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Your product can have a world-class cloud stack and a blazing-fast 5G link, then lose the whole experience in the last half meter. That’s the “last meter” problem, and it’s why we sat down with Dr. Frederic Nabki Co-founder and Chief Technology Officer Spark Microsystems, to talk about ultra-wideband wireless that targets wirelike responsiveness instead of “good enough” latency.

We dig into where Bluetooth and Wi‑Fi still shine and where they hit real limitations for deterministic wireless, ultra-low latency, and interference-heavy environments. Frederick explains why Spark’s approach uses impulse radio UWB, how sub-nanosecond-scale pulses change the game for multipath and coexistence, and how wide UWB spectrum enables frequency agility when the airwaves get crowded. If you’ve ever been in a trade show hall where microphones and earbuds fall apart, you’ll recognize why interference robustness is no longer optional for industrial IoT, medical devices, wearables, and robotics.

The examples get concrete: a gaming mouse that targets about 150 microseconds end-to-end latency, robots that need fast control loops to avoid collisions, and brain-computer interface systems where cables create infection risk and power budgets are unforgiving. We also cover Spark’s go-to-market details, including transceiver silicon, an SDK, reference designs, antenna guidance for FR4 PCBs, and why modules can simplify certification.

If you care about ultra-wideband, UWB data communication, ultra-low power wireless, and real-time connectivity, hit play, then subscribe, share the episode, and leave a review so more builders can find it.

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Your carrier has more data than almost any company you interact with, yet most telcos still struggle to turn that advantage into growth. We sit down with Miguel Carames, the Chief Product Officer at Mobileum to sort out what’s real, what’s next, and what’s pure hype when it comes to AI in telecom, 5G monetization, and the future of operators as intelligence-driven businesses. Along the way, we get honest about why “we invested billions” doesn’t automatically translate to new revenue and why regulation and privacy expectations reshape every AI roadmap.

We also challenge the idea that AI only arrived with generative tools. Telecom has used machine learning for years in automation, anomaly detection, and capacity planning, but the story hasn’t been told well. Miguel shares concrete, production-minded examples: using LLM-style interfaces to make deeply technical testing platforms usable for roaming managers and analysts, moving toward automated root cause analysis, and deploying agent workflows in fraud and revenue assurance so cases arrive pre-analyzed with evidence and a human still making the final call.

From there we go into customer experience, where proactive network intelligence can prevent tickets before customers ever feel the pain, and into churn reduction, where the opportunity is huge but the privacy line is delicate. We wrap with fraud and security, the whack-a-mole reality of bad actors, and what it takes to escape pilot purgatory so telecom can move at AI speed. If you found this useful, subscribe, share it with a telecom leader, and leave a review. What’s the best AI use case you’ve seen a telco actually scale?

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If your SOC is buried under alert noise, another flashy AI demo won’t save you. We go deeper into what actually works: starting with data strategy and detection quality so automation has real signal to work with, not chaos to summarize. Our guest CEO and Founder Karthik Kannan from Anvilogic explains what “agentic SecOps” looks like in practice, from data onboarding and normalization to detection engineering, hunting, triage, investigation, and the integrations that move outcomes into your ticketing or case management systems.

We talk through why many AI security operations tools jump straight to alert triage and why that can turn into a band aid. The more durable path is end-to-end context: knowing exactly which data sources fed a detection, what logic fired, and how the alert was produced. That lineage supports higher accuracy, cleaner investigations, and consistent mapping to frameworks like MITRE ATT&CK. We also dig into “show your work” explainability, why black box answers stall adoption, and how a decision trace helps teams build trust step by step.

On the architecture side, we explore federated security operations across the tools enterprises already run, including Splunk, Microsoft Sentinel, Snowflake, and Databricks. Instead of forcing every byte into a monolithic SIEM, federated queries and data lake strategies let teams correlate where the data lives while controlling cost and complexity. We close with a grounded take on whether AI replaces security analysts and why the real win is reducing burnout and up-leveling people into higher judgment work.

If this helped you rethink SOC automation, subscribe, share the episode with your team, and leave a review with the biggest bottleneck you want AI to tackle next.

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