CertifyOS

certifyos

Say it with me: data engineers are not a data catalog. You would be hard-pressed to find “answering multiple Slack messages every week about which tables are good to use for this report,” in their job description, but it happens nonetheless. Data analysts aren’t psychic. Yet, they are often placed in the position of having to intuit if the data being piped is trustworthy. This misalignment has arisen as data teams are pushed to move faster, weave themselves across the , and enable increasingly self-service data platforms. data mesh It’s the data team’s equivalent of the classic document version control issues that have plagued knowledge workers for decades. What starts as a tight pitch deck evolves into: A million people making and sharing ad-hoc slides; Massaging content on those slides until it becomes an echo of its original intent; and Creating copies labeled V6_Final_RealFinal. The same thing happens across the data team. Everyone is trying to do the right thing (i.e., support your stakeholders, generate insights, pipe more data, etc.), but everyone is also moving fast. One day you look up and notice you have 6 different models with slight variations essentially doing the same thing…and no one knows which one is most up-to-date or even which field to use. This creates real operational problems downstream including: Inefficient cycles of redundant “traffic control;” Lower data quality; Time spent resolving problems created by analysts using improper/problematic data; Lower data trust across the organization; and Increased data downtime When you don’t trust your data or you have lower data reliability, organizations often pad the margins of error in their forecasts. As highlighted by Peleton’s , poor forecasting can be especially problematic during the pandemic when uncertainty across demand, supply chains, and the overall business environment is at an all-time high. recent production halt More data discovery, more problems Data discovery is a new approach to understanding the health of your distributed data assets in real-time, and it’s an essential part of the solution. Data discovery provides a domain-specific, dynamic understanding of your data based on how it’s being ingested, stored, aggregated, and used by a set of specific consumers. As with a data catalog, governance standards and tooling are federated across these domains (allowing for greater accessibility and interoperability), but unlike a data catalog, data discovery surfaces a real-time understanding of the data’s current state as opposed to its ideal or “cataloged” state. It is especially useful when teams take a distributed approach to that holds different data owners accountable for their data as products, which allows data-savvy users throughout the business to self-serve from those products. governance But as data becomes more accessible, how can downstream stakeholders determine what data sets have been served, transformed, and approved by a given domain’s data team? How can one domain be sure a common set of are being upheld across the organization? data quality standards, ownership, and communication processes One of my customers, a leading media company with a mature data organization, was facing these exact questions. As a result, we have been working with them and several others to implement a data certification program. What is data certification? is the process by which data assets are approved for use across the organization after having met mutually agreed-upon , or service-level agreements,  for data quality, observability, ownership/accountability, issue resolution, and communication. Data certification SLAs Similar to the concepts of data quality, data validation, or data verification, data certification layers on critical processes that align people, frameworks, and technology to central business policies. Data certification requirements vary based on the needs of the business, the capacity of the data engineering team, and the availability of data, but typically incorporate the following features: Data certification programs increase scalability by leveraging a consistent approach applied across multiple domains. They also increase efficiency by facilitating more trustworthy exchanges of information between domains with clear lines of communication. Here’s how it works. Step 1: Build out your data observability capabilities Implementing data observability–an organization’s ability to fully understand the health of the data in their system–is an important first step in the data certification process. Not only do you need insight into your current performance to set a baseline, but you also need a systemic end-to-end approach for proactive incident discovery, alerting, and triaging. If within the pipeline breaks–and it will break–you will be the first to know. This head start, along with a detailed understanding of the data ecosystem, will reduce time to detection and resolution by pinpointing where errors occur. anything Knowing what systems and data sets have a tendency to create the largest or most frequent problems downstream also helps inform the process of writing effective data SLAs (Step 4). Additionally, understanding the upstream dependencies of your most important tables or reports helps data teams understand what data to give the most attention. The bottom line is that a table or data set should be closely monitored for anomalies  (ideally continuously learning and evolving via machine learning) to be considered certified. Step 2: Determine your data owners Each certified data asset should have a responsible party across its lifecycle from the ingestion to the analytics layer. Some data teams may choose to implement a , others may build it directly into the specific SLA along with the expected communication procedures and resolution times. RACI (responsible, accountable, consulted, informed) matrix Step 3. Understand what “good” data looks like By asking your business stakeholders the “who, what, when, where, and why,” you can understand what data quality means to them and which data is actually the most important. This will enable you to develop key performance indicators such as: : Freshness Data will be refreshed by 7:00 am daily (great for cases where the CEO or other key executives are checking their dashboards at 7:30 am). Data will never be older than X hours. : Distribution Column X will never be null. Column Y will always be unique. Field X will always be equal to or greater than field Y. : Volume Table X will never decrease in size. : Schema No fields will be deleted from this table. : Lineage 100% of the data populating table X will have upstream sources and downstream ingestors mapped and include relevant metadata. (or availability) Data downtime : The number of incidents is multiplied by (the time to detection + time to resolution). An example of a data downtime SLA could be, table X will have less than Y hours of downtime a year. SLAs that measure each of the components of data downtime can be more actionable. Examples include: we will reduce our incidents X%, time to detection X%, and time to resolution X%. Query Speed: Our friends at : “Average query run time is a good place to start, but you may need to create a more nuanced metric (e.g., X% of queries finish in <Y seconds). Locally Optimistic suggest Ingestion (great for keeping external partners accountable): Data will be received by 5 am each morning from partner Y. This process also enables you to configure granular alerting rules tailored to what matters most to the business. Step 4: Set clear SLAs for your most important data sets Setting SLAs (service level agreements) for your data pipeline is a major step toward increasing your data reliability and is essential to a data certification program. SLAs need to be specific, measurable, and achievable. Not only do SLAs describe an agreed-upon standard of service, but they also define the relationship between parties. In other words, they outline who is responsible for what during normal operations as well as when issues occur. , a Senior Data Scientist with Red Ventures, suggests that an effective SLA is realistic. Simply saying “having reliable data at all times” is too vague to be useful; instead, Brandon suggests, that teams should set SLAs that are focused. Brandon Beidel “Good SLAs are specific and detailed. They will describe why it’s important to the business, what the expectations are when those expectations need to be met, how they will be met, where the data lives, and who is impacted by it.” Beidel includes within his SLAs how the team should respond if the SLA isn’t met. For example, “the data in table X will be refreshed every day by 7:00 am” will transform into, “Team Z will ensure the data in table X will be refreshed every day by 7:00 am. Within 2 hours of an anomaly alert, the team will verify, communicate to affected parties, and begin a root cause analysis of the issue. Within one business day, a ticket will be created and the wider team will be updated on the progress made toward resolution.” To achieve this level of specificity and organization, teams should align early – and often – with stakeholders to understand what good data looks like. That includes within the data team as well as the business. A good SLA needs to be informed by the realities of how the business operates and how your users consume the data. I take a slightly different approach and differentiate between what I consider the SLA of “table x will be updated by 7 am” and the SLO (Service Level Objective) of “we will aim to meet this SLA 99% of the time.” However you decide to approach it, I’d recommend against boiling the ocean. Most of my customers are implementing their data certification programs as “go forward” first and cleaning up older assets in a second wave. In fact, many of the best data teams will start certifying the most critical tables and data sets: the ones that add the most value to the business, have the most query activity, the number of users, or dependencies. Some are also implementing tiers of certification–bronze, silver, gold–that convey different levels of service and support. Step 5: Develop your communication and incident management processes Where and how will alerts be sent to the team? How will the next steps and progress be communicated internally and externally? While this may seem like table stakes, clear and transparent communication is essential to creating a culture of accountability. Many teams opt to have alerts and incident triage discussions take place in Slack, PagerDuty or Microsoft Teams. This enables rapid coordination while giving full transparency to the wider team as part of a health incident management workflow. It’s also important to consider how to communicate major outages to the rest of the organization. For example, if an alert turns out to be a huge production outage, how does the on-call engineer inform the rest of the company? Where do they make that announcement and how frequently do they provide updates? Step 6: Determine a mechanism to tag the data as certified At this point, you have created SLAs with measurable objectives, transparent ownership, clear communication processes, and strong issue resolution expectations. You have the tools and proactive measures in place to empower your teams to be successful. The final step is to certify and surface the approved data assets for your stakeholders. I recommend decentralizing the certification process. After all, the certification process is designed to help make teams faster and more scalable. Having centralized regulations, enacted at the domain level will achieve these goals and avoid creating too much red tape. For the certification process, data teams will tag, search and leverage their tables appropriately either using data discovery solutions, a home-grown tool, or some other form of data catalog. Step 7: Train your data team and downstream consumers Of course, just because tables are tagged as certified doesn’t guarantee analysts will stay inbound. The team will need to be trained in the proper procedures, which will need to be enforced as necessary. Fine-tuning the level of alerts and communication is important as well. Occasionally receiving alerts that don’t require action is healthy. For example, you may have a table that grows significantly in size, but it was expected because the team added a new data source. Nothing is broken and in need of fixing, but it’s still helpful for the team to know. After all, “expected” behavior to one person might still be newsworthy and critical to another member of the team – or even another domain. However, alert fatigue is real. If the team is starting to ignore alerts, it can be a sign to optimize your approach by either adjusting your monitors or bi-furcating communication channels to better surface the most important information. When it comes to your data consumers, don’t be shy! You have put in an incredibly robust system for data quality aligned to their needs. Help them move from a subjective to objective understanding of how your team is performing and start giving them the vocabulary to be part of the solution. It’s all about proactive communication Data certification can be a beautiful process to see in action. The data engineer tags the table as certified along with the owner of the data set and surfaces it within the data warehouse for an analyst to grab it and use in their dashboard. And voila! No more (or at least, a whole lot less) data downtime. At its core, this process underscores that without the proper processes and culture in place, certifying reliability and building organizational trust in your data is extremely difficult. Technology will never be a replacement for good data hygiene, but it certainly helps. Also published here.

Certify Your Data Assets to Avoid Treating Your Data Engineers Like Catalogs

“Burn the Witch! Burn the Witch!” “I have a spell that can make it easy to differentiate AI-generated content from real content.” Like me, you probably got hooked on this ‘burn the witch’ trend before you even realized those videos were AI-generated. Honestly, no one can fault you for that because the things we can do with AI are becoming increasingly limitless. With tools like ChatGPT, DALL·E, Midjourney, Deepfakes, and more that can produce high-quality text, images, and videos on a massive scale, we face a greater risk of misinformation than ever before. Traditional methods like fact-checking have become too slow to keep us in the loop with the right information. So that begs the question: How do we maintain trust in a digital content era where anything can be faked instantly? Keep reading, and you’ll learn the spell. The AI Content Crisis—Why Trust Is Under Attack Imagine watching a video of your favorite politician only to find out it was entirely fabricated by AI. How can we trust anything we see or read online anymore? AI-generated content has never been more prevalent than it is now. For a generation whose trust issues run deep, the thought that any content you read—no matter how human it sounds—could have been generated using a language model like ChatGPT is unsettling. And to top it off, for every human-created video you scroll through, there are three more generated by AI tools like Deepfake. The worst part? You might not even realize that what you saw was AI-generated because how do you explain the voices and faces in the videos? This puts us at risk of not just misinformation but also scams, propaganda, and political manipulation. For instance, there are numerous videos online of politicians saying things they never said. Fake news articles have been used to spread false narratives during elections, and synthetic reviews artificially inflate product reputations. If you weren’t worried already, now would be a good time to start second-guessing the information you’ve consumed in the last few weeks. I know I have had to pause several times and try to confirm if what I was seeing was real—and now I’m less encouraged to turn to the internet in search of valuable information. Unfortunately, this is a looming epidemic. Having to second-guess everything you come across on the internet is not a way to live, especially when it is already such an integral part of our lives. So, what can we do? Enter blockchain—a technology that promises to restore trust by creating a permanent, tamper-proof record of digital content. Blockchain as a Digital Truth Ledger Immutability and Transparency Blockchain provides a solution and an anchor for truth thanks to its immutability, decentralization, and transparency. Data recorded on a blockchain cannot be altered or deleted—it remains permanent. Also, the fact that no single party controls the data reduces censorship and tampering risks. In addition, these records can be publicly verified, enabling anyone to confirm authenticity. Provenance Tracking and Verification These properties work together to ensure that each piece of digital content can be time-stamped and linked to its original creator or publisher on the blockchain; consumers or platforms can verify if content matches the original record, flagging modifications or fakes. Decentralized storage systems like IPFS (Interplanetary File System) store actual content off-chain but link cryptographic hashes to the blockchain for verification. Think of it as having a tamper-proof receipt or certificate attached to every piece of media, confirming its origin and identity. How much easier would life be, and how much more assured would you feel once you spot the seal that confirms originality? I know I would heave a sigh of relief. Real-World Blockchain Applications Are Already Here The great thing is, you don’t have to just imagine this happening or wait expectantly for it to become a reality—it’s already happening. Toniq Labs uses NFTs (Non-Fungible Tokens) to represent digital assets’ origin and ownership. Toniq Labs Truepic provides verified photos and videos by cryptographically certifying content’s origin and integrity for journalism and insurance claims. Truepic Adobe’s Content Authenticity Initiative embeds origin metadata into creative files to track authenticity and modifications, integrating blockchain to store this metadata securely. Adobe’s Content Authenticity Initiative embeds Platforms like po.et enable creators to timestamp and claim ownership of written content metadata, helping verify publishing rights and originality. po.et po.et Other use cases include journalistic content verification to fight fake news, scientific data authenticity to prevent research fraud, and supply chain transparency applied to digital goods. These projects empower creators and consumers by making authenticity verifiable and transparent, building trust in digital content. Challenges on the Road to Mass Adoption While blockchain can make our information generation more authentic, several hurdles remain: Adoption and Usability It’s not enough to have blockchains; convincing creators to encrypt their work on blockchain takes effort. Then there’s the need to make verification easy and seamless for average users. Scalability Current blockchain networks have limits on transaction speed and cost, which challenge mass adoption for content verification. For example, Ethereum’s gas fees and transaction delays can be prohibitive for small-scale content verification. Privacy vs Transparency Blockchain’s transparency raises questions about how much transparency is too much transparency? What about sensitive information creators don’t want revealed, like identity or location? Exposing such data risks misuse. Solutions include off-chain storage, zero-knowledge proofs, privacy-focused blockchains, and permissioned blockchains where access is controlled. But balancing transparency and privacy remains a delicate, ongoing trade-off. Interoperability Different blockchain systems and protocols must work together for wide-scale effectiveness. Without cross-chain compatibility, users may experience friction, slowing adoption. Fragmentation may occur if verification tools are limited to specific blockchains. Solutions like cross-chain bridges—Polkadot, Cosmos, Chainlink — are bridging these gaps. Polkadot, Cosmos, Chainlink Regulatory and Ethical Questions Because blockchain is decentralized, questions remain: Who oversees content verification? What laws apply? Who handles disputes? How to comply with privacy laws like GDPR when data is immutable? Ethical questions arise about who decides “truth,” risks of weaponizing verification to silence dissent, and potential marginalization of less tech-savvy users. Collaborative efforts among regulators, technologists, and ethicists are essential to create frameworks ensuring blockchain tools are fair, transparent, accountable, and privacy-respecting. The Road Ahead: Why Blockchain is Our Best Bet Despite these challenges, blockchain stands out as our safest bet to deal with the AI-generated content pandemic. Positive signs include: Growing interest from governments and media organizations in blockchain for verification.


Combining blockchain with AI-powered content scanners for layered authentication.


Developing open-source standards and protocols focused on digital content authenticity.
According to a 2023 Deeptrace report, deepfake videos increased over 900% between 2019 and 2023. A 2024 Gartner prediction estimates that by 2026, 75% of online content will be synthetically generated. The global blockchain market is expected to reach $67.4 billion by 2026 (ResearchAndMarkets), signaling strong momentum for such applications.
Given these trends, it’s only a matter of time before a decentralized, blockchain-backed system for content authenticity becomes a foundational trust layer of the internet—critical in combating misinformation and AI-generated fake content.
How Do We Move Forward?
I have good news and bad news. The bad news is, we will have to stay vigilant for AI-generated content for quite a while.
The good news is, while blockchain might not be a cure-all, it is a key enabler for verifying digital truth. However, we won’t get the full benefits if we don’t start adopting these verification systems soon.
My opinion? Innovators, creators, and platforms must collaborate quickly to restore trust in the digital world, using blockchain as a promising piece of this complex puzzle.
As digital citizens, staying informed and supporting technologies like blockchain can help protect the truth. The fight against AI-generated misinformation needs all of us. Growing interest from governments and media organizations in blockchain for verification. Growing interest from governments and media organizations in blockchain for verification. Combining blockchain with AI-powered content scanners for layered authentication. Combining blockchain with AI-powered content scanners for layered authentication. Developing open-source standards and protocols focused on digital content authenticity.
According to a 2023 Deeptrace report, deepfake videos increased over 900% between 2019 and 2023. A 2024 Gartner prediction estimates that by 2026, 75% of online content will be synthetically generated. The global blockchain market is expected to reach $67.4 billion by 2026 (ResearchAndMarkets), signaling strong momentum for such applications.
Given these trends, it’s only a matter of time before a decentralized, blockchain-backed system for content authenticity becomes a foundational trust layer of the internet—critical in combating misinformation and AI-generated fake content.
How Do We Move Forward?
I have good news and bad news. The bad news is, we will have to stay vigilant for AI-generated content for quite a while.
The good news is, while blockchain might not be a cure-all, it is a key enabler for verifying digital truth. However, we won’t get the full benefits if we don’t start adopting these verification systems soon.
My opinion? Innovators, creators, and platforms must collaborate quickly to restore trust in the digital world, using blockchain as a promising piece of this complex puzzle.
As digital citizens, staying informed and supporting technologies like blockchain can help protect the truth. The fight against AI-generated misinformation needs all of us. Developing open-source standards and protocols focused on digital content authenticity. According to a 2023 Deeptrace report, deepfake videos increased over 900% between 2019 and 2023. A 2024 Gartner prediction estimates that by 2026, 75% of online content will be synthetically generated. The global blockchain market is expected to reach $67.4 billion by 2026 (ResearchAndMarkets), signaling strong momentum for such applications. Given these trends, it’s only a matter of time before a decentralized, blockchain-backed system for content authenticity becomes a foundational trust layer of the internet—critical in combating misinformation and AI-generated fake content. How Do We Move Forward? I have good news and bad news. The bad news is, we will have to stay vigilant for AI-generated content for quite a while. The good news is, while blockchain might not be a cure-all, it is a key enabler for verifying digital truth. However, we won’t get the full benefits if we don’t start adopting these verification systems soon. My opinion? Innovators, creators, and platforms must collaborate quickly to restore trust in the digital world, using blockchain as a promising piece of this complex puzzle. As digital citizens, staying informed and supporting technologies like blockchain can help protect the truth. The fight against AI-generated misinformation needs all of us.

How Blockchain Can Help Us Trust What We Read and See

As the line between AI automation and autonomous agency blurs, a new technical frontier is emerging, trusted, verifiable agents that can act independently across ecosystems. We sat down with Matthieu Jung, the Product Marketing Manager at iExec, to explore what this shift means for the future of intelligent infrastructure, what problems it solves, and how developers and companies should be thinking differently about agent-based computation. iExec Ishan Pandey: Thanks for joining us. To start, can you explain what "Trusted AI Agents" actually are in your framework, and how they differ from the typical AI agents we see in automation or LLM-based products? Ishan Pandey: Matthieu Jung: We all know AI agents are the new interface. They change how we create, build, and collaborate. But to scale, agents need one thing: trust. Privacy is non-negotiable. Agents work with sensitive data: private prompts, crypto assets, wallets. Without privacy there’s no trust, and without trust agents don’t scale. That’s why Trusted AI Agents run entirely inside secure enclaves like Intel TDX. Our Confidential Computing stack keeps their logic, data, and actions private, even from the host, while generating verifiable proofs of execution. Unlike typical LLM-based agents that rely on central APIs and expose user data, iExec provides the trust layer so agents can act autonomously with full confidentiality and proof. Matthieu Jung: Ishan Pandey: The collaboration between iExec and Eliza OS seems to merge privacy infrastructure with autonomous computation. How are you technically achieving both verifiability and confidentiality? Aren’t they often in conflict? Ishan Pandey: Matthieu Jung: That’s a great question, and it's central to what makes Trusted AI Agents different. The tension between confidentiality and verifiability is real in most architectures, but not in ours. Matthieu Jung: With the ElizaOS x iExec stack, we’re leveraging Trusted Execution Environments (TEEs), specifically Intel TDX, to isolate not just the agent logic built with ElizaOS, but also the AI model itself, the one used to train or fine-tune the agent. This is crucial. It means the entire runtime environment, including both the logic and the sensitive model parameters, is protected against external access, even from cloud hosts or system administrators. At the same time, these enclaves generate cryptographic proofs that certify the integrity of the execution. This is how we achieve verifiability: anyone can verify what was executed, without seeing how it works. There’s no need to choose between exposing the model for auditability or hiding it at the cost of trust. We provide both, thanks to the architecture of Confidential Computing. So to sum up: TDX enclaves bring confidentiality (by securing the model and logic) and verifiability (by producing proofs of correct execution). That’s the foundation of Trusted AI Agents. Ishan Pandey: In a world where many AI startups rely heavily on centralized APIs and closed models, what does it take to build autonomous agents that are decentralized by design? Ishan Pandey: Matthieu Jung: It takes a new stack that adds private verifiable execution to APIs. With confidential computing iExec makes it easy to prove every action on chain. iExec just launched the iApp generator, a dev tool that allows developers can build and deploy confidential apps in minutes. Our team is also launching MCP Model Context Protocol servers optimized for Trusted Agents so they can scale while remaining verifiable. Matthieu Jung: Ishan Pandey: How does the iExec infrastructure help agents prove their work, state, or logic to external parties? Ishan Pandey: Matthieu Jung: iExec runs agents inside Intel TDX enclaves that generate signed proofs of the code, inputs, and outputs. Those proofs can be shared on chain so anyone can verify the enclave identity and confirm the agent did exactly what it promised, without ever exposing the private data or code. Matthieu Jung: Ishan Pandey: Let's talk about risk. What’s the potential downside of allowing autonomous AI agents to operate across chains or applications? Are we ready for truly composable intelligence? Ishan Pandey: Matthieu Jung: Allowing agents to roam across chains or apps raises new attack surfaces and unexpected interactions. A bug in one protocol could cascade into another. Plus, without clear standards, proving liability or reversing bad actions gets tricky. That’s why trust and governance is still so important when it comes to decentralized agents and automating actions on the blockchain. Matthieu Jung: Ishan Pandey: Can you walk us through a hypothetical example or use case where a Trusted AI Agent is deployed in the wild, what it does, how it proves its execution, and why it’s better than traditional models? Ishan Pandey: Matthieu Jung: Imagine AI agents that trade for you, managing DeFi strategies, reading signals, placing trades, and adapting portfolios. It needs to interact with sensitive data and assets like your private key or wallet. With iExec, the agent’s logic runs confidentially in a TEE. Traders can protect their datasets, share access securely with a model, and even receive confidential alerts. Every trade and every decision is executed securely and verifiably. They can even monetize their private trading data. You don’t have to trust the dev or the infra. Matthieu Jung: Ishan Pandey: Finally, for builders and investors watching the Trusted AI Agent narrative unfold, what are the signals they should be paying attention to over the next 12 months? Ishan Pandey: Matthieu Jung: We already know agents are the new interface. And unfortunately, that means we will start seeing leaks of agent prompts and data exposures, and that will underscore why private execution matters. Another innovation that will take Agents to the next level is MCP or Model Context Protocol. It lets agents securely share encrypted snapshots of their working state as they move and iterate across multiple apps. iExec has already deployed an MCP server. These trends will reveal who’s truly building decentralized, privacy-first agents versus those still tied to closed APIs. Matthieu Jung: Don’t forget to like and share the story!

Unlocking Autonomous AI: iExec's Matthieu Jung on Building Trust in a Decentralized Future

Ontbloot autonome AI: iExec se Matthieu Jung op die bou van vertroue in 'n gedecentraliseerde toekoms

Отключване на автономния ИИ: Матю Юнг на iExec за изграждане на доверие в децентрализирано бъдеще

Desbloquear la IA autónoma: Matthieu Jung de iExec sobre la construcción de la confianza en un futuro descentralizado

Desbloquear a IA autónoma: Matthieu Jung de iExec sobre a construción de confianza nun futuro descentralizado

स्वायत्त एआई को अनलॉक करना: iExec के मैथ्यू जंग ने एक डिसेन्ट्रेटेड भविष्य में विश्वास का निर्माण किया

Otključavanje autonomne umjetne inteligencije: Matthieu Jung iz iExeca o izgradnji povjerenja u decentraliziranu budućnost

自動人工知能のブロック解除:iExecのマシュー・ユングが分散型の未来における信頼構築について

자율적인 AI를 해제 : 분산된 미래에 대한 신뢰를 구축하는 iExec의 Matthieu Jung

Автоном AI-ийг сэргээх: iExec-ийн Матью Юнг нь децентрализованы будущегод итгэл бий болгох талаар

Odblokowywanie autonomicznej sztucznej inteligencji: Matthieu Jung z iExec na temat budowania zaufania w zdecentralizowanej przyszłości

Autonomy AI Unlocking: iExec's Matthieu Jung on Building Trust in a Decentralized Future (தொடர்பு)

Ukukhishwa kwe-AI ye-Autonomous: Matthieu Jung ye-iExec kwi-Building Trust kwi-Future ye-Decentralized

Rohit Sindhu, a Senior Principal Engineer with over 22 years of experience in computer science, embedded systems, and high-performance interconnects, has made a significant mark in the storage industry with his recently granted US patent, US11237760B2: “Measuring performance metrics for data storage devices.” Sindhu’s career is defined by technical depth, innovation, and a passion for advancing the state of the art in PCIe Express, CXL, MCTP, and NVMe storage technologies. In addition to this milestone patent, Sindhu has multiple pending patents in the PCIe, CXL, and MCTP domains, further underscoring his ongoing contributions to next-generation interconnect and storage solutions. Notably, he has also contributed to the development of CXL and JEDEC specifications related to CXL and PCIe device management, reflecting his influence on industry standards and best practices. A Career Dedicated to Embedded, Memory and Storage Innovation Rohit Sindhu’s professional journey spans more than two decades, during which he has become widely recognized for his expertise in both hardware and software domains. He holds a Master’s degree in Computer Science from the University of Texas at Dallas with a Bachelor’s Degree in Computer Engineering from NIT Surat, Gujarat, India and has contributed to the development of embedded systems for mission-critical applications, architected solutions across the PCIe, MCTP and CXL protocol stacks, and driven innovation in Memory & storage performance, availability, manageability and reliability. Sindhu’s technical leadership and mentorship have impacted teams and products across the industry. He is known for bridging the gap between complex engineering challenges and practical, scalable solutions. Over the years, he has worked on a diverse range of projects, from firmware development and system-on-chip design to advanced debug systems and high-speed interconnect protocols. His work has spanned sectors including enterprise storage, data centers, defense, and emerging edge computing applications. As a Senior Principal Engineer in his current role, Sindhu continues to focus on the intersection of high-speed interconnects and data storage, developing solutions that address real-world bottlenecks and enable next-generation computing platforms. His broad technical background allows him to approach problems holistically, considering both hardware and software aspects to deliver robust, high-performance solutions. Sindhu is also an active mentor and thought leader in the technology community. He regularly shares insights on storage trends, PCIe and CXL advancements, and best practices for embedded systems design. His professional profile and further details about his career can be found on his LinkedIn page. US11237760B2: Raising the Bar for NVMe Latency Measurement Sindhu’s patent, “Measuring performance metrics for data storage devices,” addresses a long-standing challenge in the storage industry: accurately measuring the latency of NVMe commands in high-speed PCIe environments without introducing the overhead or inaccuracies common to traditional software-based tools. The Problem: Latency Measurement in Modern Storage In today’s data-driven world, the performance of NVMe-based storage systems is critical for applications ranging from cloud computing and artificial intelligence to real-time analytics and edge computing. Latency, the time it takes for a storage command to be processed and completed directly impacts application responsiveness, quality of service (QoS), and overall infrastructure efficiency. Traditional latency measurement methods, often software-based, can introduce significant observational overhead, skewing results and failing to capture the true performance characteristics of storage devices. Hardware probes, while useful, sometimes lack the granularity needed to isolate individual command latencies, especially in environments with high command throughput and parallelism. Sindhu’s Solution: An FPGA-Accelerated Approach Sindhu’s patented method leverages FPGA-based hardware logic to passively monitor PCIe transactions between the host and storage device, precisely timing the lifecycle of individual NVMe commands. The process unfolds in a series of orchestrated steps designed to ensure accuracy, scalability, and zero impact on normal system operation. Step-by-Step Breakdown: Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic.



Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ.



PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally.



Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range.



Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic. Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic. Queue Pair Creation Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ. Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ. Command Injection PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally. PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally. PCIe Transaction Snooping Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range. Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range. Tag Matching and Timer Start Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Completion Monitoring and Timer Stop This approach ensures that performance metrics are captured with nanosecond-level accuracy and zero impact on the normal operation of the host or device, a significant advancement over existing methods. Technical Advantages and Industry Impact Sindhu’s invention offers several key advantages over traditional software and hardware-based measurement tools: Zero Observational Overhead: The FPGA operates passively on the PCIe bus, leaving host and device workloads unaffected. There is no need for intrusive software agents or kernel modifications. Zero Observational Overhead: The FPGA operates passively on the PCIe bus, leaving host and device workloads unaffected. There is no need for intrusive software agents or kernel modifications. Zero Observational Overhead: Protocol-Agnostic and Scalable: While optimized for NVMe, the method can be adapted to any PCIe-based protocol, including emerging standards like CXL.io, by reconfiguring address ranges and command parsing logic. Protocol-Agnostic and Scalable: While optimized for NVMe, the method can be adapted to any PCIe-based protocol, including emerging standards like CXL.io, by reconfiguring address ranges and command parsing logic. Protocol-Agnostic and Scalable Multi-Command Parallelism: The FPGA’s ability to track multiple Tags simultaneously enables concurrent latency measurement across many NVMe commands—critical for assessing real-world, high-throughput workloads. Multi-Command Parallelism: The FPGA’s ability to track multiple Tags simultaneously enables concurrent latency measurement across many NVMe commands—critical for assessing real-world, high-throughput workloads. Multi-Command Parallelism Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility Applications Across the Storage Ecosystem Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting.



Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers.



Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation.



CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting. Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting. Data Center Optimization Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers. Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers. Storage OEM Validation Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation. Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation. Autonomous and Edge Systems CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. CXL and Next-Gen Storage A Visionary Leader and Mentor Beyond his technical achievements, Rohit Sindhu is recognized as a mentor and advocate for innovation in the embedded, memory, and storage communities. He has guided teams through complex engineering challenges, shared his expertise through technical talks and publications, and actively supports the next generation of engineers. Sindhu’s approach is characterized by a relentless pursuit of precision, efficiency, and scalability. His work on US11237760B2 exemplifies his commitment to solving real-world problems with elegant, practical solutions that have a lasting impact on the industry. Looking Forward As storage technologies continue to evolve to meet the demands of AI, big data, and cloud-scale infrastructure, the need for accurate and efficient performance measurement will only grow. Rohit Sindhu’s patented approach provides a robust foundation for the next wave of innovation in storage analytics and validation. With multiple pending patents in PCIe, CXL, and MCTP domains, and with his direct contributions to CXL and JEDEC specifications related to CXL and PCIe device management, Sindhu’s influence on the next generation of high-speed interconnects and storage technologies is set to expand even further. To learn more about Rohit Sindhu’s professional journey, connect with him on LinkedIn. LinkedIn LinkedIn US11237760B2 is more than a patent—it is a testament to Rohit Sindhu’s enduring impact on the technology industry, and a blueprint for the future of precise, hardware-accelerated storage performance measurement. US11237760B2 is more than a patent—it is a testament to Rohit Sindhu’s enduring impact on the technology industry, and a blueprint for the future of precise, hardware-accelerated storage performance measurement. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. here here

A Breakthrough in Storage Analytics: How Rohit Sindhu's Patent Enables Nanosecond-Level NVMe Latency

Un avance en el análisis de almacenamiento: cómo la patente de Rohit Sindhu permite la latencia NVMe a nivel de nanosegundo

Un avance na análise de almacenamento: como a patente de Rohit Sindhu permite a latencia NVMe a nivel de nanosegundos

פריצת דרך בניתוח אחסון: כיצד פטנט רוהיט סינדו מאפשר ננו-שנייה רמות NVMe איחור

Yon Breakthrough nan Analytics depo: Ki jan Rohit Sindhu a patant pèmèt nan nan-sekond-nivo NVMe latency

A Breakthrough in Storage Analytics: How Rohit Sindhu's Patent Enables Nanosecond-Level NVMe Latency (ストレージアナリティクスにおける突破:ロヒット・シンドゥーの特許がどのようにNVMeレベルの遅延を可能にするか)

스토리지 분석의 획기 : 로히트 시드루 (Rohit Sindhu)의 특허가 어떻게 나노초 수준의 NVMe 지연을 가능하게 하는가

Przełom w analizie przechowywania: Jak patent Rohita Sindhu umożliwia opóźnienie NVMe na poziomie nanosekundowym

Prelom v analýze ukladacieho priestoru: Ako patent Rohit Sindhu umožňuje nanosekundovú latenciu NVMe

Shuruudaha ah ee Analysis Storage: Sida Patent Rohit Sindhu waxay la isticmaali karaa NVMe nanosecond-level latency

Ufanisi katika Uchambuzi wa Uhifadhi: Jinsi Patent ya Rohit Sindhu Inawezesha Latency ya NVMe ya Nano-Second

Depolama Analizinde Çarpışma: Rohit Sindhu'nun Patentinin Nanosekundelik NVMe Latensiyeni Nasıl Etkilediği

I-Breakthrough ku-Storage Analytics: Indlela I-Patent ye-Rohit Sindhu Inikeza I-Nanosecond-Level NVMe Latency

In today's rapidly evolving telecommunications landscape, where 5G technology deployment faces rigorous scrutiny and complex regulatory requirements, the exceptional success of the Field Testing Carrier Certification Program at a leading global consulting firm stands as a testament to strategic leadership and technical excellence. Under the guidance of Test Engineering Manager Kranthi Kiran Kusuma, this critical initiative has established new benchmarks for carrier certification, technology implementation, and cross-functional collaboration across North America's telecommunications sector. The ambitious program, focused on certifying new and existing technologies for deployment across carrier networks, presented substantial challenges in coordination, technical compliance, and timeline management. With responsibility for managing vendor relationships, serving as the technical point of contact for major carriers, and overseeing multiple field testing processes simultaneously, Kranthi Kiran Kusuma navigated the complex matrix of stakeholder requirements while maintaining strict quality controls and achieving aggressive certification milestones. The program's scope encompassed a broad spectrum of cutting-edge technologies, requiring precise evaluation methodologies and deep technical knowledge. With the telecommunications industry rapidly transitioning toward 5G implementation, the certification program faced unprecedented challenges in validating performance across diverse network environments and usage scenarios. The technical complexity was further amplified by the need to ensure backward compatibility with existing infrastructure while simultaneously pushing the boundaries of next-generation capabilities. At the core of this success story was a methodical and comprehensive approach to program management. As the primary authority for technical guidance and certification protocols, Kranthi implemented rigorous testing frameworks and innovative evaluation methodologies that not only met but substantially exceeded carrier requirements. His leadership in developing detailed project plans while simultaneously mitigating risks ensured the program maintained its critical path despite the inherent complexities of field testing emerging technologies like 5G FR2/FR1 and 4G LTE. Kranthi's technical expertise proved invaluable in navigating the intricate certification landscape. His deep understanding of radio frequency characteristics, signal propagation patterns, and network architecture enabled him to anticipate potential issues before they manifested during field testing. This proactive approach to problem identification and resolution significantly reduced testing iterations and accelerated the certification timeline. Particularly noteworthy was his ability to translate complex technical requirements into actionable testing protocols that field teams could execute with precision and consistency. The impact of this leadership extended far beyond basic compliance metrics. Through strategic resource allocation and efficient team coordination, the program achieved significant optimization of testing processes, resulting in expedited certification timelines – a notable accomplishment in the telecommunications certification landscape. This streamlined approach was achieved while maintaining exceptional quality standards, with comprehensive documentation and training initiatives ensuring consistent performance across multiple parallel projects. The program's success was further enhanced by Kranthi's implementation of innovative testing methodologies that leveraged advanced diagnostic tools and data analytics to identify performance patterns and optimization opportunities. By introducing automated test sequences for routine certification elements, he freed technical resources to focus on more complex evaluation scenarios, effectively multiplying the program's testing capacity without corresponding increases in personnel. This technological leverage represented a significant advancement in certification efficiency, establishing new industry benchmarks for resource utilization and throughput. Financial management played a crucial role in the program's success. Through careful budget forecasting, precise quote preparation for OEMs, and diligent invoicing oversight, Kranthi ensured financial accountability while contributing substantially to program profitability. This fiscal discipline, combined with technical excellence, created a sustainable framework for carrier certification that balanced quality, speed, and cost-effectiveness. His implementation of value-based pricing models for certification services aligned fee structures with the complexity and strategic importance of different technologies, optimizing revenue generation while providing clients with transparent cost structures. The program's financial performance exceeded initial projections, with efficiency improvements directly translating to bottom-line benefits. By identifying opportunities to consolidate testing requirements across multiple certification pathways, Kranthi reduced redundant processes and associated costs. His strategic approach to resource allocation—deploying specialized expertise precisely where and when needed—further enhanced the program's cost structure while maintaining exceptional quality standards. These financial innovations established the certification program as both a technical center of excellence and a significant contributor to business growth. Stakeholder management proved to be a defining element of the program's effectiveness. The exceptional performance in addressing technical queries and resolving certification challenges left lasting impressions with carriers and OEMs alike, strengthening the company's position as a trusted partner in the telecommunications ecosystem. This relationship-building aspect of Kranthi's leadership approach has created long-term value beyond individual certification projects. The complexity of stakeholder management was particularly evident in navigating the sometimes divergent priorities of network operators, device manufacturers, and regulatory authorities. Kranthi demonstrated remarkable diplomatic skill in aligning these diverse perspectives toward common certification objectives, often facilitating technical compromises that satisfied all parties while maintaining compliance with industry standards. His ability to communicate complex technical concepts in accessible language enhanced transparency throughout the certification process, building confidence among stakeholders and reinforcing the program's credibility as an objective authority in technology validation. For Kranthi Kiran Kusuma personally, the program represented a significant career milestone, showcasing his ability to lead complex technical initiatives involving cutting-edge technologies while managing cross-functional teams and stakeholder expectations. The success has strengthened his position as a versatile leader in telecommunications, opening pathways to senior project and program management roles with increasingly strategic responsibilities. The program's achievements have established him as an industry authority in carrier certification, with senior executives across multiple organizations recognizing his distinctive ability to navigate both the technical and business dimensions of telecommunications implementation. The knowledge capital developed through the program extended beyond immediate certification outcomes. Kranthi's systematic documentation of testing methodologies and certification pathways created intellectual property that enhanced the organization's service offerings and established repeatable frameworks for future implementations. His willingness to mentor junior team members and share technical insights fostered a culture of excellence that extended beyond his immediate responsibilities, creating a multiplier effect that enhanced the organization's overall capabilities in telecommunications field testing certification. This success story illustrates how technical expertise, when combined with effective program management practices, can transform the implementation of advanced telecommunications technologies. The Field Testing Carrier Certification Program not only contributed to the expansion of next-generation network infrastructure but also established new standards for certification processes in the sector. As the industry continues its rapid evolution toward 5G and beyond, this program serves as a compelling example of how focused leadership can drive exceptional results in complex technology implementation. The program's impact on the broader telecommunications ecosystem was particularly noteworthy. By elevating certification standards while simultaneously improving process efficiency, Kranthi's leadership accelerated the market readiness of critical technologies, enabling faster deployment of advanced connectivity solutions. This ripple effect extended to end users across North America, who benefited from earlier access to enhanced network capabilities and innovative services. The certification program thus served not only organizational objectives but also the broader industry goal of advancing telecommunications infrastructure to meet evolving consumer and enterprise demands. Looking ahead, the implications of this program success extend beyond immediate achievements. It demonstrates how effective project management can overcome intricate technical challenges while delivering exceptional value to telecommunications stakeholders. As the sector continues to advance with emerging wireless technologies, the certification program stands as a model for future implementations, showcasing the powerful combination of technical leadership, operational excellence, and stakeholder management in driving successful outcomes under Kranthi Kiran Kusuma's capable guidance. The certification methodologies pioneered in the program have created a foundation for addressing future technological challenges, including those associated with network virtualization, edge computing integration, and IoT device proliferation. The frameworks established under Kranthi's leadership demonstrate remarkable adaptability to evolving certification requirements, providing a sustainable approach to technology validation that can scale with industry advancement. This forward-looking dimension of the program underscores the strategic vision behind its implementation and highlights Kranthi Kiran Kusuma's role not merely as a program manager but as an architect of telecommunications excellence and innovation. About Kranthi Kiran Kusuma With a distinguished career spanning over 15 years in the telecommunications industry, Kranthi Kiran Kusuma has emerged as a visionary leader at the intersection of technical innovation and strategic program management. His exceptional expertise in carrier certification processes has positioned him as a sought-after authority in the implementation of next-generation wireless technologies. Based in Ontario, California, Kranthi has built a reputation for excellence through his meticulous attention to technical detail combined with a broad understanding of industry trends and regulatory frameworks. Throughout his career, Kranthi has spearheaded critical initiatives for leading telecommunications organizations, demonstrating remarkable versatility in managing complex certification requirements across diverse network environments. His comprehensive knowledge of cellular technologies—from emerging 5G applications to established 4G LTE frameworks—is complemented by advanced skills in project planning, risk mitigation, and cross-functional team leadership. This unique combination of technical depth and management breadth has enabled him to consistently deliver results that exceed industry standards and stakeholder expectations. Kranthi's approach to telecommunications challenges is characterized by an innovative mindset and unwavering commitment to quality, earning him recognition as a transformative force in an industry undergoing rapid evolution. As telecommunications continues its advancement toward increasingly sophisticated connectivity solutions, Kranthi Kiran Kusuma remains at the forefront—driving excellence, fostering collaboration, and establishing new paradigms for successful technology implementation. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. here here

Advancing Telecommunications Excellence: Kranthi Kiran Kusuma's Leadership in Carrier Certification

የቴሌኮሙኒኬሽን Excellence Advancing: Kranthi Kiran Kusuma's Leadership in Carrier Certification

Napredovanje Telekomunikacije Excellence: Kranthi Kiran Kusuma je lider u certifikaciji prevoznika

Avanzar la excelencia en telecomunicaciones: el liderazgo de Kranthi Kiran Kusuma en la certificación de transportistas

קידום מצוינות בטלקום: Kranthi Kiran Kusuma's Leadership in Carrier Certification

Napredovanje izvrsnosti u telekomunikacijama: Kranthi Kiran Kusumaovo vodstvo u certificiranju prijevoznika

Advancing Telecommunications Excellence: Kranthi Kiran Kusuma's Leadership in Carrier Certification (Kranthi Kiran Kusumaのキャリア認証のリーダーシップ)

ការអភិវឌ្ឍគុណភាពទូរទស្សន៍ទូរទស្សន៍: Kranthi Kiran Kusuma's Leadership in Carrier Certification

Pamba la Pamba la Pamba la Pamba la Pamba la 100%

Telekomunikāciju izcilības veicināšana: Kranthi Kiran Kusuma vadība pārvadātāju sertifikācijā

پرمختللي تلويزيون Excellence: Kranthi Kiran Kusuma د ګرځنده تصدیق کې د لارښوونې

Напредна изврсност у телекомуникацијама: Кранти Киран Кусума'с Лидерство у сертификацији носача

ٹیلی ویژن کے اعلی درجے کو فروغ دینے: Kranthi Kiran Kusuma کی نقل و حمل کے سرٹیفیکیشن میں قیادت

Meet SNATIKA, Winner of HackerNoon's Startups of The Year 2024 in Training And Consulting

Quality education is now costlier than it used to be. But this expense isn't about price; it's about access. In 2019, the World Bank recorded that 53% of children who suffer from learning poverty, with 55% in lower-middle-income countries and 90% in low-income countries. They can’t read proficiently. A 2024 press release from UNESCO estimates the yearly cost of this on the global economy to be $10,000 billion. 53% of children 53% of children $10,000 billion $10,000 billion Several reasons prevent universal access to quality education, and technology has tried to bridge the gap to a large extent. For instance, lack of funding and distance, which are major barriers to quality tertiary education, have been met with cost-effective remote learning opportunities. And now that AI is here, the ride is about to get smoother — only if it is maximized properly. We'll look at some ways to achieve this, but first, let's explore the other critical barriers to quality education. Barriers Preventing Access to Quality Education Several factors hinder quality education globally, but I like to categorize them into three. When these problems get addressed, other problems will become easier to tackle. These three are distance, funding, and the availability of professional educators. Distance Distance Distance’s geographical barrier is predominantly significant in rural and developing areas where transportation means are not diversified. In many areas of sub-Saharan Africa, Latin America, and Asia, schools offering quality education are usually few and far from each other. Eventually, guardians are forced to settle for less or nothing. Funding Funding Apart from tuition, many families cannot afford other necessities to facilitate their child's education. While some areas have government support, it is inadequate or unavailable in others. In addition, the cost of transportation might as well be overbearing. This financial disparity limits educational opportunities, leading to cycles of poverty and inequality. Lack of qualified educators Lack of qualified educators The unavailability of qualified educators significantly affects student performance and learning outcomes. In well-developed countries, teachers and educators undergo training that certifies them as professionals. However, many institutions cannot afford the cost of hiring a professional. Hence, they hire underqualified people who cannot implement effective teaching methods. How Can AI Democratize Access to Quality Education? With advancements in artificial intelligence, it has become much easier to tackle these challenges, because apart from being versatile, AI is accessible anywhere there's an internet. Take a look at these five ways AI removes most of the friction in democratizing education. Personalized Learning Personalized Learning When I think of personalized learning, I think of the adage, "All fingers aren't equal." Traditional educational methods make it seem like the opposite. They use the same materials, educators, and teaching styles, forgetting that students learn at different paces. AI, on the other hand, can effectively tailor exercises, lessons, and approaches to each student. It can track a student's progress, analyze their strengths and weaknesses, and then use that information to provide feedback or improve the learning process. For instance, a student struggling with a topic can use AI to double down with more lessons, examples, and exercises. At the same time, it increases the learning pace for such student to make quick progress with the curriculum. Language Translation and Localization Language Translation and Localization The fastest solution to language barriers is translation. Most of the materials and educational content today are available in dominant languages, creating a barricade for people who speak minority languages. As natural language processing (NLP) advances, tools like Google Translate can translate written and spoken resources into multiple languages. Apart from translation, there's also localization. NLP tools can potentially translate information in a culturally relevant way to the student's background. With this, students in rural or underdeveloped areas can benefit from AI-powered translation tools that bring them closer to the same resources available in more developed countries. Access to Educational Content Access to Educational Content Before AI, technology had made resources more accessible than it used to be. Webs one and two have made it possible for people to read information on blogs and websites and even download PDFs. AI takes this a step further by making the information on the internet more accessible. For instance, a quick Google search no longer only brings you results. It summarizes the information it can get regarding your search through its search lab. AI-powered algorithms like Chatgpt, Google search labs, copilot, perplexity AI, etc. can sift through vast amounts of educational materials, such as textbooks, online courses, videos, and research papers, and recommend the most relevant and effective resources for students. This increases access and flexibility, which encourages learning. Automating Administrative Tasks Automating Administrative Tasks Educational quality is not only measured by the student's learning process alone. It also has to deal with how learning is managed in institutions at various levels. For learning to be managed effectively, administration must go on without a hick. Tasks like grading assignments, enrollment, record keeping, etc., are all important to keep the learning environment healthy. Some advanced e-learning institutions already use AI to automate most of these tasks. AI can track the student's performance over time and recommend where to improve. That way, educators can focus on teaching and giving personalized advice to students, while administrators can focus on decision-making. Support for Special Education Support for Special Education Students with disabilities have trouble hearing, seeing, or doing other things that aid the process. Some may not be able to travel long distances or afford assistive learning. AI assistive learning tools can provide support that removes these extra barriers. Speech recognition, captioning, text-to-speech, and sign language translation are all AI-powered solutions that students with special needs can benefit from. Visually impaired students can use the text-to-speech function on their devices to access what's on their screen. Students with hearing impairments can read AI-powered captions, and dyslexic students can benefit from tools that improve reading comprehension. Conclusion Artificial intelligence has the potential to democratize access to quality education globally. More than just potential, many learning platforms already use it to expand access to educational content and improve their learning system. Through AI, students, regardless of location or ability, can benefit from tailored, personalized learning using the best resources. Learning can become more inclusive, efficient, and effective as long as it is used ethically and responsibly.

5 Ways Artificial Intelligence Can Help Democratize Access to Quality Education

1. Introduction In the context of blockchain technology, a blockchain node is a fundamental component that forms the backbone of any blockchain network. Based on the Pasifika Web3 Tech Hub infrastructure, blockchain nodes are essentially computers that process data, monitor conditions, and support the network by participating in the validation with verification of transactions and blocks. blockchain node Pasifika Web3 Tech Hub Nodes are critically important for blockchain networks because they create the decentralized infrastructure that makes blockchains resilient and trustworthy. Unlike traditional centralized systems where a single entity controls all operations, blockchain networks distribute control across numerous nodes operated by different participants. In the Pasifika Web3 Tech Hub's DePIN (Decentralized Physical Infrastructure Network) model, nodes help secure and maintain the blockchain by: Processing transactions related to data, digital content, traditional knowledge, handicrafts, and local produce
Maintaining a synchronized copy of the blockchain ledger
Enforcing the rules and protocols of the network
Contributing to the consensus mechanism that validates new transactions
Supporting physical and digital infrastructure development that benefits Pacific Island communities Processing transactions related to data, digital content, traditional knowledge, handicrafts, and local produce Maintaining a synchronized copy of the blockchain ledger Enforcing the rules and protocols of the network Contributing to the consensus mechanism that validates new transactions Supporting physical and digital infrastructure development that benefits Pacific Island communities 2. Types of Blockchain Nodes The Pasifika Web3 Tech Hub's constitution acknowledges several types of node operators within the ecosystem: Personal Computer Nodes These are standard nodes operated by individuals on personal computers, processing data, monitoring conditions, and supporting the network. These form the basic infrastructure of the Pasifika blockchain. Local Distribution Hubs These specialized nodes serve as physical centers connecting remote communities to the blockchain network, ensuring inclusivity even in areas with limited technological access. Craft Documentation Centers These specialized facilities act as nodes for digitizing traditional crafts and uploading them to the blockchain as dynamic NFTs, preserving cultural heritage while enabling artisans to monetize their work. Full Nodes vs. Light Nodes While not explicitly mentioned in the Pasifika constitution, blockchain networks typically include: Full nodes: Store the entire blockchain and verify all transactions and blocks
Light nodes: Store only block headers and rely on full nodes for complete verification
Mining/Validator nodes: Participate in consensus mechanisms like Proof of Work or Proof of Stake Full nodes: Store the entire blockchain and verify all transactions and blocks Full nodes Light nodes: Store only block headers and rely on full nodes for complete verification Light nodes Mining/Validator nodes: Participate in consensus mechanisms like Proof of Work or Proof of Stake Mining/Validator nodes Node Operators and IoT Sensor Networks The Pasifika ecosystem integrates node operators with IoT sensor networks for collecting physical data that updates dynamic NFT states, particularly useful for agricultural products and environmental monitoring. 3. How Blockchain Nodes Process Transactions In the Pasifika Web3 Tech Hub ecosystem, nodes play a critical role in processing various types of transactions: Verification and Storage Process When a transaction is initiated (such as listing traditional handicrafts or agricultural products as dynamic NFTs), it is broadcasted to the network
Node operators receive the transaction and verify its validity by checking:

Metadata completeness and accuracy
Cultural appropriateness (for traditional knowledge and artifacts)
Quality standards and authenticity
Smart contract terms and conditions


Once verified, the transaction is added to a block
The block is validated through the consensus mechanism
After validation, the block is added to the blockchain
Node operators update their copy of the ledger When a transaction is initiated (such as listing traditional handicrafts or agricultural products as dynamic NFTs), it is broadcasted to the network Node operators receive the transaction and verify its validity by checking:

Metadata completeness and accuracy
Cultural appropriateness (for traditional knowledge and artifacts)
Quality standards and authenticity
Smart contract terms and conditions Metadata completeness and accuracy
Cultural appropriateness (for traditional knowledge and artifacts)
Quality standards and authenticity
Smart contract terms and conditions Metadata completeness and accuracy Cultural appropriateness (for traditional knowledge and artifacts) Quality standards and authenticity Smart contract terms and conditions Once verified, the transaction is added to a block The block is validated through the consensus mechanism After validation, the block is added to the blockchain Node operators update their copy of the ledger Consensus Mechanisms While the specific consensus mechanism isn't detailed in the Pasifika constitution, the blockchain operates on a Layer-2 technology, suggesting it may use a more efficient consensus mechanism rather than the traditional Proof of Work: Proof of Stake (PoS): Node operators stake PASIFIKA tokens to validate transactions, creating an energy-efficient alternative aligned with the DAO's environmental stewardship value
Validator Approval: The document mentions validators who review and certify quality, authenticity, and cultural appropriateness
Token-Based Governance: Voting power is determined by token holdings and staking duration
Quadratic Voting: The system employs mechanisms to ensure fair representation Proof of Stake (PoS): Node operators stake PASIFIKA tokens to validate transactions, creating an energy-efficient alternative aligned with the DAO's environmental stewardship value Proof of Stake (PoS) Validator Approval: The document mentions validators who review and certify quality, authenticity, and cultural appropriateness Validator Approval Token-Based Governance: Voting power is determined by token holdings and staking duration Token-Based Governance Quadratic Voting: The system employs mechanisms to ensure fair representation Quadratic Voting 4. Managing Blockchain Nodes: Challenges and Solutions Running node infrastructure comes with significant challenges, particularly in the Pacific Island context: Challenges Technical Knowledge Requirements: Operating nodes requires specialized technical skills
Infrastructure Limitations: Internet connectivity and reliable electricity can be challenges in remote island communities
Hardware Requirements: Operating full nodes requires substantial computing resources
Energy Consumption: Traditional blockchain nodes can consume significant energy
Cultural Integration: Balancing technical operations with cultural respect and appropriate protocols Technical Knowledge Requirements: Operating nodes requires specialized technical skills Technical Knowledge Requirements Infrastructure Limitations: Internet connectivity and reliable electricity can be challenges in remote island communities Infrastructure Limitations Hardware Requirements: Operating full nodes requires substantial computing resources Hardware Requirements Energy Consumption: Traditional blockchain nodes can consume significant energy Energy Consumption Cultural Integration: Balancing technical operations with cultural respect and appropriate protocols Cultural Integration Pasifika's Solutions The Pasifika Web3 Tech Hub addresses these challenges through several innovative approaches: Technical Capacity Building:

Training programs for local token operations and management
Education initiatives for blockchain and cryptographic skills
Support for Pacific Islander developers and technical contributors
Documentation and knowledge resources in local languages


Sustainable Infrastructure:

Distributed token validation across Pacific Islands to ensure decentralization
Renewable energy sources (solar, hydro) prioritized for all operations
Low impact blockchain technology (Layer-2 solutions)


Local Distribution Hubs:

Physical centers in communities to bridge technical gaps
Localized support for node operators


Cultural Integration:

Island-specific node operator optimizations
Community validation of cultural appropriateness
Integration of traditional knowledge with node operations Technical Capacity Building:

Training programs for local token operations and management
Education initiatives for blockchain and cryptographic skills
Support for Pacific Islander developers and technical contributors
Documentation and knowledge resources in local languages Technical Capacity Building Training programs for local token operations and management
Education initiatives for blockchain and cryptographic skills
Support for Pacific Islander developers and technical contributors
Documentation and knowledge resources in local languages Training programs for local token operations and management Education initiatives for blockchain and cryptographic skills Support for Pacific Islander developers and technical contributors Documentation and knowledge resources in local languages Sustainable Infrastructure:

Distributed token validation across Pacific Islands to ensure decentralization
Renewable energy sources (solar, hydro) prioritized for all operations
Low impact blockchain technology (Layer-2 solutions) Sustainable Infrastructure Distributed token validation across Pacific Islands to ensure decentralization
Renewable energy sources (solar, hydro) prioritized for all operations
Low impact blockchain technology (Layer-2 solutions) Distributed token validation across Pacific Islands to ensure decentralization Renewable energy sources (solar, hydro) prioritized for all operations Low impact blockchain technology (Layer-2 solutions) Local Distribution Hubs:

Physical centers in communities to bridge technical gaps
Localized support for node operators Local Distribution Hubs Physical centers in communities to bridge technical gaps
Localized support for node operators Physical centers in communities to bridge technical gaps Localized support for node operators Cultural Integration:

Island-specific node operator optimizations
Community validation of cultural appropriateness
Integration of traditional knowledge with node operations Cultural Integration Island-specific node operator optimizations
Community validation of cultural appropriateness
Integration of traditional knowledge with node operations Island-specific node operator optimizations Community validation of cultural appropriateness Integration of traditional knowledge with node operations 5. Conclusion Nodes are absolutely essential to blockchain technology because they embody the core principles of decentralization, transparency, and community ownership. In the Pasifika Web3 Tech Hub's ecosystem, nodes not only support the technical infrastructure but also help preserve cultural heritage, promote sustainable agriculture, and empower Pacific Island communities. The future evolution of node infrastructure in systems like Pasifika's might include: Increased Specialization: Nodes with specific functions for cultural validation, agricultural tracking, or environmental monitoring


Greater Sustainability: Further improvements in energy efficiency through advanced consensus mechanisms


Enhanced Accessibility: More user friendly node operation to lower barriers to participation


Cultural Integration: Deeper alignment of node operations with indigenous knowledge systems


AI Integration: The Pasifika constitution mentions AI agent infrastructure for market operations, suggesting nodes may increasingly incorporate AI capabilities


IoT Expansion: More extensive integration with sensors and physical infrastructure Increased Specialization: Nodes with specific functions for cultural validation, agricultural tracking, or environmental monitoring Increased Specialization: Nodes with specific functions for cultural validation, agricultural tracking, or environmental monitoring Increased Specialization Greater Sustainability: Further improvements in energy efficiency through advanced consensus mechanisms Greater Sustainability: Further improvements in energy efficiency through advanced consensus mechanisms Greater Sustainability Enhanced Accessibility: More user friendly node operation to lower barriers to participation Enhanced Accessibility: More user friendly node operation to lower barriers to participation Enhanced Accessibility Cultural Integration: Deeper alignment of node operations with indigenous knowledge systems Cultural Integration: Deeper alignment of node operations with indigenous knowledge systems Cultural Integration AI Integration: The Pasifika constitution mentions AI agent infrastructure for market operations, suggesting nodes may increasingly incorporate AI capabilities AI Integration: The Pasifika constitution mentions AI agent infrastructure for market operations, suggesting nodes may increasingly incorporate AI capabilities AI Integration IoT Expansion: More extensive integration with sensors and physical infrastructure IoT Expansion: More extensive integration with sensors and physical infrastructure IoT Expansion As the Pasifika Web3 Tech Hub demonstrates, blockchain nodes can go beyond technical infrastructure to become tools for community empowerment, cultural preservation, and sustainable development. By democratizing access to blockchain technology through distributed node networks, communities can build systems that honor their values while embracing technological innovation.

Pasifika’s Blockchain Nodes Are Doing Way More Than Just Crunching Data

KEY TAKEAWAYS WasmEdge functions as a stand-alone option. It replaces traditional OCI runtime systems with its own faster and lighter solution.
These containers provide faster startups and reduced resource consumption. So, the performance is faster alongside minimal overhead expenses.
The secure sandboxing of WasmEdge containers creates an improved security environment that cuts down system vulnerabilities while reducing the exposed attack area.
WasmEdge containers can run on various hardware architectures. Some of the common include x86 together with ARM and RISC-V while maintaining full portability without needing any modifications.
WasmEdge allows users to perform simplified Kubernetes deployments that work perfectly with edge computing alongside serverless applications. WasmEdge functions as a stand-alone option. It replaces traditional OCI runtime systems with its own faster and lighter solution. These containers provide faster startups and reduced resource consumption. So, the performance is faster alongside minimal overhead expenses. The secure sandboxing of WasmEdge containers creates an improved security environment that cuts down system vulnerabilities while reducing the exposed attack area. WasmEdge containers can run on various hardware architectures. Some of the common include x86 together with ARM and RISC-V while maintaining full portability without needing any modifications. WasmEdge allows users to perform simplified Kubernetes deployments that work perfectly with edge computing alongside serverless applications. INTRODUCTION The application running landscape has undergone a complete revolution through docker containers. These create isolated systems that can be easily managed. Kubernetes workloads traditionally depend on OCI runtimes, including containers and CRI-O, for executing container operations. A newer and more efficient solution known as WebAssembly (Wasm) containers with WasmEdge technology emerges as an alternative. In this guide, I will explain the practical implementation of running WebAssembly-based containers on Kubernetes through WasmEdge by avoiding traditional container runtimes. Moreover, it covers complete explanations, starting from configuration deployment to debugging tasks and performance evaluations. You will end up with complete knowledge about deploying WasmEdge to implement containers that deliver high efficiency and speed alongside architectural portability. WHY REPLACE OCI RUNTIMES WITH WEBASSEMBLY Before jumping into the setup and practical steps, let’s first understand why you might want to replace OCI runtimes with WasmEdge. PERFORMANCE GAINS WITH WASMEDGE WasmEdge provides superior performance. It should be the primary reason to choose the platform. The initialization and resource usage of traditional containers last a long time. WasmEdge exists for fast operation. These containers begin operations in milliseconds while requiring fewer system resources. So they become suitable for time-sensitive applications and devices that operate under resource limitations, such as edge systems, and IoT components. When developing an application that needs speed-based scaling according to user demand, you should consider it. WasmEdge containers can establish themselves practically instantaneously compared to how long traditional Docker containers take to begin their operation. They offer a breakthrough benefit for systems that must respond in critical periods like serverless computing and edge computing deployments. SECURITY Standard containers provide running security issues when deployed in operational systems. The main vulnerability of running your container environment exposes applications to potential attacks that could harm neighboring applications sharing the same infrastructure. Each WebAssembly workload receives an isolated sandbox environment through WasmEdge execution. A new security layer is created through WasmEdge implementation effectively. It minimizes the vulnerable points in your system. Each WasmEdge container provides a secure environment that makes it harder for malicious code to spread. Therefore, the host system remains secure. In addition, the security model proves critical for multi-tenant cloud deployments and microservice development. Since it provides necessary isolation between various applications. The implementation of WasmEdge benefits developers who aim to establish application resistance against attacks. CROSS-PLATFORM COMPATIBILITY Among the useful features of WasmEdge is its capability to work across multiple platforms. The normal deployment of containers between different platforms like x86 and ARM requires modifications either in container images/platform-specific configuration implementation. WasmEdge shows different behavior in this respect. The container engine operates without issues on multiple processor systems that include x86 ARM and RISC-V. WasmEdge operates containers on cloud servers, edge devices, and Raspberry Pi without any changes to the initial configuration. The high level of portability reduces deployment complexity. Because your application needs to function within diverse execution environments. IDEAL FOR EDGE AND SERVERLESS COMPUTING WasmEdge demonstrates exceptional performance within edge and serverless computing domains. It's compatible with handling small and quick booting containers. WasmEdge serves efficient workloads because engineers developed it specifically for lightweight, small systems that need fewer resources than traditional containers. WasmEdge works flawlessly in situations where equipment has restricted processing power, such as smart sensors, edge devices, and IoT setups. WasmEdge serves developers who construct microservices or serverless applications through its optimal features. The quick boot times of containers match their need for scarce resources while operating. Such performance attributes make WasmEdge ideal for applications that require fast responsiveness under various traffic conditions, such as event-driven workloads or stateless applications. SETTING UP KUBERNETES FOR WASMEDGE CONTAINERS This section will explain the necessary steps for WasmEdge installation on Kubernetes platforms, although the Kubernetes experience is beneficial. PREREQUISITES The following items should be checked before starting: A Kubernetes 1.26+ cluster. WasmEdge deployment requires a Kubernetes service provider.


Your cluster needs Kubectl installation with optimal configuration to access it from any location.


All Kubernetes nodes must have WasmEdge runtime running.


Container with Wasm support, and CRI-O with WasmEdge integration. A Kubernetes 1.26+ cluster. WasmEdge deployment requires a Kubernetes service provider. A Kubernetes 1.26+ cluster. WasmEdge deployment requires a Kubernetes service provider. Your cluster needs Kubectl installation with optimal configuration to access it from any location. Your cluster needs Kubectl installation with optimal configuration to access it from any location. All Kubernetes nodes must have WasmEdge runtime running. All Kubernetes nodes must have WasmEdge runtime running. Container with Wasm support, and CRI-O with WasmEdge integration. Container with Wasm support, and CRI-O with WasmEdge integration. Install the WasmEdge on Kubernetes nodes by using the command below: curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash After installation, make sure that WasmEdge is installed rightly: wasmedge --version wasmedge --version This should return the version of WasmEdge you’ve installed. CONFIGURING KUBERNETES FOR WASMEDGE Your Kubernetes environment requires registration of WasmEdge as its runtime for containers. The process needs you to establish WasmEdge Runtime Class configuration for Kubernetes usage. WasmEdge can be used by Kubernetes instead of the default container, and CRI-O runtimes through this configuration. Create a RuntimeClass YAML file for WasmEdge: apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: wasmedge
runtimeHandler: wasmedge apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: wasmedge
runtimeHandler: wasmedge Apply this configuration using: kubectl apply -f runtimeclass-wasmedge.yaml kubectl apply -f runtimeclass-wasmedge.yaml This tells Kubernetes to use WasmEdge as a valid runtime for your containers. DEPLOYING A WEBASSEMBLY (WASM) CONTAINER FOR KUBERNETES Now, let’s discuss, how can you deploy a WebAssembly (Wasm) Container on Kubernetes: CREATING A WASMEDGE-BASED DOCKER IMAGE First, convert your application into a WebAssembly (Wasm) module. For example, try a Rust-based HTTP server and convert it into a Wasm module. Next, install the wasm-pack tool. It helps you compile Rust code into WebAssembly: cargo install wasm-pack cargo install wasm-pack Build the Rust app into a Wasm module: wasm-pack build --release --target=wasm32-wasi wasm-pack build --release --target=wasm32-wasi The Wasm module requires setup before it can be deployed to Docker Hub and ORAS container registries. Kubernetes can obtain Wasm images through this mechanism during its deployment process. Example: docker push myrepo/wasm-app:latest docker push myrepo/wasm-app:latest DEPLOYING THE WASMEDGE CONTAINER Now, you’re ready to deploy the Wasm-based container on Kubernetes. Create a deployment YAML file for Kubernetes to use: apiVersion: apps/v1
kind: Deployment
metadata:
  name: wasm-app
spec:
  replicas: 2
  selector:
    matchLabels:
      app: wasm-app
  template:
    metadata:
      labels:
        app: wasm-app
    spec:
      runtimeClassName: wasmedge
      containers:
        - name: wasm-container
          image: myrepo/wasm-app:latest
          command: ["/wasm-app.wasm"] apiVersion: apps/v1
kind: Deployment
metadata:
  name: wasm-app
spec:
  replicas: 2
  selector:
    matchLabels:
      app: wasm-app
  template:
    metadata:
      labels:
        app: wasm-app
    spec:
      runtimeClassName: wasmedge
      containers:
        - name: wasm-container
          image: myrepo/wasm-app:latest
          command: ["/wasm-app.wasm"] Now, apply this deployment file to Kubernetes: kubectl apply -f wasm-deployment.yaml kubectl apply -f wasm-deployment.yaml The WasmEdge container receives deployment through Kubernetes with two exact duplicates enabled. The deployment status can be checked by running this command: kubectl get pods -l app=wasm-app kubectl get pods -l app=wasm-app The above command provides a quick overview of the deployment status. RUNNING AND DEBUGGING WASM CONTAINERS Once deployed, it's imperative to verify that everything is working as intended. Let’s talk about some practical commands to monitor and troubleshoot your WasmEdge containers. VERIFYING THE DEPLOYMENT To check if your WasmEdge container is running, use: kubectl get pods -l app=wasm-app kubectl get pods -l app=wasm-app If everything is running in the right manner, this should show the pods associated with your deployment. To check the logs for a specific pod: kubectl logs <pod-name> kubectl logs <pod-name> TROUBLESHOOTING If you face any issues with your container, use the Kubectl describe pod to see detailed information: kubectl describe pod <pod-name> kubectl describe pod <pod-name> The command will show you the events related to the pod. It can help you debug issues such as image pull errors or misconfigurations. Additionally, WasmEdge provides a CLI tool to validate your Wasm module. To certify that your Wasm module is accurately built and ready to run, use wasmedge --validate my-wasm-app.wasm wasmedge --validate my-wasm-app.wasm This will check for any potential issues with the Wasm module before you deploy it. BENCHMARKING WASMEDGE VS. OCI CONTAINERS It’s important to compare the performance of WasmEdge containers with traditional OCI containers. Let’s look at a few performance benchmarks to see how WasmEdge performs. STARTUP TIME COMPARISON One of the most significant benefits of WasmEdge is the startup time. To evaluate the startup latency, run both WasmEdge and OCI containers using the time command: time docker run myrepo/wasm-app
time wasmedge run myrepo/wasm-app.wasm time docker run myrepo/wasm-app
time wasmedge run myrepo/wasm-app.wasm MEMORY & CPU USAGE To compare memory and CPU usage, use the kubectl top pods command: kubectl top pods kubectl top pods This will show you how much CPU and memory your containers are consuming. WasmEdge containers should consume far fewer resources than their OCI counterparts. For even more detailed profiling, use wasm-stats: wasm-stats my-wasm-app.wasm wasm-stats my-wasm-app.wasm It will help you understand how efficiently WasmEdge handles your workload. CONCLUSION WasmEdge offers users lightweight and secure container runtime functions. It replaces existing traditional OCI runtimes effectively. OCI runtime containers will become more effective when you migrate to WasmEdge because your applications will start faster and use fewer resources, while the sandbox security measures improve system safety. WasmEdge operates across multiple platforms. So, it means your containers can function on x86 devices as well as ARM and RISC-V devices without changing any configurations. The guide presents simple deployment and management instructions for WasmEdge containers on Kubernetes systems. WasmEdge proves to be an ideal solution for developers who create microservices and serverless applications as well as edge deployments.

Running Docker Containers on Kubernetes Without a Container Runtime Using WasmEdge

The tech industry is at the forefront of cutting-edge innovation. Everywhere you turn, someone's talking about disruption, transformation, or unlocking new possibilities – but the truth is, not every new tech trend aligns with actual user needs. And as product managers, we would do well to remember that the innovation should always serve the customer, not the trend. Last week, I was invited to share my experiences of leading product at companies working with emerging technologies like artificial intelligence (AI) and the new iteration of the internet focussed on data ownership (web3) on a Product People webinar. Using a few examples from my career, I argued for ignoring the hype and focussing on solving real user needs – aligned, of course, to business objectives – when introducing AI or web3 into the product. Product People webinar Understanding the temptation Understanding the temptation Of course, it's tempting to jump on the bandwagon. After all, if everyone is hyped up about something, shouting about how it's going to change the world, that energy can be contagious. Even more so in the tech sector, where we're all afraid of missing out on the next big thing. So whether it's accepting crypto or integrating AI tools, there's often pressure – from consumers, from leaders, from investors, hell, even from ourselves – to "do something with the trend". But here's the catch – just because we have access to a new technology doesn't mean it makes sense for our users. A cautionary example is the way businesses have been introducing AI chatbots. Sure, you can now say that you're AI-powered, but how is the experience for your typical user? Far too often, poorly executed chatbots frustrate users rather than helping. Instead of enhancing their interaction with your product, it can lead to friction as they struggle to get access to human support agents. In other words, adding AI just for the sake of it can end up alienating the very users it's meant to serve. It's not all about the tech It's not all about the tech This tends to happen because we get so caught up in the possibilities of the technology that we forget the most important thing, what problem are we solving? This is especially relevant when we talk about driving adoption of new technologies. technology what problem are we solving I was once on a panel with a woman who'd been an early adopter of Bitcoin and blockchain-based currencies more generally, and she drew a comparison with the success of Skype and no one caring about Voice over Internet Protocol (VoIP). The underlying technology is secondary. Its main purpose is helping you achieve your goals quickly and easily. Skype helped connect you to relatives across the world without paying exorbitant fees, that's the value. helping you achieve your goals quickly and easily that's The other interesting thing to highlight from my fellow panellist's observation is that, of course, it benefits from hindsight. We know (as we knew then) what problems Skype was solving, but we didn't know at the time whether the Skype protocol would win over the other variations of protocols being developed. I must admit that I don't know the history in detail, but I would hazard a guess that user experience had something to do with Skype's ultimate success. hindsight didn't In the same way, blockchain is just the facilitator[1]. Of course, there are significant advantages in terms of security and permanence, but ultimately, it's about the problem it's solving for users. And right now, it's lacking on the user experience side, though we have made great strides in improving this. I'm personally a firm believer that blockchain, crypto, NFTs, and especially AI do add value, but my overarching point is that we as product managers need to look past the technology and solve for users. have do Uncovering the "why" Uncovering the "why" As such, when it comes to our users, we need to get to know them so well that we can anticipate their needs and build solutions without worrying about the underlying technology. There's the often-quoted Henry Ford statement that if he had asked user what they wanted, they would've said a faster horse. While it's unlikely that he actually said this, not the least because he didn't actually invent the car, the quote conveys an important point about the user need state to (a) get somewhere faster and (b) to be able to afford it. Ford's invention, therefore, becomes the solution to a very specific problem – getting from point A to point B more efficiently. without worrying about the underlying technology Therefore, it's always a good idea to take a step back and ask a simple question: why? Why are we adding [insert feature here]? If you can't answer in a way that addresses a key business objective, whether that's increased revenue, improved retention, better engagement, or whatever else, through a better customer experience, it's time to reconsider. It's easy to get caught up in the hype and think that blockchain or AI or some other new technology is the magical silver bullet solution, but you can't lose sight of what you're trying to achieve – both as a business and as the product with the customer top-of-mind. why The benefits of web3 and AI The benefits of web3 and AI So what benefits are there in web3 and AI? Of course, we haven't yet reached the mass adoption of, say, Skype circa 2013, and it remains to be seen whether we will from a consumer side. From the consumer side, there's a disconnect between the promise (store of value) and the reality (speculation) of crypto, as well as the promise (owning IP) as well as the reality (again, speculation) of NFTs – so while the technology is innovative, the why hasn’t been fully addressed, at least not for consumers. But blockchain is already being used in government and business applications. Estonian and American government bodies, for example, are using the KSI blockchain for protecting privacy and security. Governments and big companies store value in bitcoin. are consumer side why is And then when we think about AI, it's more of the same. Maybe we haven't quite identified the overarching why of consumer AI, though in my personal opinion, it's just a matter of time before mass adoption for the simplification of mundane tasks (my mother regularly uses DeepL to check her English, neurodiverse acquaintances swear by ChatGPT for tackling simple emails that would otherwise take hours, etc). From a business perspective, however, AI is already embedded into our meeting software, into collaboration tools, and even into the core of software development through tools like GitHub Copilot, with more use cases added daily. why Knowing when to pivot Knowing when to pivot A few years ago, I was working with a company that encountered this very problem – it failed to find product market fit among consumers, but found it for businesses. This was a mission-driven crypto startup focussed on bringing Bitcoin to emerging markets as a hedge against inflation. We gamified the experience, rewarding users with small amounts of bitcoin for engaging with the platform. On paper, this seemed like a great way to build user engagement, but the reality was that this didn't support our business goals long term. consumers businesses Given the market and the industry, we had to do everything by the book – complying with regulation, receiving the necessary licenses, partnering with banks and authorities. This was not without its challenges, often financial. It quickly became apparent that our target audience wasn't able to sustain our growth ambitions, and while target users were using us to earn bitcoin, we weren't seeing enough traction in terms of transactions to make any money off the exchange. Now, I was young, a bit naïve, and so committed to the mission as central to the product – in other words, the technology and the mission became inextricably entwined for me – that I left when the CEO decided to pivot, leveraging all our banking relationships and licenses to actually improve the experience of transferring money to more complicated regions using blockchain rails and our user-friendly UI. But with the wisdom of hindsight, I can admit that this use case actually solves a problem and adds value for users, and, from a business perspective, it's a clever repackaging of our existing tech and partner stack to solve a different problem for a different set of users. These new users don't care that the money transfers are cheap and convenient because of blockchain, they just care that they're cheap, secure, and get the funds to a heavily regulated market compliantly. because When technology becomes the solution to a problem that doesn't exist When technology becomes the solution to a problem that doesn't exist But it doesn't always work that way. Sometimes, we want to work the technology into the solution so much that the problem we're solving for users is forgotten. In another company I worked for, the founding mission was the ability to register Certificates of Authenticity for physical artworks on the blockchain as a permanent record, which matters to artists who want to track provenance. Blockchain was seen as a solution to the question of permanence, as well as security. The issue was adoption – this is an issue for the art world generally, why there isn't a single database of Certificates of Authenticity is that each artist, gallery, museum or whatever it may be issues their own, which makes it difficult to track and authenticate. So while there was a use case, the same issue pursued the startup, because in order for these Certificates to continue to exist as intended to, they needed to be transferred alongside the artwork. This was then complicated by the emergence of NFTs and our company's entry into the space, because NFTs were intended, first and foremost, to serve as proof of ownership in and of themselves – you own the NFT, therefore you own the underlying art, and any transactions are visible on the blockchain, therefore establishing provenance. However, instead of doubling down on the use case and pursuing the vision of establishing an immutable repository of Certificates, we focussed on the technology, and tried to certify NFTs alongside physical works. The logic being that NFTs can be faked, because it's easy to make an NFT with an underlying image that isn't your artwork – though, of course, NFTs track which address created the artwork, so the artists for whom this matters can prove or disprove authorship anyway. So how does this all reflect on the problem we were trying to solve? Well, the issue is that the decisions we were making (pursuing NFTs in parallel to certification) were contradicting the why of what we were doing (establishing authenticity of certification). And, of course, the big one – we were solving a very niche problem in a very complicated way, which prevented us from driving adoption by focussing on simplifying user needs. The main takeaway being, of course, that before getting too carried away with how to build the solution, determine whether the problem being solved matches your business objectives. simplifying Build or buy? Build or buy? To top it all off, there's the additional consideration of whether to build the solution yourself or to delegate the work to a third party. This becomes especially relevant when it concerns AI, because ownership of the underlying data model is a competitive advantage. In my current organisation, we have a number of products that are introducing AI-powered solutions that address specific user need states, such as manual and labour-intensive purchase invoicing. This kind of repetitive process with clear parameters is ripe for AI automation. Why AI? Because it needs to account for nuance in business operations, which is why attempting to address it with rule-based processing is not sufficient. But this kind of solution comes with additional considerations of tradeoffs (time-to-market versus long-term value, as well as who builds it and who handles the legal questions like data processing), all within the context of the breakneck pace of AI. nuance In Silverbucket, the product I am currently working on, there are many areas in which we can improve usability through AI, both internally and customer-facing. The important thing is to recognise what is actually relevant for us – for example, there's no need for us to screen "easy" customer service queries trained on our knowledge base, because we don't typically get any queries that don't require engineering. This then paves the way for a more comprehensive question of how to build AI assistants for our customers, because a simple AI chatbot for the sake of it is unnecessary. The main question that we need to answer when we think about how our solution addresses user needs is, how do we facilitate the user's intended result in the easiest way possible? As you can see, that last question doesn't say anything about AI – but in our day and age, this can very well end up being the answer, which is something that we're therefore obligated to explore as a potential (but not only) solution. how do we facilitate the user's intended result in the easiest way possible? Conclusion Conclusion The main takeaway is that companies need to solve real pain points, and the technology is just there as a facilitator. If we use it correctly, our users benefit; if we don't, we risk alienating them. The only thing we can really rely on is continuous discovery – watching users, speaking to them, looking at product analytics, exploring their need states, and understanding how to solve their problems, and this can be with or without web3, blockchain, AI or whatever new technology appears on the horizon. [1] I do want to sound a note of caution here, because AI is a bit of a different beast, the inputs do matter, because they can replicate existing inequalities or spread misinformation, but that's somewhat of a different conversation. do

Stop Jumping on Every Tech Trend—Real Innovation Always Puts Users First

Stories, News & Mentions about CERTIFYOS

Trending Topics

Classic

Neon Noir

Minty

Newspaper

HN StartUps