MAHESH YADAV

This morning’s deep dive into Quality of Service (QoS) was a clear reminder of how essential it is to maintain performance and reliability in networked systems. what i deducted · QoS isn’t just about bandwidth—it’s about intelligently prioritizing traffic to ensure critical applications run smoothly, especially under load. · Mechanisms like traffic classification, queuing, and policing help manage delay, jitter, and packet loss. · Without QoS, voice, video, and real-time data can degrade, impacting user experience and business operations. In today’s hybrid and cloud-heavy environments, implementing thoughtful QoS policies is no longer optional—it’s foundational for consistent service delivery. What strategies are you using to manage network performance? #QualityOfService #QoS #Networking #ITInfrastructure #NetworkManagement #TechLearning

1

STOP ANALYZING: Decision Paralysis Is a Structural Flaw The real cost of indecision isn't time—it's the structural chaos that keeps high-performers stuck. You aren't facing a dilemma; you're facing an Editing Problem. My methodology, The Author's Blueprint, is rooted in the honor of structure. We eliminate indecision not by adding data, but by applying a rigid, non-negotiable filter. The Decisive Action Filter (The 3-Step Edit) To move past analysis and into principled action: 1. Define the Final Chapter Title: Identify the one absolute non-negotiable goal for the next 12 months. 2. Test for Alignment: Does every option you are considering make that Title non-negotiable, or does it complicate it? 3. Execute The Editor’s Cut: Ruthlessly discard anything that complicates the narrative. The remaining choice is your Decisive Action. Discipline is the greatest accelerator. The honor of a clear structure is the only thing that separates true execution from perpetual analysis. Read the full blog post on the structural root of indecision here: https://lnkd.in/dqNw3Hhu #TheClarityCoach #DecisiveAction #AuthorsBlueprint #Strategy #Execution

2

T-Mobile just set a world record for uplink speeds in sub-6 GHz using cutting-edge 5G Advanced (Release 17) technology!  With support from our partners at Nokia and MediaTek, our engineers achieved 550 Mbps uplink on a live commercial site extending our lead in wireless innovation. 💪  The future isn’t just about fast downloads — it’s about real-time, responsive experiences. From #XR to #AI to cloud-driven interaction, the next era demands uplink speed and reliability. And T-Mobile is building the network to power it!  https://lnkd.in/gYWpVpnn

313

4 Comments

How does an AI coding tool remain competitive in an overcrowded market? 🤖🖥️ The GPT-5-Codex upgrade introduces dynamic thinking capabilities that adjust processing time based on task complexity, delivering optimized results no matter the challenge. From surpassing coding benchmarks like SWE-bench Verified to excelling in code-refactoring and reviews, the model consistently outshines its predecessors. Engineers have praised its ability to offer fewer incorrect comments and more impactful insights, making it a valuable addition to the competitive AI coding market—where solutions like Cursor and GitHub Copilot are also advancing rapidly. Could dynamic AI models like this redefine software development workflows? Share your thoughts! 💭 #AI #SoftwareEngineering #CodingTools #DynamicThinking.

2

1 Comment

I was honored to deliver a keynote at this year’s Marconi Society event today at UCLA. I opened with how control (AI), compute, and connectivity technologies each began in the 1980s as monolithic, centralized, and specialized silos, gradually evolving over decades toward distributed, composable fabrics. Today, they are undergoing a deeper transformation toward “distributed convergence,” where these three layers increasingly operate as a "distributed, composable, integrated, and intelligent fabric". Control (AI/ML): symbolic, rule-based AI → neural networks → deep learning → foundational models/LLMs → multi-agent systems Compute: mainframes → PCs → client/server → virtualization → central cloud → distributed cloud (edge) Connectivity: 1G–2G (circuit-switched) → 3G (hybrid) → 4G (packet-switched) → 5G (cloud-native) → toward 6G (AI-native) The next decade will be defined by distributed convergence, where control (AI), compute, and connectivity function as one cohesive system. This is the essence of the AI-native era. Building on this foundation, I reflected on the connectivity progression: We advanced from circuit-switched 2G, to IP-native 4G that unified transport for voice, video, and data, and then to cloud-native 5G, which transformed networks into programmable platforms for distributed workloads. We are now entering the AI-native era, where a new class of devices and applications demands a new class of networks, aligned to the dominant unit of computation: first packets, then workloads, now agents. This set the stage for the architectural continuum I discussed: - Packets move bits - Workloads run services - Agents pursue goals These layers are additive, not replacements. AI-native systems continue to rely on packet-switched substrates and cloud-native orchestration, while adding agentic and semantic capabilities on top. I framed this evolution as a progression from programmability → orchestrability → autonomy → sustainability, guided by new architectural constructs for AI-native networks , including a dual-stack model: today’s syntactic Internet alongside an emerging semantic Internet. My sincere thanks to my colleague, Sheryl Genco, Ph.D. and the Marconi Society for the invitation and for its leadership in bringing the community together to advance the future of connectivity for societal impact.

119

9 Comments

This is true. I see Telco would need to reshape their Observability Framework to collect their own sources of data from their network to train their own models not to buy AI solutions, but create AI platform instead and build their own models on top. I do see they are jumping to multi Agents frameworks without understanding that the most important to have reliable outcome with most cost effective is to create a foundation for AI, not all about the Agents.

11

Thank you, Built In, for publishing my op-ed: “AI Is Already Thinking For Us.” The threat isn’t that AI will outthink us — it’s that it will make us stop thinking at all. Large language models don’t create; they predict. And when prediction replaces reflection, originality disappears. Maybe the most radical act today isn’t rejecting AI — but using it after we’ve thought for ourselves. #AI #Ethics #FutureOfWork #Creativity #HumanAgency #OriginalThinking

8

1 Comment

Google DeepMind Researchers Introduce Evo-Memory Benchmark and ReMem Framework for Experience Reuse in LLM Agents Evo Memory introduces a streaming benchmark and agent framework that tests whether LLM agents can actually learn from continuous task streams by reusing and refining past experience, not just recalling dialogue history. It restructures existing math, QA, tool use and interactive environments into sequential tasks, then compares memory modules like ExpRAG and the ReMem action, think, refine memory loop, showing consistent gains in exact match, success and step efficiency without retraining the base models.

3

From startups to enterprise-scale builders, the AI House community is shaping the next wave of breakthroughs in the Pacific Northwest. Their teams rely on fast, scalable, secure connectivity to train models, host live events, and support AI‑driven workloads. That’s where Lumen Technologies comes in.   With ultra‑low latency, on‑demand bandwidth, and security built in, Lumen is helping AI House and Seattle’s growing AI ecosystem push past traditional limits and build what’s next.⚡   📖 Read the full story to see how this partnership is unlocking new possibilities for innovators across the region: https://bit.ly/4jP7y6h   #PartnerProgram #SolvedWithLumen #TrustedNetwork

2

At NVIDIA GTC 2026, Jensen Huang made something subtle but profound clear: the AI infrastructure race is no longer measured only in FLOPS or GPUs — it’s becoming a token economy. In the AI era, data centers are optimizing around three new core metrics: • Tokens per Watt – how efficiently infrastructure converts power into AI inference throughput • Cost per Token – the economic unit of AI, driven by platform advances from systems like Blackwell and the upcoming Vera Rubin architecture • Tokens per Second per User (TPS/User) – the new user-experience metric for AI responsiveness and real-time interaction This is fundamentally different from the traditional mobile operator economy, where networks evolved around: • Gbps capacity • Cost per GB transported • Latency and throughput as the user experience benchmark In simple terms: Telecom optimized for moving bits. AI infrastructure optimizes for generating intelligence. How telecom can benefit from the AI infrastructure economy paradigm.. Not all the bits on network are equal in value, yet they are charged the same. Will these two worlds converge As AI inference increasingly moves closer to the edge — integrated with telco clouds, RAN edge sites, and distributed compute — mobile operators may need to evolve from a “Gbps economy” to a hybrid “Gbps + Tokens economy.” That shift introduces new strategic questions: • How many tokens/sec can be served at the edge per cell site or metro edge? • What is the cost per AI interaction over a mobile network? • Can operators monetize AI responsiveness the same way they monetized bandwidth? The next phase of telecom infrastructure may not just deliver connectivity — it may deliver real-time intelligence as a service. And if that happens, the network is no longer just transporting data. It becomes part of the AI inference supply chain. #AIInfrastructure #GTC2026 #NVIDIA #Telecom #EdgeAI #AgenticAI

21

2 Comments