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English(EN) Cascaded LLMs Lift E-Commerce Cart Adds 2.7% in Online Test A cascaded LLM framework for e-commerce storefront generation lifted cart adds by +2.7% in online te

Anthropic发布Claude Opus 4.7,关注安全;低成本工作空间出现

Anthropic发布了Claude Opus 4.7,在SWE-Bench Verified基准测试中获得80.1分,比前代产品略有下降。最新版本强调安全调优,可能以牺牲基准测试的峰值性能为代价。此外,一位开发者创建了一个持久化的Claude AI编码工作空间,每月费用为10美元,利用Pi的执行层和Cloudflare Tunnel克服上下文限制。 AI

影响 Anthropic的最新模型优先考虑安全性,可能影响其在基准测试中的竞争优势,同时为Claude AI用户出现了一个新的低成本工作空间。

排序理由 该集群包含一款具有基准分数的新模型发布以及一个持久化工作空间的技术实现。

在 Mastodon — fosstodon.org 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

报道来源 [3]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    Anthropic Ships Claude Opus 4.7: 80.1 SWE-Bench, 1M Context Anthropic released Claude Opus 4.7 on April 16, 2026, scoring 80.1 on SWE-Bench Verified, a slight r

    Anthropic Ships Claude Opus 4.7: 80.1 SWE-Bench, 1M Context Anthropic released Claude Opus 4.7 on April 16, 2026, scoring 80.1 on SWE-Bench Verified, a slight regression from Opus 4.6's 80.3. The release prioritizes safety tuning over benchmark leadership. https:// gentic.news/ar…

  2. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    Hacker builds $10/mo persistent workspace for Claude Code A $10/month persistent workspace for Claude Code and Claude AI using Pi's execution layer, MCP, and Cl

    Hacker builds $10/mo persistent workspace for Claude Code A $10/month persistent workspace for Claude Code and Claude AI using Pi's execution layer, MCP, and Cloudflare Tunnel. Bypasses session context loss by sharing one filesystem and database across all M https:// gentic.news/…

  3. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    Cascaded LLMs Lift E-Commerce Cart Adds 2.7% in Online Test A cascaded LLM framework for e-commerce storefront generation lifted cart adds by +2.7% in online te

    Cascaded LLMs Lift E-Commerce Cart Adds 2.7% in Online Test A cascaded LLM framework for e-commerce storefront generation lifted cart adds by +2.7% in online tests, using teacher-student fine-tuning to approach closed-weight LLM quality at production latency. https:// gentic.news…