Christopher Bradshaw

Meta just announced they're building AI supercomputers as large as Manhattan. And Zuckerberg isn't kidding around. These aren't just data centers. These are AI empires. Meet Prometheus and Hyperion - Meta's Manhattan-sized bet on artificial superintelligence. Prometheus (launching 2026): ☑ 1 gigawatt of compute power ☑ Enough electricity to power tens of thousands of homes ☑ Built in Ohio's New Albany ☑ Confirmed by Zuckerberg himself on Threads Hyperion (the bigger one): ☑ Scales up to 5 gigawatts of continuous compute ☑ Zuckerberg says just one cluster "covers a significant part of Manhattan" ☑ This is what building AGI infrastructure looks like The money involved is absolutely insane - Meta is investing hundreds of billions in AI infrastructure. Their 2025 CapEx forecast is $64-72 billion, up from $39 billion in 2024. They're deploying 1.3 million GPUs this year alone. Here's how big they are for speed: Meta is using temporary tent-based data centers to accelerate deployment and bypass construction delays. Think about that. They're so eager to get these systems online that they're willing to put billion-dollar AI infrastructure in tents. Here’s the strategic reality: These aren't just data centers. They're the foundation for Meta's Superintelligence Labs, led by Alexandr Wang and Nat Friedman, aiming to surpass AGI milestones. The energy implications are insane - 5 gigawatts is equivalent to hundreds of nuclear reactors. This raises serious questions about power sourcing, sustainability, and emissions. But Meta doesn't care. They're willing to bear those risks. Zuckerberg isn't just doubling down on AI. He's going all-in at empire scale. With Manhattan-scale supercomputers, tent rollouts, and ultra-heavy CapEx, Meta is signaling: → They believe AI superintelligence will redefine everything → Short-term losses are worth it → Power concerns and centralization are risks they'll accept Is Zuckerberg building the future of AI, or is he just spending hundreds of billions on the world's most expensive gamble? Either way, Manhattan-sized AI supercomputers are now a reality. And that changes everything about the AI race.

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I've said it before, and it's only becoming more clear: data enrichment is about to explode. Today’s commerce UI is built around human limitations—how much we can read, compare, or decide at once. But AI agents don’t have those constraints. They consume and process massive volumes of information instantly. What they do care about is relevance. Personalization will become entirely agent-driven, and it won’t rely on brand storytelling. It’ll be about matching precise user needs. This raises a tough question: how do you influence product choice when you no longer control the message? It’s especially challenging for smaller brands. In a world where ad targeting gets weaker and web presence becomes the new distribution, strong data quality and deep, niche enrichment will define who wins.

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The voice AI industry has fundamentally misunderstood its own product-market fit. We're obsessing over conversational sophistication while failing at basic speech synthesis. These "performance barriers" Rishav calls out aren't technical limitations; they're the predictable result of prioritizing flashy demos over robust engineering. Companies won't adopt voice agents not because the technology isn't ready, but because vendors have consistently oversold capabilities while underdelivering on reliability. It’s about time someone said it out loud: the barriers to building RELIABLE voice AI (with stats). Here it out 👇

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The reality is that every engineer is becoming a tech lead with a crew of agents. In that world, the primary job shifts from writing code to coordinating work: tasks, context, handoffs, and verification. That likely calls for a new kind of UI, one that feels closer to Kanban, Jira, or Asana than a traditional IDE. We don’t know yet if Intent is the final winning approach, but I’m excited we’re shipping a research preview instead of standing still. PS: What’s one small, recurring pain in your daily workflow? Drop it in the comments. If it’s a good fit, I’ll build a prototype with Intent and share what we learn.

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Platform engineering is the future 🚀 Developers are at the core of everything we do at PagerDuty 💚 Our latest collaboration with Spotify for Backstage brings PagerDuty’s industry-leading Incident Management directly into Backstage, the open platform reimagining developer experience. With the new PagerDuty Plugin for Backstage, devs can see incidents, on-call context, and service ownership right where they already work : Less dilution ; More focus. Let’s go 🎉 Read more 👇 https://lnkd.in/gnsq8Nr9

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Openclaw joining OpenAI feels like the closest signal yet that the “one-person unicorn” era might not be fiction. If this model scales, headcount stops signaling capability, leverage shifts to agentic systems and architecture, and founders win on system design—not org size. We’re moving from “build a team” to “build a machine.” #AI #Startups #Founders #AgenticAI #FutureOfWork

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Roblox is launching Creator Rewards, a new program that pays developers not just for in-game spend, but for growing the entire platform. If a creator brings in a new or returning user, or helps keep someone engaged daily, they now share in the value that user generates. It’s a big shift — and one that better aligns incentives between developers and the platform. Huge credit to Jim Meenan and the Creator Success team for leading this work. Full details here: https://lnkd.in/gBkVnq5x Our CPO Manuel Bronstein articulates the broader strategy in this Verge interview: https://lnkd.in/g2fKWDMq

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Developing a large ecosystem with both open- and closed-source components comes with some interesting challenges. Back when we were bootstrapping CKEditor, my purist mindset pushed us to develop each of our 30+ packages in separate repositories. The goal was to sort of "force" higher modularity and more responsible architecture for every component. As you can probably guess 😅, it did not scale. Later, we consolidated most of the packages into two monorepos – one open source, one closed source – while keeping a few granular packages separate where it still made sense. This helped a lot, but it didn’t fully solve the problem. We were still working across multiple repositories and kept running into issues like dependency coordination and lockfiles. Recently, we took another step forward. We migrated to pnpm and redesigned our overall approach. In this post, Filip Sobol explains the new setup implemented by the CKEditor 5 Platform team and what we learned along the way: https://lnkd.in/eHS28qxe

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"This is genuinely the most exciting design role in the company right now - leading AI at scale for millions of merchants, defining future of software interfaces, Shopify's #1 priority with full executive support and resources." Always a strong signal when you're discussing a new opening for the roster, this incoming role is huge...!

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Today we announced new capabilities for our Shop Direct and Buy for Me experiences on Amazon, enabling now feeds to facilitate external merchants to list reliably their product listings and reach millions of Amazon customers. Excited on this feature, that is a win/win experience for customers, merchants and brands. Excited to be part of such important project.

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