Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

✨ Emily Campbell ✿ has studied AI design patterns more than just about anyone I've ever seen so today's episode is an AI UX deep dive we look at patterns, systems, and mental models for designing great AI products 👇

22,361 Aufrufe • vor 9 Monaten •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

280,790 Aufrufe • vor 2 Monaten

🚨 AI JUST DISCOVERED QUANTUM EFFECTS THAT SCIENTISTS DIDN'T KNOW EXIST. Researchers at the University of Washington used artificial intelligence to simulate dozens of atomically thin sheets of molybdenum ditelluride stacked in precise twisted patterns. At small scales, these materials look relatively ordinary. But when the AI modeled much larger stacks, completely new quantum behaviors emerged phenomena that only exist because of the complex, repeating moiré patterns formed across many layers. Why this matters: • Many of the most interesting quantum effects only appear at scales that are too large for traditional supercomputers to simulate • AI can act as a fast “surrogate” that learns from smaller calculations and predicts behavior at much bigger scales • These large-scale moiré systems can host exotic quantum states useful for quantum computing and new types of electronics • The same approach could be used to discover many other hidden quantum materials The deeper implication: We are entering an era where AI doesn’t just help us analyze data it helps us discover entirely new quantum phenomena that were previously invisible because they only exist in systems too complex for conventional modeling. This could dramatically speed up the search for materials that power future quantum technologies. What do you find more exciting using AI to uncover hidden quantum effects in materials, or the possibility that these stacked atomic sheets could become building blocks for future quantum computers? Follow for more frontier quantum materials and AI-driven discovery.

TheNewPhysics

29,224 Aufrufe • vor 2 Monaten