与「STEP」相关的搜索结果

This March, we introduced Ask Maps, the biggest @googlemaps update in a decade. Now, we’re making it even more helpful and bringing it to more people. 🌏 Expanded availability: Rolling out in Australia, Brazil, Canada, Indonesia, Japan and Mexico, along with over 150 countries and territories in English. ✨Get more done with Ask Maps: Including complex, multi-step tasks, like ordering food – along with finding the perfect hotel or discovering nearby events. ⌚Real-time transit information: Stay informed about up-to-the-minute delays and conditions for your bus, train, and more. 💬Personal intelligence and past conversations: Choose to securely connect @Gmail so Ask Maps can automatically reference booking details, like a hotel reservation or flight information. You can also pick up conversations just where you left them. 🗣️An easier way to help your community: Share tips and suggest edits to a place conversationally, like changing a store’s hours, right in Ask Maps and the Contribute tab.

Ladies and gentlemen, it's time to pass the torch and demote myself to my natural state: a poster. I'll be stepping back from leading product for 𝕏 and will continue on as an advisor. Serving the X community has been the privilege of a lifetime. X is, and will remain, the most important communication technology in history. But running this app is a 24/7 job and it's now time for me to take a breather. The app is seeing unprecedented growth in new users & engagement. We continue to break records every month. We've climbed 70 spots in the App Store since this time last year. And in the last 400 days, we rebuilt almost every aspect of X: the Timeline, the Android app, onboarding, notifications, chat and more. We also launched nearly 30 new products while protecting the integrity of the town square: becoming the first app to show Country-of-Origin on profiles and mounting defenses against AI bots. There's certainly much more work to be done, but our foundation is stronger than ever. None of this would have been possible without the incredible team here. The next leaders will take X to even greater heights with @benjitaylor on design, @singhai on core product engineering and @dinkin_flickaa on mobile engineering -- among many other great people. Thank you to Elon and the X team for welcoming me into the company. See you on the Timeline.

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

BREAKING: SpaceX has paid Grimes County $10 million ahead of schedule under its Terafab tax agreement, a major step forward for the proposed project. • It equals nearly 40% of the county’s expected $26.5 million in property-tax revenue for all of 2026 • County leaders may use the money for infrastructure, employee pay and retention • SpaceX/Terafab representatives are scheduled for a Q&A at the August 5 Commissioners Court meeting • The broader Terafab project could eventually involve up to $119 billion in investment, as per KBTX.

STB × HKTKWW: A key milestone for Sanya’s global communication! On August 3, Sanya Tourism Board (STB) and Hong Kong Ta Kung Wen Wei Media Group (HKTKWW) officially signed a strategic partnership agreement — marking a major step forward in expanding Sanya’s global communication network and sharing Sanya’s stories with the world. Together, we’re building stronger networks, bigger platforms, and better stories — bringing Sanya to the world. 8月3日，三亞市旅遊發展局與香港大公文匯傳媒集團（HKTKWW）正式簽署國際傳播戰略合作協議，標誌著三亞在傳播網絡佈局上的又一重要落子。 未來，雙方將依託全媒體傳播優勢，在港澳及海外市場開展三亞旅遊常態化宣傳，共同推動三亞打造國際知名旅遊目的地！ 拓展網絡，共建平臺，講好故事！

A good research agent should't just answer a question. It should know which tool to call next. At every research workflow is carefully designed as a sequence of actions: 1. Clarify needs 2. Plan the study 3. Search personas 4. Scan social media 5. Build new AI Personas 6. Interview or run group discussions 7. Generate the research report and reusable panel Each step uses different tools to move the research forward. And the report is not the end. The Personas involved become a reusable AI Panel — a consumer asset you can return to whenever you have a new question. Validate an idea. Design a product concept. Analyze competitors. Build a go-to-market plan. One research workflow can become a reusable system for understanding the people you're building for. 👉 Start your AI research from here: