CS teams: Drive product adoption with shared channels in customers' @Microsoft Teams/@discord. Real-time @SlackHQ sync, no friction. Faster adoption, less churn! Free beta: #CustomerSuccess# #TetherChat#

CS teams: Tickets great for bugs, but poor for relationships, adoption, & churn prevention. Platform mismatch (your @SlackHQ vs their @discord) kills it TetherChat bridges natively: real-time CS channels across Slack/Teams/Discord. 1-min setup, free beta.

A CS undergrad asked me where he could have most effect in the AI age. I said probably at either extreme: either close to the technology, actually making LLMs, or close to the customer, using AI to give them exactly what they want. Or maybe both if you can stretch that far.

The CS-4 is the fastest AI accelerator in the world, establishing a new roofline for Frontier AI. CS-4 is built to accelerate every part of the AI ecosystem: - Developers who want faster tokens for interactive reasoning and agentic applications. - AI factories require infrastructure that is simpler to deploy and built for hyperscale. - Hyperscalers and Neoclouds need ultra-fast, high-throughput inference to serve the most intelligent AI models in real-time.

Barclays CEO CS Venkatakrishnan says the “extreme specter of AI” shouldn’t overshadow its mainstream capabilities. Watch our full interview here:

If you’re a CS student, I’d suggest writing your thesis around SDCs (silent data corruptions) and efficient algorithmic fault tolerance. We’re quickly going to need to accept the (un)reliability of computing systems again! Every indicator (density, lower voltage gating, raw scale) is moving towards SDCs becoming a more serious issue over the next decade. And let’s not forget when all of this stuff starts to go in space and we have to deal with cosmic events on “not-super-rad-hard” CPUs+GPUs! No, you can’t solve it all in hardware. It’s prohibitively expensive; and the long tail of “potential SDCs” is just too long. One bad apple ruins your pie. One bad GPU can inject repeated corruptions into otherwise noise-tolerant workloads (*cough* AI training). If you continuously accept a corrupted value from say…a bad tensor core, that bad value can quickly spread across the whole cluster. If it happens again and again and again and no one notices; it’s like putting a little bit of spin on a bowling ball. Eventually, the overall trajectory ends up widely different! When you start to imagine this stuff being in space (cosmic bit flips), and quantized(!), each remaining bit carries more critical information. Long term we’re going to have to accept clever, low-overhead software algorithms. Accepting say, a ~3% perf loss can be *absolutely* worth it if it increases your odds of detecting a mercurial core enough!