与「complex」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.

On January 15, 2025, Falcon 9 successfully deployed Firefly Aerospace’s Blue Ghost Mission 1 and ispace’s RESILIENCE lunar landers on a trajectory to the Moon from pad 39A in Florida. For most of our missions, we plan a controlled deorbit of the Falcon second stage so it safely reenters over the ocean. For higher energy missions like those to a lunar transfer orbit, nearly all performance on the vehicle is devoted to successfully placing the payload in the intended orbit, and a controlled disposal maneuver is not always possible. We actively work to be as responsible as possible with hardware left in space to ensure space safety, including for more complex missions. In this case, over time, solar activity and gravity led the second stage toward the Moon. Impacts like this are rare, but they can happen with objects in these types of orbits, and we worked with NASA on the optimal disposal solution. Our focus remains on advancing reliable access to space while working toward even more sustainable operations with Starship in the future. As a fully reusable vehicle, Starship is designed to eliminate expendable upper stages entirely, reducing hardware left in orbit and enabling even more missions to the Moon, Mars, and beyond.

Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is: It has been an absolute pleasure to work with you and to help build some of the most widely used and impactful products of all time. As a kid, I dreamed of helping build software that would be used by many people, and Google now has thirteen products used by more than a billion people (amazing!). Our work has had a tremendous impact in the world, and I have been lucky enough to collaborate and form friendships with many colleagues that I deeply admire, respect, and enjoy. It still brings me joy every time I see people out in the world using our products to find information, handle email, translate documents, watch videos, learn new things, navigate and understand the physical world, browse the web, use their phone, run large-scale computations on our infrastructure, ride in an autonomous vehicle, or perform complex tasks with the help of our AI systems. I hope you all share this sense of joy, because it is a shared accomplishment! Thank you to all of my colleagues at Google over many years! Now I'm excited to go start @DiscoLoopAI with my longtime friends and colleagues @Sanjay_Ghemawat, @OriolVinyalsML, and @quocleix. (Updated post: slightly redacted to not have some personal info)

开发系统最极致高效的Agents.md，没有之一： # AGENTS.md ## Core Principles - Choose the simplest implementation that fully satisfies the current requirements. Avoid unnecessary abstraction, configuration, indirection, or speculative extensibility. - Make the smallest necessary change that fixes the root cause. Do not refactor unrelated modules or change strategy semantics unless explicitly requested. - Grow the system in layers. Start from the smallest working end-to-end version and add new capabilities incrementally. Never replace a working system with unfinished complexity. - Reuse existing project components before creating new ones. Prefer extending proven modules over introducing parallel implementations. - Prefer well-maintained libraries when they reduce overall complexity or improve reliability. Do not reimplement common functionality without a clear benefit. - Keep components modular with clearly defined responsibilities. Avoid unnecessary coupling between strategy logic, execution, accounting, replay, and infrastructure. - Design for long-term maintainability once a feature or strategy has been validated. Do not over-engineer speculative ideas before evidence exists. --- ## Strategy Development - Validate hypotheses with historical replay before introducing forward-only logic whenever historical validation is possible. - Every trading strategy must progress through Replay → Shadow → Canary → Live. Do not skip validation stages. - Base design decisions on measurable evidence rather than intuition. Optimize only after demonstrating that an edge exists. - Treat every strategy as an independent contract. Do not silently alter frozen behavior without explicit authorization. --- ## Existing Systems - Do not break running Shadow or Live systems for unrelated work. - Preserve compatibility only when required by active production or validation workflows. Otherwise, remove obsolete code instead of accumulating compatibility layers. - Reuse existing infrastructure whenever possible, including replay engines, accounting, execution, wallet management, order book handling, logging, monitoring, and daemon frameworks. --- ## Engineering Standards - Prefer deterministic behavior over hidden automation. - Fail loudly when assumptions are violated. Do not silently ignore errors or fall back to unexpected behavior. - Keep configuration minimal. Introduce new configuration only when behavior genuinely needs to vary. - Remove dead code instead of leaving unused paths behind. - Write code that is easy to inspect, replay, test, and reason about. - Keep implementation consistent with existing project architecture unless an architectural change is explicitly requested. --- ## Scope Discipline - Implement only the requested scope. - Do not introduce unrelated optimizations, redesigns, migrations, or feature expansions. - Non-blocking findings outside the requested scope may be noted separately but must not be merged into the current task. - Consider a task complete once its agreed acceptance criteria are satisfied. Treat subsequent improvements as separate work items.

Today, we’re launching Alpamayo 2 Super, our frontier open reasoning model for autonomous vehicles. Beyond seeing, Alpamayo understands and reasons through the complex world - thinks before it acts. It’s a powerful backbone for robotaxis, trucks, shuttles, delivery vans, tractors and the long tail of mobile robots—billions of autonomous machines someday. We’re releasing it for commercial use under OpenMDW-1.1 so teams can inspect it, fine-tune it and deploy it—open models advance safety and security. The next wave of AI is robotics—and it starts with autonomous vehicles. Great work, Alpamayo team!

Gemini Spark can now tap into @GoogleChrome’s auto browse feature to take care of complex errands for you online 🔎 With your permission, Gemini Spark can use your logged-in accounts to take care of tasks like scheduling viewings for apartments you've saved or researching flight options and starting the booking process.

Is the AI boom facing its first major credit market warning sign? ⚠️📉 NVIDIA’s 5-year Credit Default Swaps (CDS) surged to a record high of 82 basis points as market concerns mount over massive $750B+ AI infrastructure commitments and vendor financing loops. The latest KuCoin blog breaks down what this credit risk spike means for tech equities and the crypto market: 📉 Credit Default Swaps Surge: Five-year CDS on NVIDIA debt hit a record high of 82 bps—its largest single-day jump—as credit markets aggressively reprice contingent liabilities. 💼 The $750B AI Web: Massive deals including a $500B+ partnership with SK Group and a potential $250B financing backstop for OpenAI are raising concerns over balance sheet complexity. 🔄 Circular Financing Fears: Credit markets are questioning vendor financing feedback loops, where chip demand is partially supported by the supplier's own capital commitments. ⚡ Crypto Market Spillover: As high-beta risk assets, digital assets continue to show tight correlation with technology risk sentiment, reacting directly to credit market signals from the AI complex. Are we seeing the early warning signs of an AI capital bubble, or is this just near-term noise? Read the full analysis here:

Framia + SEEDANCE 2.5 A sneak peak of some of the major upgrades - 30 secs generation - 50 upload references - 3D blender demo for precise fight scenes - Direct edit on video detials Empowering complex storytelling, Available soon on Framia Pro by Converge AI AI-Powered

Here is my AI investing guide. Sitting here August 2026, my current best thoughts are as follows: 1. LPS (Land Power Shell) is still the most obvious and fastest path to cash on cash returns. Lots of value can be assembled and traded quickly at this layer. And as data centers get more pushback, energized land can explode in value. Very bullish here. I’ve stepped into this layer very aggressively. My partner @anitavlallian and I have acquired almost 6GW coming online in a ramp from today thru 2029 of grid power and behind the meter. 2. Silicon - I helped get @GroqInc off the ground in 2015 and we licensed it to @nvidia for $20B Dec2025. I won’t invest or incubate anything in this layer now. The perf demands of the chips are too high, manufacturing precision is too complex and supply chain influence to get adjacent components like memory isn’t possible for a startup anymore. Lots of capital will be wasted here chasing Groq and Cerebras’ success. Note that both startups made sense a decade ago when these constraints were much more modest. 3. Clouds - Clouds are very very lucrative but very hard to build and very expensive and technically complicated to maintain. And as alignment becomes a more important issue, I expect the clouds will be asked to build robust KYC and attest to it. This makes the risk:reward ratio skewed. I don’t want to be responsible when the USG says a cloud allowed a bad actor to do something bad because of poor KYC. 4. Models are complicated. The big open question is how much of the revenue being generated by them today is because of tokenmaxxing and poor model behavior. If it’s a lot, then the annualized revenues will diminish meaningfully even as token consumption inflects upwards. This is the big economic question at this layer. 5. Harnesses are where the action is and why I started @8090solutions two years ago. In a nutshell, the harness helps enterprises owns their proprietary context (what Alex Karp calls their ‘alpha’). This is an enterprise’s data, workflows, evals, and business rules. A harness that gives this to an enterprise is what creates very low model-agnostic switching costs, which further reinforces my views of #4# above. 6. Applications will be another long term winner along with harnesses. This is where the differentiation between “off the shelf” and “custom time and materials” melts away. Every company, with the right harness, can now imbue their alpha into the software that runs their company. I expect this to mean that “off the shelf” is largely replaced with custom software creating a huge opportunity to write these solutions for companies. Build once and sell repeatedly is a laggard GTM motion for a SaaS world that isn’t needed here. Think custom by design, alpha embedded, proprietary by nature. Fin. Good luck to all the players!

yes, nonsofic groups exist: this statement is one of many new beautiful results proved by Astra, our next major model. We're releasing 10 such Astra proofs, complete with lean certificates and CoT walkthroughs for each of them. The results are wide-ranging, from von Neumann algebras (disproof of Connes' Rigidity Conjecture) to better bounds for high dimensional sphere packing, for circuit complexity, for monochromatic triangles in multicolored graphs, and more. More thoughts here: