Ted Liang

Advitya is making a point that most people in AI infrastructure haven’t fully processed yet: the cooling problem has moved. The big chips get liquid-cooled now — they’re getting the attention they deserve. What’s choking these systems is the heat building up in the thousands of smaller components around them — optics, networking, power delivery — in tight spaces where bulk airflow can’t reach. The shift is from flooding everything with cold air to precision thermal transport at the exact point of pain. Everyone talks about the chip. The GPU. The $3 million rack. But the chip isn’t what’s failing. Now add the economics. A 72-GPU rack generates roughly $3.3M–$6.6M in annual revenue depending on utilization (CoreWeave pricing). One HPE configuration shows the power split clearly: 132 kW total — 115 kW liquid-cooled, 17 kW still air-cooled. That remaining 17 kW is only 13% of power, but it lives in the components that can stall the whole machine. A 1,000-GPU cluster has over 4,000 transceivers, each pulling 14–17 watts. That’s roughly 60 kW of distributed heat in exactly the spots where airflow doesn’t reach well (Cisco). Keysight reports that in unoptimized AI networks, over 50% of GPUs can sit idle waiting on data — and nearly half of training failures come from network and component issues, not the algorithm. Even a 5% hidden performance penalty is $166K–$331K per rack per year. Scale that across a 100-rack site and you’re looking at tens of millions in invisible losses. Same pattern on autonomous vehicles, robots, and drones. More AI crammed into tighter sealed boxes. One thermal chokepoint and the whole platform derates — less range, less uptime, less autonomy. This is the drip irrigation moment for cooling. For decades, thermal management was flood irrigation — blast cold air and hope enough reaches the hot spots. That’s increasingly broken. What’s needed now is precision: a smart emitter at every hotspot, delivering exactly what’s needed, where it’s needed, nothing wasted. That’s what Actasys is building. And it maps to a pattern we keep seeing across every domain where AI meets the physical world: the system fails because the physical environment isn’t ready. Thermal limits don’t stay contained as engineering limits on the GPU. They expand into habitat limits. Solve them at the point of pain — with distributed thermal intelligence — and you unlock everything downstream. More on this pattern soon. Actasys isn’t selling cooling. They’re selling the unlock to the most powerful compute. Advitya Patyal, David Menicovich, Miles Flamenbaum, Netafim #PrecisionCooling #AIInfrastructure #PhysicalAI #DataCenterCooling #DripIrrigation #DeepTech #ThermalManagement #DistributedIntelligence

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In the past year, Nate Herkelman has built 200+ AI automations in n8n, and they all boil down to 17 core nodes. If you master these 17 nodes inside n8n, you can automate almost any process you can imagine. So I put together your 2026 n8n Cheat Sheet, a free PDF that walks through: → What each node does → When you should use it → Real examples → Best practices I wish I had learned earlier And to go with it, I just dropped a 24-minute YouTube breakdown where I go through all 17 nodes, explain why they’re useful, and show real automations where each one was used in production. Here are the 17 core nodes I rely on every single day: 1 - Schedule Trigger 2 - Event Triggers 3 - Subworkflows 4 - Split Out 5 - Aggregate 6 - Edit Fields (Set) 7 - IF Node 8 - Switch 9 - Code 10 - HTTP Request 11 - Loop 12 - Webhook 13 - Respond to Webhook 14 - AI Agent 15 - AI Tools 16 - Structured Output Parser 17 - Google Sheets Key takeaway: You don’t need to learn 200 things to build with AI automation. You just need to learn these 17 really well. cc: Nate Herkelman

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As with past eras of technology disruption, there is a phenomenal amount of innovation happening around AI and the massive data centers being built to enable AI. Given that power has become the primary bottleneck to scaling these data centers on planet earth, Philip Johnston and his team have created an innovative new solution, data centers in space! Congrats to Philip Johnston and the Starcloud team.

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