Raj Murtinty

“This greatly enhances the capability of DFA’s existing network and gives us the confidence that we can meet the growing digital demands of our customers,” said Andreas Uys, Chief Technology Officer at MAZIV, DFA’s parent company. “We can seamlessly onboard next-generation connectivity services in selected regions before scaling them nationwide.” https://lnkd.in/dwgS3bQS

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Customers often ask how we manage the physical side of Extreme IX - ~50 POPs across India, close to 100 racks, and thousands of devices, cables, cross-connects, fiber pairs, power feeds, etc. It’s a fair question. Things move fast here. Racks fill up, circuits change, new devices are deployed every week. Keeping it all in your head, or scattered across spreadsheets, is a recipe for pain and a brewing disaster... That’s why we use NetBox. It’s our source of truth for everything in the network. Every site. Every rack. Every device, port, cable, and power feed. Every fiber pair and vendor cross-connect. Every server, virtual machine, IP address, Vlan. If it exists in our infrastructure, it exists in NetBox. Over time, it’s grown from being “just documentation” into something far more powerful - a living map of how Extreme IX is built. We track dark fiber routes, vendor details, and contract information, so we always know who provides which link, where it runs, and what connects on each end. Our monitoring systems pull device data directly from NetBox. So when an alert triggers, it doesn’t just say what’s down - it tells us where it lives, what it connects to, and who to call. That context changes everything. It turns a 20-minute investigation into a 2-minute fix. We’ve even mapped power distribution across every rack: how each PDU is wired, which breaker feeds what, and how redundancy is maintained. If a feed fails, we already know the impact. The result: - Faster troubleshooting because every alert has context. - Better planning because we can see capacity and dependencies clearly. - More confidence because our documentation isn’t an afterthought — it is the system. We don’t treat infrastructure knowledge as tribal wisdom that lives in people’s heads. We treat it as structured data that lives in one reliable place. That’s what NetBox gives us: a calm, accurate picture of a complex system. It’s how we manage scale without losing control. And btw Netbox is open source and has a rapid development and a vibrant community. Here’s a glimpse of what that looks like 👇 Would love to hear if some of the ISPs in India are also using Netbox or plan to use it?

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Every base station is already a sensor array, a signal processor, and an AI inference engine. Most operators have no way to use any of it. That’s the problem RANIQ solves — not by adding hardware, not by bolting analytics onto a management layer, but by integrating at the right depth, from the inside out. Today we’re launching RANIQ: ODC’s Unified Edge Intelligence Platform for AI-native RAN — built on @NVIDIA AI Aerial platform. What it does: — Turns every base station into a programmable RF sensor node — Exposes real-time signal intelligence across L1–L3 through the RANIQ SDK — Runs co-resident on existing DU/CU compute — no new hardware — Enables RF sensing, distributed ISR, real-time ML inference and ISAC Full technical write-up in the article below ↓   #5G #OpenRAN #EdgeAI #ISAC #Telecom #NetworkIntelligence #PrivateNetworks #DefenseTech

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https://lnkd.in/gf9PRggT 🚀 Understanding the Hidden Impact of Onboarding Failures Many product launches face a silent setback: customers who try but then disappear. Months of development can be lost in a confusing first experience, impacting revenue and growth. Building well-crafted onboarding strategies turns initial confusion into a structured plan for success. 💡 Key Insight Effective onboarding can boost activation and willingness to pay by Between 12 and 21. Most users delete apps because they do not understand how to use them. Clear, helpful first impressions help retain 86 of customers. 🔍 How to Evaluate Onboarding Solutions Use tools with built-in analytics, segmentation, and targeted messaging. Look for features that measure behavior, automate engagement, and integrate knowledge bases. Invest in platforms that provide measurable impact and continuous improvement capabilities. ✅ Key Recommendations - Simplify first-run guidance to reduce churn. - Use checklist-driven flows for high completion rates. - Select platforms with analytics and feedback loops. - Deliver personalized guidance to increase engagement and revenue. - Before purchasing, evaluate pricing, security, and review real-world case studies. 🎯 Who Should Use This Guidance Founders, product teams, growth managers, and customer success leaders aiming to improve activation and adoption. Teams deciding whether to build or buy will find clear criteria for fast deployment with minimal engineering. Larger enterprises benefit from governance, integrations, and centralized insights. 🔑 Critical Success Factors - First-time clarity shortens the path to value and reduces drop-offs. - Checklists and guided flows improve notable adoption. - Platforms that analyze actions and enable automation yield continuous improvements. - Personalization elevates engagement levels and customer willingness to invest. 📊 Why Onboarding Matters for Long-Term Success Early user guidance influences long-term loyalty through faster time to value. Shortening the activation journey increases feature adoption, retention, and revenue growth. Teams that track these metrics explicitly can see direct benefits such as increased customer expansion and higher engagement. 🚧 Recognizing User Drop-offs and Confusion Analytics reveal where users stall — signups, first steps, or feature underuse — guiding targeted interventions. Many users delete because they do not know how to proceed, making interactive checklists and contextual guidance essential. 🤖 Modern Onboarding Technologies 🛠️ Integrating AI-driven guidance, behavioral analytics, and targeted messaging creates a connected growth engine. This stack continuously interprets signals, adjusts paths, and personalizes experiences at scale. Significant components include guidance platforms, analytics tools, and communication channels.

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Thank you Nitin Dahad and EE Times | Electronic Engineering Times for covering zeroRISC's $10M seed funding round. This article conveys the opportunity to put supply chain integrity on firm footing with ZeroRISC's integrity management technology and open source silicon. "ZeroRISC said in its latest announcement that it is expanding access to production-grade, OpenTitan-based designs suitable for every stratum of the silicon supply chain, from IoT to data centers. Rizzo told EE Times that the most valuable and quickest opportunities would be in IoT and chiplets, and that automotive could be a real opportunity too...A key part of ZeroRISC’s long term revenue model is to provide “identity-as-a-service” as opposed to simply being a silicon provider. For this, its Integrity Management Platform decouples device security from place of manufacture, scaling ownership and control with software rather than relying on physical possession. Device consumers can define security policies suited to their operational needs rather than relying on operating system developers or manufacturers to impose ill-fitting controls." Read more here: https://lnkd.in/et-2YdM7 

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As part of the IndiaAI Mission, Avataar is building UAI (Unified AI Interface) - a foundational DPI layer with thin, durable rails for trust and interoperability, connecting models, data and compute into a cohesive, discoverable ecosystem. We are privileged to collaborate with Prof. Ramesh Raskar (MIT) and Project NANDA: Architecting the Internet of AI Agents in shaping a broader “Internet of AI Agents” vision - exploring how a discovery layer for India’s evolving digital public infrastructure can unlock openness, innovation and responsible scale together. Equally grateful to Aafreen Siddiqui Sherwani (United Nations) for anchoring the dialogue, and to Pradeep Gupta (NVIDIA), Tanvi Lall (EkStep Foundation), Vineet Singh (Digital Green) and Jibu Elias (Mozilla Foundation) - for a thoughtful, forward-looking exchange on what it truly takes to build open AI platforms for India. The real opportunity ahead is not just technical, it is architectural. How we design these rails now will shape how intelligence compounds across citizens, enterprises and governance systems for decades. What principles should guide the design of AI infrastructure at national scale? #IndiaAI #DigitalPublicInfrastructure #ViksitBharat

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AI tools have started making waves in chip design, and it's exciting to see how they can enhance productivity. While large language models like Claude are proving beneficial in RTL coding and verification, there's a growing array of new tools emerging from both established EDA vendors and numerous startups. With the current talent crunch in the chip development space, the introduction of these innovative tools could be key to improving team efficiency without compromising quality. If you are utilizing any of these AI tools and have experienced significant productivity improvements, please share your insights. Looking forward to a lively discussion on this topic. Let's get it started.

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Oru'el's primary traction came in December when I met Pratyush Kumar of Sarvam at an event. "More compute without adding more GPUs, how?" he asked and we strolled off the event for 20 odd minutes. I explained to him what happens BTS, how does 'predciting' GPU failures affect this and how Foundational AI companies, and ultimately users, suffer. The sad part is, this is not something anyone could have controlled. It's really hard to predict as well. Grey failures is one of the biggest problems with GPUs and as quoted by Meta, 58% of their training run failures during Llama 3 were GPU related! Last week, Ajith and Nihith finalised the models and the architecture for our MVP. It outperforms what Bytedance did by a margin. Tho, there's no formal benchmark that exists, we would soon make one. Every SRE we have talked to told us the same 3 things : - We don't know when it would fail - We don't know why it would fail - We don't know what is the cause of failure until thorough post-mortem And since engineers don't really care about CapEx, the billions lost monthly across the world is just another headache. Oru'el's system works on pinpointing failures, telling you particular GPU about to fail, the cause of it and how much time is left to act with an actionable that they can swiftly follow. Latency? sub-milisecond. This is an SRE's dream come true. Predicting GPU failures be it for training or inference not only optimises the work flow but also calls for maintenance -- not on scheduled calendar but when you need it. This also tells you the 'health' of a GPU in real time, treating Silicon like a living being. Oru'el will be piloting soon and we have got our first prospects to work with. Everytime we cold called and talked about this to the decision makers, we never got a 'no' and that's a sign you know this industry needs physics to solve a physics problem -- not RL slapped on stats. oru-el.com

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https://lnkd.in/gT_UttPT Wondering what you think of this.  My take is that it’s worth delaying the launch if it leads to significantly new or improved capabilities.  We’re in the “rush our AI to market NOW!” panic phase of the market.  But key questions are:  1) What does the new version do for consumers or businesses that they couldn’t do with the current version?  2) What business advantages does the new version provide?  Does it help sell related services?  Does it create preference for using their company platforms - e.g., in Meta’s case, does it increase Facebook usage, or preference for Instagram over TikTok?   If waiting for important features to be developed provides tangible advantages, then it's likely better to wait.  From my experience as product management leader, while some in the company will be disappointed that they couldn’t announce ahead of rivals, having real meat in the announcement ultimately proves its value.  Concerns with AI around accuracy and privacy are already building – at least deliver tangible benefits and value with a new release.  What’s your take? #productmanagement #AI #productstrategy #releasestrategy

In today’s world of massive, complex chips powering AI and high-performance computing, the industry is embracing a deeper “shift left” approach while ensuring no change in later stages re-introduced gaps in the netlist phase. 🚀 This blog explores 5 key trends in RTL signoff that are transforming the design process: 1️⃣ Advanced linting for highest design quality 2️⃣ Advanced Clock and glitch checks to prevent silicon bug escapes 3️⃣ Incremental Netlist CDC signoff complemented by correct-by-construction Synthesis 4️⃣ Cleaner design with cycle-aware RDC 5️⃣ Smarter Debugging of synthesis-optimized registers 📖 Read the full blog to dive deeper into these trends and see how they’re reshaping chip design #RTLSignoff #ShiftLeft #ChipDesign #AI #HPC #EDA #Semiconductors #DesignVerification #CDC #RDC #Linting #RTL #FirstPassSilicon #VCSpyGlass #Synopsys Paras Jain Ajay Singh Amit Goldie Vikas Kamboj Bradley Geden Sanjay Bali sureshbabu barla

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Indian telecom operators need to recognize the significance of this move and the critical role Internet Exchange Points (IXPs) play in efficient content delivery and traffic exchange.

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