curated · 2026-09-01

This caught my eye because Anthropic's latest model just grabbed the top spot on a third-party benchmark. Artificial Analysis ran Claude Fable 5.1 at max effort and got a 66, which is a clear jump over the previous leader. The trade-off is straightforward: 20% more per task than Fable 5. I'd discount this a bit for now. The post only gives a headline score and a cost bump—no task breakdown, no latency numbers. If that 20% premium comes mostly from reasoning-heavy tasks, it might be worth it for power users. If it's padded by multiple-choice and common-sense questions, less so. Also worth noting: Fable 5 was already strong on this index. 5.1 looks like it pushed the ceiling higher rather than catching up from behind. I'd wait for the full task split and latency data before deciding if the extra cost pays off.

The reason to click: this moves video understanding from describing what's on screen to actually doing the task. You show Gemini a recording of filling a web form or completing an order in a mobile app, and it replicates those clicks, typing, and scrolling on its own. That's more flexible than traditional UI automation scripts—in theory, you don't need to rewrite rules when the interface changes. I'd discount the hype for now. The post is an official blog announcement with a feature description and a testing invite, but it skips the numbers that matter: task success rate, per-step latency, and robustness to UI variations. UI agents break easily in the wild—a popup or a loading delay can derail the whole flow. Treat this as an early experimental direction, not a ready-to-use assistant.

This piece is worth a look because it puts hard numbers on the fluffiest part of the AI infrastructure boom. Reuters found that data center power requests across multiple US regions top 700 GW—more than 10x actual usage. Texas is the extreme case: grid applications jumped from 48 GW in 2023 to 474 GW, which pushed the state to freeze new interconnections and demand audits on ultimate owners, water plans, and on-site generation. A lot of these applications are duplicates or from companies that can't actually build. After Ohio imposed a $100K study fee, AEP Ohio's pipeline shrank by more than half. Exelon cut its high-probability demand forecast by roughly 40% after tightening guarantees. PJM capacity costs already rose $29.4B due to projected data center growth. I'd read this as a "de-fluffing" signal. States are using upfront fees, audits, and stricter permits to filter out ghost projects. That's good for grid planning, but it also means real data center builds will face slower, more expensive power approvals going forward.