can be present without being invoked. Static analysis also misses cases where data is accessed indirectly or through system behavior that does not require explicit permissions. ... Apps increasingly defend against MITM using certificate pinning, which causes the app to reject traffic interception even if a root certificate is installed. Analysts may respond by patching the APK or using dynamic instrumentation to bypass the pinning logic at runtime. Both approaches can fail depending on the app’s implementation. Mopri’s design treats these obstacles as expected operating conditions. The framework includes multiple traffic capture approaches so investigators can switch methods when an app resists a specific setup. ... Raw network logs are difficult to interpret without enrichment. Mopri adds contextual information to recorded traffic in two areas: identifying who received the data, and identifying what sensitive information may have been transmitted.

When the AI goes dark: Building enterprise resilience for the age of agentic AI

Instead of merely storing data, AI accumulates intelligence. When we talk about AI “state,” we’re describing something fundamentally different from a database that can be rolled back. ... Lose this state, and you haven’t just lost data. You’ve lost the organizational intelligence that took hundreds of human days of annotation, iteration and refinement to create. You can’t simply re-enter it from memory. Worse, a corrupted AI state doesn’t announce itself the way a crashed server does. ... This challenge is compounded by the immaturity of the AI vendor landscape. Hyperscale cloud providers may advertise “four nines” of uptime (99.99% availability, which translates to roughly 52 minutes of downtime per year), but many AI providers, particularly the startups emerging rapidly in this space, cannot yet offer these enterprise-grade service guarantees. ... When AI agents handle customer interactions, manage supply chains, execute financial processes and coordinate operations, a sustained AI outage isn’t an inconvenience. It’s an existential threat. ... Humans are not just a fallback option. They are an integral component of a resilient AI-native enterprise. Motivated, trained and prepared teams can bridge gaps when AI fails, ensuring continuity of both systems and operations. When you continually reduce your workforce to appease your shareholders, will your human employees remain motivated, trained and prepared?

The blind spot every CISO must see: Loyalty

The insider who once seemed beyond reproach becomes the very vector through which sensitive data, intellectual property, or operational integrity is compromised. These are not isolated failures of vetting or technology; they are failures to recognize that loyalty is relational and conditional, not absolute. ... Organizations have long operated under the belief that loyalty, once demonstrated, becomes a durable shield against insider risk. Extended tenure is rewarded with escalating access privileges, high performers are granted broader system rights without commensurate behavioral review, and verbal affirmations of commitment are taken at face value. Yet time and again patterns repeat. What begins as mutual confidence weakens not through dramatic betrayal but through subtle realignments in personal commitment. An employee who once identified strongly with the mission may begin to feel undervalued, overlooked for advancement, or weighed down by outside pressures. ... Positions with access to crown jewels — sensitive data, financial systems, or personnel records — or executive ranks inherently require proportionately more oversight, as regulated sectors have shown. Professionals in these roles accept this as part of the terrain, with history demonstrating minimal talent loss when frameworks are transparent and supportive.

Researchers Warn: WiFi Could Become an Invisible Mass Surveillance System

Researchers at the Karlsruhe Institute of Technology (KIT) have shown that people can be recognized solely by recording WiFi communication in their surroundings, a capability they warn poses a serious threat to personal privacy. The method does not require individuals to carry any electronic devices, nor does it rely on specialized hardware. Instead, it makes use of ordinary WiFi devices already communicating with each other nearby. ... “This technology turns every router into a potential means for surveillance,” warns Julian Todt from KASTEL. “If you regularly pass by a café that operates a WiFi network, you could be identified there without noticing it and be recognized later, for example by public authorities or companies.” Felix Morsbach notes that intelligence agencies or cybercriminals currently have simpler ways to monitor people, such as accessing CCTV systems or video doorbells. “However, the omnipresent wireless networks might become a nearly comprehensive surveillance infrastructure with one concerning property: they are invisible and raise no suspicion.” ... Unlike attacks that rely on LIDAR sensors or earlier WiFi-based techniques that use channel state information (CSI), meaning measurements of how radio signals change when they reflect off walls, furniture, or people, this approach does not require specialized equipment. Instead, it can be carried out using a standard WiFi device.

Is software optimization a lost art?

Almost all of us have noticed apps getting larger, slower, and buggier. We've all had a Chrome window that's taking up a baffling amount of system memory, for example. While performance challenges can vary by organization, application and technical stacks, it appears the worst performance bottlenecks have migrated to the ‘last mile’ of the user experience, says Jim Mercer ... “While architectural decisions and developer skills remain critical, they’re too often compromised by the need to integrate AI and new features at an exponential pace. So, a lack of due diligence when we should know better.” ... The somewhat concerning part is that AI bloat is structurally different from traditional technical debt, she points out. Rather than accumulated cruft over time, it usually manifests as systematic over-engineering from day one. ... Software optimization has become even more important due to the recent RAM price crisis, driven by surging demand for hardware to meet AI and data center buildout. Though the price increases may be levelling out, RAM is now much more expensive than it was mere months ago. This is likely to shift practices and behavior, Brock ... Security will play a role too, particularly with the growing data sovereignty debate and concerns about bad actors, she notes. Leaner, neater, shorter software is simply easier to maintain – especially when you discover a vulnerability and are faced with working through a massive codebase.

The ‘Super Bowl’ standard: Architecting distributed systems for massive concurrency

In the world of streaming, the “Super Bowl” isn’t just a game. It is a distributed systems stress test that happens in real-time before tens of millions of people. ... It is the same nightmare that keeps e-commerce CTOs awake before Black Friday or financial systems architects up during a market crash. The fundamental problem is always the same: How do you survive when demand exceeds capacity by an order of magnitude? ... We implement load shedding based on business priority. It is better to serve 100,000 users perfectly and tell 20,000 users to “please wait” than to crash the site for all 120,000. ... In an e-commerce context, your “Inventory Service” and your “User Reviews Service” should never share the same database connection pool. If the Reviews service gets hammered by bots scraping data, it should not consume the resources needed to look up product availability. ... When a cache miss occurs, the first request goes to the database to fetch the data. The system identifies that 49,999 other people are asking for the same key. Instead of sending them to the database, it holds them in a wait state. Once the first request returns, the system populates the cache and serves all 50,000 users with that single result. This pattern is critical for “flash sale” scenarios in retail. When a million users refresh the page to see if a product is in stock, you cannot do a million database lookups. ... You cannot buy “resilience” from AWS or Azure. You cannot solve these problems just by switching to Kubernetes or adding more nodes.

Cloud-native observability enters a new phase as the market pivots from volume to value

“The secret in the industry is that … all of the existing solutions are motivated to get people to produce as much data as possible,” said Martin Mao, co-founder and chief executive officer of Chronosphere, during an interview with theCUBE. “What we’re doing differently with logs is that we actually provide the ability to see what data is useful, what data is useless and help you optimize … so you only keep and pay for the valuable data.” ... Widespread digital modernization is driving open-source adoption, which in turn demands more sophisticated observability tools, according to Nashawaty. “That urgency is why vendor innovations like Chronosphere’s Logs 2.0, which shift teams from hoarding raw telemetry to keeping only high-value signals, are resonating so strongly within the open-source community,” he said. ... Rather than treating logs as an add-on, Logs 2.0 integrates them directly into the same platform that handles metrics, traces and events. The architecture rests on three pillars. First, logs are ingested natively and correlated with other telemetry types in a shared backend and user interface. Second, usage analytics quantify which logs are actually referenced in dashboards, alerts and investigations. Third, governance recommendations guide teams toward sampling rules, log-to-metric conversion or archival strategies based on real usage patterns.

How recruitment fraud turned cloud IAM into a $2 billion attack surface

The attack chain is quickly becoming known as the identity and access management (IAM) pivot, and it represents a fundamental gap in how enterprises monitor identity-based attacks. CrowdStrike Intelligence research published on January 29 documents how adversary groups operationalized this attack chain at an industrial scale. Threat actors are cloaking the delivery of trojanized Python and npm packages through recruitment fraud, then pivoting from stolen developer credentials to full cloud IAM compromise. ... Adversaries are shifting entry vectors in real-time. Trojanized packages aren’t arriving through typosquatting as in the past — they’re hand-delivered via personal messaging channels and social platforms that corporate email gateways don’t touch. CrowdStrike documented adversaries tailoring employment-themed lures to specific industries and roles, and observed deployments of specialized malware at FinTech firms as recently as June 2025. ... AI gateways excel at validating authentication. They check whether the identity requesting access to a model endpoint or training pipeline holds the right token and has privileges for the timeframe defined by administrators and governance policies. They don’t check whether that identity is behaving consistently with its historical pattern or is randomly probing across infrastructure.

The Hidden Data Access Crisis Created by AI Agents

As enterprises adopt agents at scale, a different approach becomes necessary. Instead of having agents impersonate users, agents retain their own identity. When they need data, they request access on behalf of a user. Access decisions are made dynamically, at the moment of use, based on human entitlements, agent constraints, data governance rules, and intent (purpose). This shifts access from being identity-driven to being context-driven. Authorization becomes the primary mechanism for controlling data access, rather than a side effect of authentication. ... CDOs need to work closely with IAM, security, and platform operations teams to rethink how access decisions are made. In particular, this means separating authentication from authorization and recognizing that impersonation is no longer a sustainable model at scale. Authentication teams continue to establish trust and identity. Authorization mechanisms must take on the responsibility of deciding what data should be accessible at query time, based on the human user, the agent acting on their behalf, the data’s governance rules, and the purpose of the request. ... CDOs must treat data provisioning as an enterprise capability, not a collection of tactical exceptions. This requires working across organizational boundaries. Authentication teams continue to establish trust and identity. Security teams focus on risk and enforcement. Data teams bring policy and governance context.

Daily Tech Digest - October 22, 2025

Quote for the day:

"Good content isn't about good storytelling. It's about telling a true story well." -- Ann Handley

When yesterday’s code becomes today’s threat

A striking new supply chain attack is sending shockwaves through the developer community: a worm-style campaign dubbed “” has compromised at least 187 npm packages, including the that has 2 million hits weekly, and spreading to other maintainers' packages. The malicious payload modifies package manifests, injects malicious files, repackages, and republishes — thereby infecting downstream projects. This incident underscores a harsh reality: even code released weeks, months, or even years ago can become dangerous once a dependency in its chain has been compromised. ... Sign your code: All packages/releases should use cryptographic signing. This allows users to verify the origin and integrity of what they are installing. Verify signatures before use: When pulling in dependencies, CI/CD pipelines, and even local dev setups, include a step to check that the signature matches a trusted publisher and that the code wasn’t tampered with. SBOMs are your map of exposure: If you have a for your project(s), you can query it for compromised packages. Find which versions/packages have been modified — even retroactively — so you can patch, remove, or isolate them. Continuous monitoring of risk posture: It's not enough to secure when you ship. You need alerts when any dependency or component’s risk changes: new vulnerabilities, suspicious behavior, misuse of credentials, or signs that a trusted package may have been modified after release.

Cloud Sovereignty: Feature. Bug. Feature. Repeat!

isn’t just a buzzword anymore, argues Kushwaha. “It’s a real concern for businesses across the world. The pattern is clear. The cloud isn’t a one-size-fits-all solution anymore. Companies are starting to realise that sometimes control, cost, and compliance matter more than convenience.” ... Cloud sovereignty is increasingly critical due to the evolving geopolitical scenario, government and industry-specific regulations, and vendor lock-ins with heavy reliance on . The concept has gained momentum and will continue to do so because technology has become pervasive and critical for running a state/country and any misuse by foreign actors can cause major repercussions, the way Bavishi sees it. Prof. Bhatt captures that true digital sovereignty is a distant dream and achieving this requires a robust ecosystem for decades. This isn’t counterintuitive; it’s evolution, as Kushwaha epitomises. “The cloud’s original promise was one of freedom. Today, when it comes to the cloud, freedom means more control. Businesses investing heavily in digital futures can’t afford to ignore the fine print in hyperscaler contracts or the reach of foreign laws. Sovereignty is the foundation for building safely in a fragmented world.” ... Organisations have recognised the risks of digital dependencies and are looking for better options. There is no turning back, Karlitschek underlines.

Securing AI to Benefit from AI

As organizations begin to integrate AI into defensive workflows, identity security becomes the foundation for trust. Every model, script, or autonomous agent operating in a production environment now represents a new identity — one capable of accessing data, issuing commands, and influencing defensive outcomes. If those identities aren't properly governed, the tools meant to strengthen security can quietly become sources of risk. The emergence of make this especially important. These systems don't just analyze; they may act without human intervention. They triage alerts, enrich context, or trigger response playbooks under delegated authority from human operators. ... AI systems are capable of assisting human practitioners like an intern that never sleeps. However, it is critical for security teams to differentiate what to automate from what to augment. Some tasks benefit from full automation, especially those that are repeatable, measurable, and low-risk if an error occurs. ... , , and are prime candidates for automation. These are data-heavy, pattern-driven processes where consistency outperforms creativity. By contrast, , attribution, and response decisions rely on context that AI cannot fully grasp. Here, AI should assist by surfacing indicators, suggesting next steps, or summarizing findings while practitioners retain decision authority. Finding that balance requires maturity in process design.

The Unkillable Threat: How Attackers Turned Blockchain Into Bulletproof Malware Infrastructure

When EtherHiding emerged in September 2023 as part of the CLEARFAKE campaign, it introduced a chilling reality: attackers no longer need vulnerable servers or hackable domains. They’ve found something far better—a global, decentralized infrastructure that literally cannot be shut down. ... When victims visit the infected page, the loader queries a smart contract on or using a read-only function call. ... Forget everything you know about disrupting cybercrime infrastructure. There is no command-and-control server to raid. No hosting provider to subpoena. No DNS to poison. The malicious code exists simultaneously everywhere and nowhere, distributed across thousands of blockchain nodes worldwide. As long as Ethereum or BNB Smart Chain operates—and they’re not going anywhere—the malware persists. Traditional law enforcement tactics, honed over decades of fighting cybercrime, suddenly encounter an immovable object. You cannot arrest a blockchain. You cannot seize a smart contract. You cannot compel a decentralized network to comply. ... The read-only nature of payload retrieval is perhaps the most insidious feature. When the loader queries the smart contract, it uses functions that don’t create transactions or blockchain records.

New 'Markovian Thinking' technique unlocks a path to million-token AI reasoning

Researchers at have proposed a new technique that makes large language models (LLMs) vastly more efficient when performing complex reasoning. Called Markovian Thinking, the approach allows LLMs to engage in lengthy reasoning without incurring the prohibitive computational costs that currently limit such tasks. The team’s implementation, an environment named , structures the reasoning chain into fixed-size chunks, breaking the scaling problem that plagues very long LLM responses. Initial estimates show that for a 1.5B parameter model, this method can cut the costs of training by more than two-thirds compared to standard approaches. ... The researchers compared this to models trained with the standard . Their findings indicate that the model trained with Delethink could reason up to 24,000 tokens, and matched or surpassed a LongCoT model trained with the same 24,000-token budget on math benchmarks. On other tasks like coding and PhD-level questions, Delethink also matched or slightly beat its LongCoT counterpart. “Overall, these results indicate that Delethink uses its thinking tokens as effectively as LongCoT-RL with reduced compute,” the researchers write. The benefits become even more pronounced when scaling beyond the training budget.

The dazzling appeal of the neoclouds

While their purpose-built design gives them an advantage for AI workloads, neoclouds also bring complexities and trade-offs. Enterprises need to understand where these platforms excel and plan how to integrate them most effectively into broader cloud strategies. Let’s explore why this buzzword demands your attention and how to stay ahead in this new era of cloud computing. ... Neoclouds, unburdened by the need to support everything, are outpacing hyperscalers in areas like agility, pricing, and speed of deployment for AI workloads. A shortage of GPUs and data center capacity also benefits neocloud providers, which are smaller and nimbler, allowing them to scale quickly and meet growing demand more effectively. This agility has made them increasingly attractive to AI researchers, startups, and enterprises transitioning to AI-powered technologies. ... Neoclouds are transforming cloud computing by offering purpose-built, cost-effective infrastructure for AI workloads. Their price advantages will challenge traditional cloud providers’ market share, reshape the industry, and change enterprise perceptions, fueled by their expected rapid growth. As enterprises find themselves at the crossroads of innovation and infrastructure, they must carefully assess how neoclouds can fit into their broader architectural strategies.

Wi-Fi 8 is coming — and it’s going to make AI a lot faster

Unlike previous generations of Wi-Fi that competed on peak throughput numbers, Wi-Fi 8 prioritizes consistent performance under challenging conditions. The specification introduces coordinated multi-access point features, dynamic spectrum management, and hardware-accelerated telemetry designed for AI workloads at the network edge. ... A core part of the Wi-Fi 8 architecture is an approach known as (UHR). This architectural philosophy targets the 99th percentile user experience rather than best-case scenarios. The innovation addresses AI application requirements that demand symmetric bandwidth, consistent sub-5-millisecond latency and reliable uplink performance. ... Wi-Fi 8 introduces (ELR) mode specifically for IoT devices. This feature uses lower data rates with more robust coding to extend coverage. The tradeoff accepts reduced throughput for dramatically improved range. ELR operates by increasing symbol duration and using lower-order modulation. This improves the link budget for battery-powered sensors, smart home devices and outdoor IoT deployments. ... Wi-Fi 8 enhances roaming to maintain sub-millisecond handoff latency. The specification includes improved (FILS) and introduces coordinated roaming decisions across the infrastructure. Access points share client context information before handoff.

Life, death, and online identity: What happens to your online accounts after death?

Today, we lack the tools (protocols) and the regulations to enable digital estate management at scale. Law and regulation can force a change in behavior by large providers. However, lacking effective protocols to establish a mechanism to identify the decedent’s chosen individuals who will manage their digital estate, every service will have to design their own path. This creates an exceptional burden on individuals planning their digital estate, and on individuals who manage the digital estates of the deceased. ... When we set out to write this paper, we wanted to influence the large technology and social media platforms, politicians, regulators, estate planners, and others who can help change the status quo. Further, we hoped to influence standards development organizations, such as the and the Internet Engineering Task Force (IETF), and their members. As standards developers in the realm of identity, we have an obligation to the people we serve to consider identity from birth to death and beyond, to ensure every human receives the respect they deserve in life and in death. Additionally, we wrote the planning guide to help individuals plan for their own digital estate. By giving people the tools to help describe, document, and manage their digital estates proactively, we can raise more awareness and provide tools to help protect individuals at one of the most vulnerable moments of their lives.

5 steps to help CIOs land a board seat

Serving on a board isn’t an extension of an operational role. One issue CIOs face is not understanding the difference between executive management and governance, Stadolnik says. “They’re there to advise, not audit or lead the current company’s CIO,” he adds. In the boardroom, the mandate is to provide strategy, governance, and oversight, not execution. That shift, Stadolnik says, can be jarring for tech leaders who’ve spent their careers driving operational results. ... “There were some broad risk areas where having strong technical leadership was valuable, but it was hard for boards to carve out a full seat just for that, which is why having CIO-plus roles was very beneficial,” says Cullivan. The issue of access is another uphill battle for CIOs. As Payne found, the network effect can play a huge role in seeking a board role. But not every IT leader has the right kind of network that can open the door to these opportunities. ... Boards expect directors to bring scope across business disciplines and issues, not just depth in one functional area. Stadolnik encourages CIOs to utilize their strategic orientation, results focus, and collaborative and influence skills to set themselves up for additional responsibilities like procurement, supply chain, shared services, and others. “It’s those executive leadership capabilities that will unlock broader roles,” he says. Experience in those broader roles bolsters a CIO’s board résumé and credibility.

Microservices Without Meltdown: 7 Pragmatic Patterns That Stick

A good sniff test: can we describe the service’s job in one short sentence, and does a single team wake up if it misbehaves? If not, we’ve drawn mural art, not an interface. Start with a small handful of services you can name plainly—orders, payments, catalog—then pressure-test them with real flows. When a request spans three services just to answer a simple question, that’s a hint we’ve sliced too thin or coupled too often. ... Microservices live and die by their contracts. We like contracts that are explicit, versioned, and backwards-friendly. “Backwards-friendly” means old clients keep working for a while when we add fields or new behaviors. For HTTP APIs, plus consistent error formats makes a huge difference. ... We need timeouts and retries that fit our service behavior, or we’ll turn small hiccups into big outages. For east-west traffic, a or smart gateway helps us nudge traffic safely and set per-route policies. We’re fans of explicit settings instead of magical defaults. ... Each service owns its tables; cross-service read needs go through APIs or asynchronous replication. When a write spans multiple services, aim for a sequence of local commits with compensating actions instead of distributed locks. Yes, we’re describing sagas without the capes: do the smallest thing, record it durably, then trigger the next hop.

Daily Tech Digest - June 18, 2024

The Intersection of AI and Wi-Fi 7

Wi-Fi 7 is the newest standard in wireless networking. Though official ratification isn't expected until the end of 2024, Wi-Fi 7 client devices and wireless access points are already available. The top line speed of Wi-Fi 7 is often stated at 46 Gbps, but actual speeds will be lower. The higher speeds of Wi-Fi 7 are delivered by using a 320 MHz wide channel, increasing the transmission rate to 4K QAM and increasing the number of transmit and receive chains to 16. Another key advantage of Wi-Fi 7 is a significant reduction in packet latency, thanks to a feature called Multi-Link Operation (MLO). ... AI Autonomous Networks consolidate key performance indicators to aid decision-making. During the shift from 2.4 GHz and 5 GHz to 6 GHz networking, IT managers can use AI to expose timing and predict improvements, facilitating timely network upgrades. Another example is digital twin architecture, which simulates the network environment using real-world client analytics to model behavior, evaluate security changes, and assess configuration adjustments. The goal is to provide IT managers with tools for timely and accurate decisions.

Linux in your car: Red Hat’s milestone collaboration with exida

Red Hat’s collaboration with exida marks a significant milestone. While it may not be obvious to all of us, Linux is playing an increasingly important role in the automotive industry. In fact, even the car you’re driving today could be using Linux in some capacity. Linux is very well known and appreciated in the automotive industry with increasing attention being paid both to its reliability and its security. The phrase “open source for the open road” is now being used to describe the inevitable fit between the character of Linux and the need for highly customizable code in all sorts of automotive equipment. The safety of vehicles that get us from one place to another on a nearly daily basis has become a serious priority. ... Their focus on ensuring the safety of both individual components and the operating system as a whole is crucial. This latest achievement brings them even closer to realizing the first continuously-certified in-vehicle Linux Red Hat In-Vehicle Operating System. Their open source first approach to the organization, culture and thought process is an exemplary superset of what exida regards as a best practice for world-class safety culture.

How CIOs Can Integrate AI Among Employees Without Impacting DEI

As technology adoption accelerates, employees risk falling behind in adapting to meet enterprise demands. This trend has been evident across computing eras, from PCs to the current AI and Internet of Things era. Each phase widens the gap between technology introduction and employees’ ability to use it effectively. ... To prioritize DEI in addressing employee upskilling to leverage AI, CIOs can embrace a spectrum of initiatives, from establishing peer mentorship programs to providing access to online courses, workshops, and conferences. The aim is to promote educational opportunities for those most at risk of falling behind, which will increase the cost risk in the future due to the extra cost of retraining staff or seeking new talent. To successfully link digital dexterity to DEI to prepare employees, CIOs should implement a training program that equitably exposes all workforce segments to AI and the machine economy to develop soft and technical skills. Shift the focus of AI adoption away from solely business needs and focus on individual empowerment

What is a CAIO — and what should they know?

CAIOs and others tasked with overseeing AI deployments play an essential role in “shaping an organization’s strategic, informed and responsible use of AI,” he said. “There are many responsibilities baked into the role, but at its core, it’s about steering the direction of AI initiatives and innovation to align with company goals. AI leads must also create a culture of collaboration and continuous learning.” ... While CAIOs might not always be seated at the C-suite table, those who are there are keenly focused on genAI and its potential to drive efficiencies and profits. Without an executive guiding those deployments, achieving the performance and ROI organizations seek will be tough, she said. “It’s hard to imagine how pieces come together and how you’d bring together so many players,” Kosar said, noting that PwC has more than a dozen different LLMs running internally to power AI tools and products in virtually every business unit. “You have to have the ability to do short-term and long-term planning and balance the two and stay focused on innovation,” she continued. “At the same time, you need to recognize the pace of change while not getting distracted by the latest shiny object.”

How AI is impacting data governance

Every organization needs to establish policies around the handling of its data—informed by federal, state, industry, and international regulations as well as internal business rules. In larger enterprises, a data governance committee sets those policies and specifies how they should be followed in a living document that evolves as regulations and procedures change. The natural language capabilities of generative AI can pop out first drafts of that documentation and make subsequent changes much less onerous. By analyzing data usage patterns, regulatory requirements, and internal workflows, AI can help organizations define and enforce data retention policies and automatically identify data that has reached the end of its useful life. ... AI-powered disaster recovery systems can help organizations develop sound recovery strategies by predicting potential failure scenarios and establishing preventive measures to minimize downtime and data loss. Backup systems infused with AI can ensure the integrity of backups and, when disaster strikes, automatically initiate recovery procedures to restore lost or corrupted data.

The impact of compliance technology on small FinTech firms

However, smaller firms often struggle to adapt quickly due to resource constraints, leading to a more reactive compliance management approach. For smaller firms, running on thin resources could mean higher risks. Many operate with minimal compliance staff or assign compliance duties to employees who juggle multiple roles. This can stretch employees too thin, making it tough to keep up with regulatory changes or manage conflicts of interest that might jeopardize the firm. The use of basic tools like spreadsheets and emails increases the risk of missing important updates or failing to adequately address identified risks due to the lack of clear ownership and effective action plans. Furthermore, regulatory penalties can disproportionately impact smaller firms that lack the financial buffer to absorb significant fines. The ever-evolving regulatory landscape poses an ongoing risk to compliance. Smaller firms must navigate a vast array of compliance policies and procedures. Even those with dedicated compliance or legal experts face the challenge of sifting through extensive documentation to identify relevant changes.

Revolutionising firms’ security with SASE

For Indian companies, today is an opportune time to have a well-thought long-term SASE strategy and identify short-term consolidation tactics to achieve your desired SASE model. There may be a change required in the firm’s IT culture to adopt integrated networking and security teams, which involves a shift from silo ways of working to shared control. Because no two SASE journeys are the same, therefore, it is up to enterprises to prepare differently and plan for different or customized outcomes. And the first step to doing so is selecting a trusted partner to help in the assessment of your network and security roadmaps against SASE as the reference architecture. Just as significant as the delivery and operational components of SASE, is having a partner who understands innovation and agility, with an eye towards the future. The partner should be able to assist in technology evaluation, establish proof of value, and recommend adaptations to integrate SASE components – all of which go toward laying the foundation for the firm’s security and network roadmaps. Firms should know that when it comes to executing SASE, it isn’t just done and dusted but a multi-disciplinary project with moving parts.

The Next Phase of the Fintech Revolution: Inside the Disruption and the Challenges Facing Banking

The thing that’s causing the most waves right now, frankly, is the regulators. We had evolved to this architecture where you had fintechs doing their thing. You had sponsor banks of various types underneath who were actually bearing the regulatory burden and holding the cash — things that only banks can really do. And then you had these middleware companies that are generically kind of known as banking as a service companies (BaaS). That architecture, which underpins much of the payments, lending and banking innovation that we’ve seen, has now been called into question by regulators and is being litigated ... The most important theme right now is the implications of generative AI for financial services and, not least of all, retail banking. What’s being funded right now are basically vendors. So, this new crop of technology companies is springing up to serve banks and financial institutions more generally and help them with digital transformation as it relates to generative AI. So, you could think of chatbot companies as being probably the most advanced wedge on this and customer service generally as a way to introduce generative AI, lower OpEx and create more customer delight.

Data Governance and AI Governance: Where Do They Intersect?

AI governance needs to cover the contents of the data fed to and retrieved through AI, in addition to considering the level of AI intelligence. Doing so addresses issues like biases, privacy, use of intellectual property, and misuse of the technology. Consequently, AIG needs to guide what subject matter can be processed through AI, when, and in what contexts. ... AIG and DG share common responsibilities in guiding data as a product that AI systems create and consume, despite their differences. Both governance programs evaluate data integration, quality, security, privacy, and accessibility. ... The data governance team audits the product data pipeline and finds inconsistent data standards and missing attributes feeding into the AI model. However, the AI governance team also identifies opportunities to enhance the recommendation algorithm’s logic for weighting customer preferences. The retailer could resolve the data quality issues through DG while AIG improved the AI model’s mechanics by taking a collaborative approach with both data governance and AI governance perspectives.

Enhancing security through collaboration with the open-source community

Without funding, it is difficult for open-source projects to get official certifications. So, companies in regulated sectors that need those certifications often can’t use open-source solutions. For the rest, open-source really has “eaten the world.” Most modern tech companies wouldn’t exist without open-source tools, or would have drastically different offerings. ... Too many just download the open-source project and run away. One way for corporate entities to get involved is by contributing bug fixes and small features. This can be done through anonymous email accounts if it’s necessary to keep the company’s involvement private. Companies should also use the results of their security analysis to help improve the original project. There is some self-interest involved here. Why should a company use its resources to maintain proprietary patches for an open-source project when it can instead send those patches back and have the community maintain them for free? Google has been doing a good job of this with their OSS-FUZZ project. It has found many bugs and helped a large number of the open-source projects using it.

Quote for the day:

"Develop success from failures. Discouragement and failure are two of the surest stepping stones to success." -- Dale Carnegie

Daily Tech Digest - April 25, 2024

The rise in CISO job dissatisfaction – what’s wrong and how can it be fixed?

“The reason for dissatisfaction is the lack of executive management support,” says Nikolay Chernavsky, CISO of ISSQUARED, which provides managed IT and security services as well as software products. He says he hears CISOs voice frustrations when their views on required security measures and acceptable risk are dismissed; when the board and CEO don’t define their positions on those issues; or when those leaders don’t recognize the CISOs work in reducing risk — especially as the CISO faces more accountability and liability. Understandably, CISOs shy away from interview requests to publicly share their frustrations on these issues. However, the IANS Research report speaks to these points, noting, for example, that only 36% of CISOs said they have clear guidance from their board on their risk tolerance. Adding to these issues today is the liability that CISOs now face with the new US Securities and Exchange Commission (SEC) cyber disclosure rules as well as other regulatory and legal requirements. That increased liability is coupled with the fact that many CISOs are not covered by their organization’s directors and officers (D&O) liability insurance.

How CIOs align with CFOs to build RevOps

CIOs who transition IT from being a cost center to being a driver of innovation, transformation, and new revenues, can become the leaders that the new economy needs. “We used to say that business runs technology,” says David Kadio-Morokro, EY Americas financial services innovation leader. “You tell me what you want, and I’ll code it and support you.” Now it’s switched, he says. “I really believe technology drives the business, because it’s going to impact business strategy and how the business survives,” he adds, and gen AI will force companies to rethink the value of their organizations to customers. “Developing and envisioning an AI-driven strategy is absolutely part of the equation,” he says. “And the CIO has this role of enabling these components, and they need to be part of the conversation and be able to drive that vision for the organization.” The CIO is also in a position to help the CFO evolve, too. CFOs are traditionally risk averse and expect certainty and accuracy from their technology. Not only is gen AI still a new and experimental technology that’s evolving quickly but is, by its very nature, probabilistic and nondeterministic.

Do you need to repatriate from the cloud?

It should be no surprise that repatriation has gained this hype. Cloud, which grew to maturity during an economic boom, is for the first time under downward pressure as companies seek to reduce spending. Amazon, Google, Microsoft, and other cloud providers have feasted on their customers’ willingness to spend. But the willingness has been tempered now by budget cuts. ... Transitioning back to on-premises is a heavy lift, and one that is hard to rescind should things go badly. And savings is yet to be seen until after the transition is complete. Switching to WebAssembly-powered serverless functions, in contrast, is less expensive and less risky. Because such functions can run inside of Kubernetes, the savings thesis can be tested merely by carving off a few representative services, rewriting them, and analyzing the results. Those already invested in a microservice-style architecture are already well setup to rebuild just fragments of a multi-service application. Similarly, those invested in event processing chains like data transformation pipelines will also find it easy to identify a step or two in a sequence that can become the testbed for experimentation.

ONDC’s blockchain is a Made-in-India visioning of global digital public infrastructures

ONDC Confidex is a transformative shift towards decentralised trust. Anchored in the blockchain’s nativity, this shift promotes a value exchange network of networks that enables the reuse of continuously assured data that is traceable, reliable, secure, transparent and immutable. Confidex provides a transparent ledger that tracks every phase in the supply chain from production to end consumption. This level of detail not only fosters trust but also aligns with the broader vision of creating a global standard for ensuring product history’s authenticity—a core aspect of continuous data assurance. In the realm of digital transactions, the reliability of data underpins the foundation of trust. Confidex enables data certainty, making each transaction verifiable and immutable. This paves the way for friction-free interactions within digital marketplaces, ensuring that every piece of data holds its integrity from the point of creation to consumption. The digital economy is plagued with issues of forgery and duplication. Confidex addresses this head-on by creating unique digital records that are impossible to replicate or alter.

How will AI-driven solutions affect the business landscape?

Redmond believes that the tech will quickly become embedded in normal business practice. “We won’t even think about asking gen AI to draft emails or documents or to generate images for our presentations.” He’s also looking forward to seeing how AI-driven video technology plays out, particularly OpenAI’s Sora. “I know that a lot of people in content generation are nervous about these tools replacing them, but I don’t think we hire an artist for their ability to draw, we hire them for their ability to draw what is in their imagination, and that is where their genius lies,” he says. “I am not sure that artists will ever stop creating wonderful works, and these technologies will just enhance that.” Tiscovschi agrees with Redmond’s outlook, stating that “this is just the beginning”. “We will continuously see more teams of humans and their AI agents or tools working together to achieve tasks,” he says. “A human quickly mining their organisation’s IP, automating repetitive tasks and then collaborating with their AI copilot on a report or piece of code will have a constantly growing multiplier on their productivity.”

5 Strategies for Better Results from an AI Code Assistant

The first step is to provide the GPT with high-level context. In her scenario, Phil demonstrates by building a Markdown editor. Since Copilot has no idea of the context, he has to provide it and he does this with a large prompt comment with step-by-step instructions. For instance, he tells the copilot, “Make sure we have support for bold, italics and bullet points” and “Can you use reactions in the React markdown package.” The prompt enables Copilot to create a functional but unsettled markdown editor. ... Follow up by providing the Copilot with specific details, Scarlett advised. “If he writes a column that says get data from [an] API, then GitHub Copilot may or may not know what he’s really trying to do, and it may not get the best result. It doesn’t know which API he wants to get the data from or what it should return,” Scarlett said. “Instead, you can write a more specific comment that says use the JSON placeholder API, pass in user IDs, and return the users as a JSON object. That way we can get more optimal results.”

ESG research unveils critical gaps in responsible AI practices across industries

In light of the ESG Research findings, Qlik recognises the imperative of aligning AI technologies with responsible AI principles. The company’s initiatives in this area are grounded in providing robust data management and analytics capabilities, essential for any organisation aiming to navigate the complexities of AI responsibly. Qlik underscores the importance of a solid data foundation, which is critical for ensuring transparency, accountability, and fairness in AI applications. Qlik’s commitment to responsible AI extends to its approach to innovation, where ethical considerations are integrated into the development and deployment of its solutions. By focusing on creating intuitive tools that enhance data literacy and governance, Qlik aims to address key challenges identified in the report, such as ensuring AI explainability and managing regulatory compliance effectively. Brendan Grady, General Manager, Analytics Business Unit at Qlik, said, “The ESG Research echoes our stance that the essence of AI adoption lies beyond technology—it’s about ensuring a solid data foundation for decision-making and innovation.

Applying DevSecOps principles to machine learning workloads

Unlike in a conventional software development environment with an integrated development environment (IDE), data scientists typically write code using Jupyter Notebooks. This takes place outside of an IDE, and often outside of the traditional DevSecOps lifecycle. As a result, it’s possible for a data scientist who is not trained on secure development techniques to put sensitive data at risk, by storing unprotected secrets or other sensitive information in a notebook. Simply put, the same tools and protections used in the DevSecOps world aren’t effective for ML workloads. The complexity of the environment also matters. Conventional development cycles usually lead directly to a software application interface or API. In the machine learning space, the focus is iterative, building a trainable model that leads to better outcomes. ML environments produce large quantities of serialized files necessary for a dynamic environment. The upshot? Organizations can become overwhelmed by the inherent complexities of versioning and integration.

Introducing Wi-Fi 7 access points that deliver more

This idea that the access point (AP) can do more than just route traffic is a core part of our product philosophy, and we’ve consistently expanded on that over multiple Wi-Fi generations with the addition of location services, IoT protocol support, and extensive network telemetry for security and AIOps. As organizations continue to innovate, and leverage applications that require more bandwidth or more IoT devices to support new digital use cases, the AP must continue to do more. Delivering solutions that go beyond standards is part of HPE Aruba Networking’s history and future. Now, with the introduction of 700 series access points that support Wi-Fi 7, we are doubling IoT capabilities with dual BLE 5.4 or 802.15.4/Zigbee radios and dual USB interfaces and improving location precision for use cases such as asset tracking and real-time inventory tracking. Moreover, we are using both the resources and the management of the AP to its full potential by delivering ubiquitous high-performance connectivity and processing at the edge. What this means is that these access points not only have optimal support for the 2.4, 5, and 6 GHz spectrum but also enough memory and compute capacity to run containers.

Why Your Enterprise Should Create an Internal Talent Marketplace

Strategically, an internal talent marketplace is a way to empower employees to be in the driver’s seat of their career journey, says Gretchen Alarcon, senior vice president and general manager of employee workflows at software and cloud platform provider ServiceNow, via email. "Tactically, it's a platform driven by technology that uses AI to match existing talent to open roles or projects within the organization," she explains. "It provides a more transparent view of new opportunities for employees and identifies untapped employee potential based on skills rather than anecdotes." ... A talent marketplace is only as good as the information it contains, Williamson warns. "Organizations should emphasize to employees that it's in their interest to keep the skills and preferences in their profiles up to date," he says. Managers. meanwhile, need to define the exact critical skills needed to be successful in a particular job or role. "That information drives recommended opportunities for employees and increases their chances of being identified by project managers to fill roles."

Quote for the day:

"Rarely have I seen a situation where doing less than the other guy is a good strategy." -- Jimmy Spithill

Daily Tech Digest - February 15, 2024

CISO and CIO Convergence: Ready or Not, Here It Comes

While CIOs are still responsible for setting and meeting technology goals and for staying on budget, their primary mandate is determining how the company can harness technology to innovate, and then procure and manage those resources. While plenty of companies still maintain large, on-premise IT estate, it's just a matter of time before they digitally transform. Either way, the CIO role has become markedly less operational over time. On the other hand, the profile of CISOs has been growing since the early 2000s, set against a non-stop carousel of compliance mandates, data breaches, and emerging cybersecurity threats. While data breaches may have forced businesses to pay attention to security, it was compliance mandates that funded it. From HIPAA and PCI DSS to GDPR, SOC 2, and more, compliance has been a double-edged sword for CISOs. Compliance increased the role of cybersecurity teams and made them more visible across IT and the business as a whole, providing CISOs with bigger budgets and increased latitude on how to spend it. However, all the effort they put into compliance did little to stymie phishing, ransomware, big breaches, and/or malicious insiders.

Will Generative AI Kill DevSecOps?

Beyond having automation and guardrails in place, you also need security policies at the company level, Moisset said, to make sure that DevSecOps understands all the generative AI tools colleagues are using. Then you can educate them on how to use it, like creating and communicating a generative AI policy. Because a total ban on GenAI just won’t fly. When Italy temporarily banned ChatGPT, Foxwell said there was a visible decrease in productivity across the country’s GitHub organizations, but, when it was reinstated, “what also picked up was the usage of tools that circumvented all of the government policies and firewalls around the prevention of using these” tools. Engineers always find a way. Particularly when using generative AI for customer service chatbots, Moisset said, you need guardrails in place around both the inputs and outputs, as malicious actors can potentially “socialize” the chatbot via prompt injection to give a desired result — like when someone was able to buy a Chevy for $1 from a chatbot. “It’s back to educating the users and developers that it’s good to use AI, we should be using AI, but we need to actually put guardrails around it,” she said, which also demands an understanding of how your customers interact with GenAI.

Combining heat and compute

Data centers offer a predictable supply of heat because they keep their servers running continuously. But the heat is “low-grade:” It is warm rather than hot, and it comes in the form of air, which is difficult to transport. So, most data centers vent their heat to the atmosphere. Sometimes, there are district heat networks, which provide warmth to local homes and businesses through a piped network. If your data center is near one of these, it is a matter of extending it to connect to the data center, and boosting the grade of heat. But you have to be in the right place to connect to one. “There are certain countries that have established or developing heat networks, but the majority don't have a heat network per se, so it's going on a piecemeal basis,” Neal Kalita, senior director of power and energy at NTT, tells DCD. You are unlikely to find one in the US, says Rolf Brink of cooling consultancy Promersion: “The United States is a fundamentally different ecosystem. But Europe is a lot more dense in terms of population, and there is more heat demand.” The Nordic countries have a lot of heat networks. Stockholm Data Parks is a well-known example - a data center campus in urban Stockholm, where every data center has a connection to the district heating network and gets paid for its heat.

Harmonizing human potential and AI: The evolution of work in the digital era

The evolving landscape of work is witnessing a profound transformation as the fusion of human potential with AI takes center stage. Concerns about the ethical implications of AI are well-known, including the potential for perpetuating bias and discrimination and its impact on employment and job security. Ensuring that AI is developed and deployed ethically and responsibly is crucial, taking into account fairness, transparency and accountability. ... Optimizing human-centric capabilities with automation and an AI-first mindset is significant for long-term success. Consider a telecoms operator with employees struggling to grapple with the labor-intensive process of manually reviewing a high volume of mobile tower lease contracts. By embracing an AI-powered platform equipped with capabilities for faster and more accurate extraction of contract clauses, employees were able to shift their focus toward leveraging hidden risks identified by the platform. This enabled the renegotiation of existing contracts, leading to millions of dollars in savings. It’s no coincidence that the enterprises that are more inclined to augment human potential are those resilient enough to maximize the value of AI-led transformations.

5 Wi-Fi vulnerabilities you need to know about

Like wired networks, Wi-Fi is susceptible to Denial of Service (DoS) attacks, which can overwhelm a Wi-Fi network with excessive amount of traffic. This can cause the Wi-Fi to become slow or unavailable, disrupting normal operations of the network, or even the business. A DoS attack can be launched by generating a large number of connection or authentication requests, or injecting the network with other bogus data to break the Wi-Fi. ... Wi-jacking occurs when a Wi-Fi-connected device has been accessed or taken over by an attacker. The attacker could retrieve saved Wi-Fi passwords or network authentication credentials on the computer or device. Then they could also install malware, spyware, or other software on the device. They could also manipulate the device’s settings, including the Wi-Fi configuration, to make the device connect to rogue APs. ... RF interference can cause Wi-Fi disruptions. Instead of being caused by bad actors, RF interference could be triggered by poor network design, building changes, or other electronics emitting or leaking into the RF space. Interference can result in degraded performance, reduced throughput, and increased latency.

AI outsourcing: A strategic guide to managing third-party risks

Bias may persist in many face detection systems. Naturally, this misidentification could have severe consequences for the parties involved. Diverse training data and transparent algorithms are necessary to mitigate the risk of discriminatory outcomes. Furthermore, complex AI models often encounter the “black box” problem or how some AI models arrive at their decisions. Teaming with a third-party AI service requires human oversight to navigate the threat of biased algorithms. ... Most of us can admit that the risk of becoming overly reliant on AI is significant. AI can quickly become a go-to solution for many challenges. It’s no surprise that companies face a similar risk, becoming too dependent on a single vendor’s AI solutions. However, this approach can become problematic. Companies can “get stuck,” and switching providers seems almost impossible. ... Quality and reliability concerns are top-of-mind for most company leaders partnering with third-party AI services. Some primary concerns include service outages, performance issues, and unexpected disruptions. Operational resilience is necessary, and contingency plans are a significant piece of the resiliency puzzle, given the damage business downtime can cause.

Practices for Implementing an Effective Data Governance Strategy

Ensuring the integrity and usability of data within an organization requires implementing clear data quality standards and metrics. These standards serve as a benchmark for data quality, guiding data management practices and ensuring that data is accurate, complete, and reliable. Organizations can streamline their data governance processes by defining what constitutes quality data, making it easier to identify and rectify data issues. This approach enhances data quality, supports compliance with regulatory requirements, and improves decision-making capabilities. Developing a comprehensive set of data quality metrics is crucial for monitoring and maintaining high data standards. These metrics should be aligned with the organization’s strategic objectives and include criteria such as accuracy, completeness, consistency, timeliness, and uniqueness. ... Creating an environment where data stewardship and accountability are at the forefront requires strategic planning and commitment from all levels of an organization. It is essential to embed data governance principles into the corporate culture, ensuring that every team member understands their role in maintaining data integrity and security.

What is the impact of AI on storage and compliance?

Right now, when you look at traditional storage, generally speaking you look at your environment, your ecosystem, your data, classifying that data, and putting a value on it. And, depending on that value and the potential impact, you put in the right security and assign the length of time you need to keep the data and how you keep it, delete it. But, if you look at a CRM [customer relationship management service], if you put the wrong data in then the wrong data comes out, and it’s one set of data. So, to be blunt, garbage in, garbage out. With AI, it’s much more complex than that, so you may have garbage in, but instead of one dataset out that might be garbage, there might be a lot of different datasets and they may or may not be accurate. If you look at ChatGPT, it’s a little bit like a narcissist. It’s never wrong and if you give it some information and then it spits out the wrong information and then you say, “No, that’s not accurate”, it will tell you that’s because you didn’t give it the right dataset. And then at some stage it will stop talking to you, because it will have used up all its capability to argue with you, so to speak. From a compliance perspective, if you are using AI – a complicated AI or a simple AI like ChatGPT – to create a marketing document, that’s OK.

How to Get Your Failing Data Governance Initiatives Back on Track

Data governance is a big lift. Organizations might make the mistake of attempting to roll the initiative out across the entire enterprise without building in the steps to get there. “If you make it too broad and end up not focusing on short-term goals that you can demonstrate to keep the funding going, these engagements [tend] to fail,” says Prasad. Organizational issues are some of the major stumbling blocks standing in the way of successful data governance, but there can also be technical obstacles. Reiter points to the importance of leveraging automation. If an enterprise team attempts to manually undertake data governance mapping, it could be irrelevant by the time it is completed. ... Documentation, or lack thereof, can be a good indicator of a data governance initiatives' progress and sustainability. “As things are changing over time and documentation isn’t updated, that's a great sign that governance is not maintainable,” Holiat says. Getting feedback from end users can alert data governance leaders to issues standing in the way of adoption. Are people throughout the organization frustrated with the data governance program? Does it facilitate their access to data, or is it making their jobs more difficult?

Adopting AI with Eyes Wide Open

For businesses in general, AI can increase efficiency, make the workplace safer, improve customer service, create competitive advantage and lead to new business models and revenue streams. But like any technological innovation, AI has its risks and challenges. At the heart of AI is code and data; code can (and often does) contain bugs, and data can (and often does) contain anomalies. But that is no different to the technological innovations that we have embraced to-date. Arguably, the risks and challenges of AI are greater – not least of all because of the potential breadth of its application – and they include (but are certainly not limited to): overreliance, lack of transparency, ethical concerns, security, and regulatory and statutory challenges which typically lag behind the pace of progress. So, what does have this to do with strategy and architecture, and in particular digital transformation? Too often in organizations, new technologies are rushed in, in the belief that there is no time to lose. Before you know it, the funds and resources have been found to embark on an initiative (programme or project) to adopt it, spearheading the way to the future. It is the future!

Quote for the day:

"I find that the harder I work, the more luck I seem to have." -- Thomas Jefferson