“For a standard Fortune 100 company, if you reorganize in Texas and list on the Texas Stock Exchange—as opposed to incorporating in Delaware and listing on another exchange—you could potentially reduce your cost structure by tens of millions of dollars a year...” @NYSETexas Chairman and CEO James Lee joins @BloombergTV anchor @daniburgz to discuss how a company’s headquarters, listing venue and legal jurisdiction are interconnected. #BloombergInvest# ⏯️

“For a standard Fortune 100 company, if you reorganize in Texas and list on the Texas Stock Exchange—as opposed to incorporating in Delaware and listing on another exchange—you could potentially reduce your cost structure by tens of millions of dollars a year...” @TXSE_ Chairman and CEO James Lee joins @BloombergTV anchor @daniburgz to discuss how a company’s headquarters, listing venue and legal jurisdiction are interconnected. #bloomberginvest# ⏯️

🚨 THE INTERNET IS GETTING HAMMERED BY CYBERATTACKS AND NOW AI IS MAKING THE HACKERS DANGEROUSLY EFFICIENT Look at what's happened in just days. Zeabur suffered a breach exposing data associated with 321,810 accounts and millions of stored environment variables. Railway warned that hackers may have accessed decrypted customer secrets. OVHcloud had malicious software planted in its developer tools. And Reuters uncovered something even more disturbing. One hacker reportedly used AI agents to break into more than 100 company websites, including sites associated with Deere and SharkNinja, while doing almost none of the technical work personally. Meanwhile, American banks are discovering unauthorized AI agents accessing customer accounts. The really frightening shift isn't just that hackers are getting into more systems. They're beginning to automate the entire process. A few instructions. An AI agent. Hundreds of potential targets. The cost of launching cyberattacks is collapsing, while the damage they can cause is anything but. Source: Axios, Reuters / Writer: Oliver

Three-person data team, a queue of weekly requests, and every analytical question outside predefined dashboards stuck in someone's inbox — that was LangChain's BI era. 🔍 The team decided to change. What they needed was a platform unifying dashboards, notebooks, and conversational interfaces with native AI agent capabilities. They chose Hex and designed a five-layer context architecture: dbt data model definitions, a semantic layer, workspace guides, endorsements as trust signals, and GitHub integration. The migration was complete in six weeks with 100% company adoption. The transformation's core wasn't technical — it was about explicitness. Rewriting a weak definition like "account_status: The status of the account" into a full description covering lifecycle states, default filters, and reporting conventions alone changed how reliably the agent answered questions. Key metrics — ARR, pipeline, customer health — were given single authoritative definitions in the semantic layer. Endorsements pointed agents to canonical sources wherever multiple assets addressed the same concept, preventing confusion before it could happen. Today, every function — marketing, product, sales, customer engineering — runs its own analysis without routing through the data team. Monthly agent conversations total roughly 2,200, processing 40x the volume the three-person team could handle manually. The team's role has shifted from "answer every question" to "design the system that lets others answer questions." LangChain's detailed account of this journey, "How LangChain Built an Agent-First Data Stack," makes the case — in concrete numbers and design principles — for what a data team looks like in the agent era. #DataStack# #AIAgent#