与「Efficiency」相关的搜索结果

This slide from the Anthropic IPO roadshow was a big factor in my decision to leave. It’s an insane plot and I’m surprised they made it public. It shows the compute efficiency of pretraining over time. The y-axis is a **log scale**: they are seeing continued superexponential algorithmic progress. Mythos was not a one-time deviation, but a point on a superexponential. They could already retrain Mythos with a fraction of the compute originally needed. It doesn’t strictly imply a software-only intelligence explosion- they have been spending more on R+D. But algorithmic improvements do not require ridiculous creative genius and are on the verge of being automated. You do the math.

You're right that cost efficiency is what actually decides usage at scale. Hydrogen hits near-frontier scores at the lowest price and highest speed of the group, so it leads for most practical work. Carbon still takes pure hard-task capability when the budget allows. The rest sit higher on the price curve for smaller gains.

Meet Gemini 4 Hydrogen. It brings cutting-edge intelligence to everyone, with speed and efficiency for everyday coding and agentic work. Across 4 thinking levels, it stands on its own or teams up with Carbon and Argon — 2.5X faster than 3.8 Flash, outpacing competing models in its class.

Following Carbon + Argon, Gemini 4 Hydrogen brings cutting-edge intelligence to everyone. Built for speed and efficiency for everyday tasks, coding and agentic workflows. Compared to 3.8 Flash, it finishes tasks 2.5x faster. Also has 4 thinking levels, so you only wait and pay for the reasoning depth a task requires. Rolling out today to all tiers of @GoogleAIStudio, the Gemini API, @GeminiApp, Search and paid Google AI Pro and Ultra subscribers in Google @Antigravity.

软件工程这个行业未来即将大变天，包括编程语言。 就像 Elon Musk 说的一样，从第一性原理来看即将变天：现在我们用的传统编程语言 efficiency 不够高，马上就有一套新的范式出来。现在很多人已经做出了雏形。 举个例子：最近那个 React Native 的 SaaS 叫 Expo，他们的老板是之前 Quora 的 CTO。他 follow 了我，然后我发现他做了一套新的 domain-specific language 来描述 UI，并且做了一套 Rust 的跨平台框架。 这套东西未来肯定会替代 Electron，但是 iOS App 的话不好说，桌面端肯定是，绝对会天翻地覆。 但我们也不需要 FOMO，因为这个东西最强的地方就在于：我们现在不去投资也没有关系，未来重写很容易

Fable 5.2 vs Opus 5.5 Tested both on a rocket launch scene. Fable 5.2: $23.45 | 32 mins Opus 5.5: $10.35 | 24 mins Fable 5.2 is really good, and the detailing and realism are incredible. But I expected better efficiency or at least pricing closer to Fable 5.1.

Introducing EmbeddingGemma 2, a new open multimodal model that sets the standard for on-device efficiency. - our first open, natively multimodal embedding model - handles text, code, image, video, and audio tasks within a lightweight, modular 740M parameter form factor - ideal for offline, privacy-first RAG when paired with Gemma 4 - outperforms some specialist models more than twice its size Weights available now on Hugging Face.

“Superconductors do not follow Joule’s law, but can we make a superconductor function like a diode?” asks Prof. Zhang Ding. He explores charging heat, superconducting efficiency, and his lab’s quantum superconducting diode at liquid nitrogen temperatures.

Introducing omni-macos: a semantic Finder for Apple Silicon. It was first released months ago, but now getting really good and becoming my daily productivity. Over the last few months I've kept improving fully local, on-device semantic search for my everyday agentic use. The goal: a local multimodal search engine that's easy for both humans and agents, fast, and handles CRUD in real time. After years of working on embedding and reranker models, I figured this would be easy, especially with coding agents hill-climbing tps for you. Turns out making it actually good is pretty damn hard. I've also seen other projects like zvec-grep going after the same thing, but I think most of them miss some key points about local search, especially on personal devices. They optimize for efficiency and relevance, which is fair with a SaaS mindset, but not enough for an air-gapped personal device: - Efficiency matters, but on a laptop, an indexer that hogs the machine to finish sooner is worse than one that takes a bit longer and you never notice. - Personal files are massively duplicated: v1, v2, v-final, v-final-final, plus backups, often with only tiny diffs between them. So using the file as the indexing unit is inefficient. - Some files get edited all the time, but only a small part changes each time. Reindexing the whole file is, again, a waste. - CRUD needs to be handled properly at the folder level. - You only have one GPU interface and it's non-preemptive. Embedding needs it, vector search needs it, OCR needs it. You can't lower the priority of work that's already queued, so priority can only be set at admission.

Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: