John Casey

Texas is quietly becoming America’s Fiber Triangle. Dallas, Austin, San Antonio, and Houston (the “DASH” corridor) now anchor one of the nation’s densest low-latency, high-redundancy fiber networks, sitting right on top of major shale corridors and AI-era energy hubs. At FrontierGen, we call this “barrels to megabits” - the convergence of power, gas, water, and fiber that’s redefining where the next generation of hyperscale campuses will be built. 🔹 Dallas–Fort Worth: interconnect gravity (150+ carriers) 🔹 Austin–San Antonio: hyperscaler expansion corridor, LatAm Gateway 🔹 Houston: energy + fiber backbone Texas isn’t just connecting data — it’s powering the future. Read More: https://lnkd.in/ePARDAGp #AI #Infrastructure #DataCenters #Texas #Fiber #InfrastructureNexus #SouthTexas #Permianbasin #naturalgas #DigitalInfrastructure #Poweredland #ERCOT #transmission #Fiberoptics

102

6 Comments

Legend Communities has broken ground on the Square at Lakeway, which will become a central location for community events. Working with dozens of Industry veterans in the Lake Travis community, Legend is engineering the INTELLIGENT INFRASTRUCTURE into the development. With micro data centers to support computing moving into local communities to support AI, protecting our data, and enabling advanced services in public safety, transportation, economic development, and education. The Lake Travis region is embracing Intelligent Infrastructure Economic Zones to support the Digital and Autonomous World. INTELLIGENT INFRASTRUCTURE will touch the day-to-day life of our fellow citizens and is crucial to our country's overall competitiveness and prosperity. https://lnkd.in/gHQwDwdD INTELLIGENT INFRASTRUCTURE enables AI and Autonomy 2.0 for our students, inspiring tomorrow's innovators, leaders, pioneers, and builders. https://lnkd.in/gmRHV6uX

35

New release from Packt Publishing… “Building Business-Ready Generative #AI Systems — Build Human-Centered Generative AI Systems with Context-Aware Agents, Memory, and LLMs for the Enterprise” See it at https://amzn.to/3Jdcio5 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🔵Build an adaptive, context-aware AI controller with advanced memory strategies 🟢Enhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT) 🟠Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fit

14

3 Comments

"For example, Google recently removed its net-zero pledge from its website, positioning AI as a tool to fight climate change while sidestepping the reality that AI itself consumes massive amounts of electricity and water." https://lnkd.in/eyMuTWi8

6

Faster Salesforce releases in the public sector start with better testing. In this webinar, Zachary Bayer, Stefan Mohapp, and Evan Bartlik walk through a practical, phased framework for moving from manual UAT to scalable, low-code automation—built specifically for public sector teams that need speed without sacrificing compliance or control. The session covers how agencies are modernizing testing, reducing manual effort, and building confidence into every release without taking on a heavy lift. If you’re still relying on spreadsheets or manual testing to get Salesforce releases out the door, this is worth a watch. Watch on YouTube: https://ow.ly/J8xN50Y5CcX Explore more resources: https://ow.ly/ZWpC50Y5CcY #PublicSector #SalesforceTesting #SalesforceDevOps #Copado

19

Why should Active Metadata Tags be the backbone of your AI strategy? Active Metadata Tags (AMTs) are used for automatic enrichment of metadata. Euno supports two types of custom metadata enrichment capabilities, both of which generate AMTs: #1 Extract structured metadata from unstructured metadata. This involved parsing metadata from elements like code and descriptions. A common application would be to enforce data modeling guardrails. Examples: → Implicit columns schema: Analyze dbt model code to determine whether the resulting schema includes implicit or explicit columns. → Verification checks: Assess naming conventions, detect use of approximate count distinct when querying large tables, evaluate documentation agility, flag full outer joins, and more. #2 Compose new AMTs. This approach combines metadata of a resource, and of any related resources, to set a new property on a resource. Examples: → Non-trivial custom SQL: Tag Tableau Custom SQL nodes that do not add logic. → Certified data source: Tag Tableau data sources as ‘certified’ if they don’t contain non-trivial custom SQL. → Certified dashboard: Tag Tableau dashboards as ‘certified’ if they only depend on certified data sources.   This capability is essential for governance workflows in large organizations where different teams maintain different parts of the stack. So team AI can build on something that team B created. Consider this scenario: → The team maintaining dbt can create a custom property to flag models for deprecation (e.g., models lacking both ‘prod’ and ‘prod_ready’ tags). → Analytics teams can then use this signal to create custom properties for their BI assets to drive workflows to migrate away from those models. Why does this also matter for AI agents? By implementing AMTs to surface trust signals, AI agents can actually tell the difference between exploratory work and official definitions. Example: → Euno’s MCP server surfaces to AI agents only Tableau logic that meets your trust standard and is considered certified such as: ‘depends only on dbt models from the gold layer and was in use in the past 60 days.’ *** Expect to hear much more about Active Metadata Tags! They’re quickly becoming the key to scale AI beyond the sandbox in your organization. #ActiveMetadata

17

1 Comment

Google just unveiled Gemini 3, their most intelligent and advanced model yet. It delivers state-of-the-art reasoning, deeper context understanding, and the ability to interpret even the most nuanced ideas with minimal prompting. Gemini 3 is rolling out across Search (on day one), the Gemini app, AI Studio, Google’s new agentic development platform Antigravity, and enterprise ecosystems like Vertex AI and Gemini Enterprise. In just two years, AI has gone from reading text and images to literally reading the room. Exciting times ahead for builders, innovators, and enterprises! GWC Data.AI a proud Google Cloud partner.

47

Infrastructure is finally catching up to the speed of GPU innovation. John Mao breaks down the launch of the CNode-X server—adding GPUs directly into the VAST-certified hardware stack to collapse architectures and accelerate SQL and vector search like never before. See how VAST is resetting the performance bar for the inference era👇

128

5 Comments

#Quantum #batteries are redefining #energy storage with ultra-fast charging and quantum dot innovations. Global leaders, from #MIT research labs to companies like #Quantinuum and #Zapata, all these innovation leads are pushing the boundaries of #sustainable #power systems. News Link- https://lnkd.in/g7U5Eef7 https://lnkd.in/gKipKEv6 #cleanenergy #energy #tech #technology #quantum #batteries

7

1 Comment

The #GDAL CLI Modernization webinar video is now live. Learn about GDALG pipelines, shell completion, the new `gdal vsi` command, and migration of stalwart Python tools like gdal_calc.py to the base library. https://lnkd.in/dXBfWbec Slides are at https://lnkd.in/daE28gS5

184

5 Comments

From my upcoming book: AI-Powered Digital Twins (Wiley 2026) The technical hurdles for large scale AI deployments are mostly related to security, interoperability, and reliability. We address those throughout the book. Given enough investment and brain power, technical hurdles are bound to melt away. Human hurdles, on the other hand, need to be acknowledged and addressed early on, since no amount of technical prowess can overcome the human element, and it shouldn’t, since an AI-powered digital twin is built by humans and in service of humans. Thankfully, human hurdles, once identified, are not insurmountable. Any strategy for an AI-powered digital twin deployment must prioritize the human side: * Human legacy * Fear of the unknown * Intimidation due to lack of technical expertise * Lack of alignment between top down and bottom up * Limited human resources * Limited financial resources * Territory * Large organizational culture * Human Error Photo: Hirshhorn, DC

58

8 Comments

Epic Games' latest AI character—now facing a SAG-AFTRA complaint—shows why AI requires both creative chaos AND thoughtful boundaries. The brilliance of AI development lies in pushing limits, but sustainable innovation demands guardrails around training data acquisition and quality. Epic's situation demonstrates that everyone needs ethical frameworks. AI isn't just about what's possible, it's about what's responsible. The right guardrails don't stifle innovation; they channel it productively and protect it from potential backlash. https://lnkd.in/g8sVvACW #AIInnovation #ResponsibleDisruption #EthicalGuardrails

16

🚄 Faster engines. Same old tracks. Amtrak’s new Acelas can touch 160 mph—but early schedules are slower because the constraint isn’t the train, it’s the century-old corridor: shared tracks, signals, switches, power. Peak speed ≠ average speed. Real-time AI has the same trap. We keep buying faster “engines” (GPUs, bigger models) while our “tracks” (data pipelines, messaging, storage, governance) are still batchy, noisy, and brittle. The result: impressive demos, sluggish decisions. A mental model I use: Top speed = model throughput Track geometry = data architecture & schemas Signals = governance, security, approvals Dwell time = handoffs, queues, ETL windows Shared right-of-way = contention with legacy systems What actually compounds value is not tokens/sec—it’s time-to-decision and tail latency. If one segment in the loop is stuck at “yesterday,” the whole system runs at yesterday’s pace. Three questions I ask teams (and myself): 1. Where is our true critical path from event → decision → action? 2. What creates tail latency (the 99th percentile) and how do we redesign around it? 3. If we couldn’t upgrade the model for a year, what infrastructure changes would still cut end-to-end latency in half? New engines are exciting. But the winners in AI will be the ones who quietly rebuild the tracks. https://lnkd.in/eXUqFW3t

33

2 Comments

California-based Poseidon Aerospace has raised $11 million in seed funding to build large-scale unmanned cargo aircraft. Poseidon is developing two cargo-hauling unmanned aircraft, "Egret" and "Heron," designed to carry up to 2 tons of supplies like food, medicine, and ammunition. The company aims to rethink the legacy air freight industry from the ground up by making unmanned logistics practical for both defense and commercial supply chains. The round was led by Tamarack Global, with participation from Drover Ventures, Draper Associates, and Starship Ventures. The funds will be used to develop the two unmanned aircraft, with flight testing expected to begin in 2026. Congratulations to CEO David Zagaynov and the Poseidon Aerospace team. Axios: https://lnkd.in/dwJF--kA Deals like this are reshaping the AI landscape. For weekly analysis on the funding, valuations, and strategic moves that matter, subscribe to the Charted Growth newsletter: chartedgrowth.beehiiv.com #Drones #Logistics #SupplyChain #Aerospace #DefenseTech #Funding #SeedRound

21

2 Comments

Persistent Systems discusses how Cloud Relay™ keeps teams connected by extending the Wave Relay® MANET beyond your local network bubble. Learn how Cloud Relay provides transport agnostic connectivity for teams. Cloud Relay routes Wave Relay data over SATCOM or cellular technologies to ensure mobile users remain persistently connected from anywhere in the world. Watch and find out more > > https://hubs.la/Q03KXDjj0 Persistent Systems, LLC

34