翻译记录

• 7 Best Agent Skills for Research in 2026: Comparion Guide | NanoSkill
  https://nanoskill.ai/blog/best-agent-skills-for-research
• What I learned building an opinionated and minimal coding agent
  https://mariozechner.at/posts/2025-11-30-pi-coding-agent/#toc_11
• What Is an AI Investment Agent? The Complete 2026 Guide — Barebone AI
  https://barebone.ai/resources/what-is-ai-financial-research-agent
• The Rise of AI-Native Software Engineering: Implications for Practice, Education, and the Future Workforce
  https://arxiv.org/html/2606.12986v1
• One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited | Towards Data Science
  https://towardsdatascience.com/one-rag-pipeline-four-very-different-pdfs-same-four-bricks-every-answer-typed-and-cited/
• Palantir Sovereign AI OS Reference Architecture with NVIDIA
  https://www.palantir.com/sovereignaios
• OpenWiki 0.2 brings OKF to codebase documentation
  https://www.langchain.com/blog/openwiki-0-2-adds-okf-support
• X 上的 Cerebras：“How we built our knowledge base” / X
  https://x.com/cerebras/status/2077822555159945507
• 5 Trends That Defined AI Engineering at World’s Fair 2026
  https://www.latent.space/p/aiewf26trends
• Kimi K3 Tech Blog: Open Frontier Intelligence
  https://www.kimi.com/blog/kimi-k3
• Best Software Engineering Books 2026
  https://newsletter.techworld-with-milan.com/p/best-software-engineering-books-2026
• AI Agent Skills: Why You Need a Package Manager for Your Agents' Knowledge
  https://www.workingsoftware.dev/ai-agent-skills-why-you-need-a-package-manager-for-your-agents-knowledge/
• Using Classical ML to Empower AI Agents | Towards Data Science
  https://towardsdatascience.com/using-classical-ml-to-empower-ai-agents/
• Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It | Towards Data Science
  https://towardsdatascience.com/loop-engineering-with-adaptive-pdf-parsing-start-cheap-pay-for-a-heavier-parser-only-when-the-page-needs-it/
• Compare Tavily, Perplexity API, Google Search Grounding, Exa with LLM-as-Judge in LangSmith
  https://websearchapi.ai/blog/compare-tavily-google-search-exa-perplexity
• How I Cut an AI Agent's Token Use by 94%
  https://vivekhaldar.com/articles/compiling-an-ai-agent-skill/
• This Week in AI: A First for Agentic Ransomware – O’Reilly
  https://www.oreilly.com/radar/this-week-in-ai-a-first-for-agentic-ransomware/
• On swimming, learning and my journey to being the last one - Event-Driven.io
  https://event-driven.io/en/on-swimming-learning-and-my-journey/
• NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval
  https://huggingface.co/blog/nvidia/nemotron-3-embed-wins-rteb
• LiteLLM: AI Gateway for LLMs — Features Overview | by Arseny Zinchenko (setevoy) | Jul, 2026 | ITNEXT
  https://itnext.io/litellm-ai-gateway-for-llms-features-overview-7a6de35575a0
• 5 Trends That Defined AI Engineering at World’s Fair 2026
  https://www.latent.space/p/aiewf26trends
• Learning Software Architecture
  https://matklad.github.io/2026/05/12/software-architecture.html
• X 上的 Peter Wang：“Building against the big labs that are trying to eat you” / X
  https://x.com/BrainsAndTennis/status/2076767931053294017?s=20
• Kimi K3, and what we can still learn from the pelican benchmark
  https://simonwillison.net/2026/Jul/16/kimi-k3/
• X
  https://x.com/jerryjliu0/article/2077537847951945742
• Kimi K3 Tech Blog: Open Frontier Intelligence
  https://www.kimi.com/blog/kimi-k3
• Integrating LLMs in Software Engineering Education: Motivators, Demotivators, and a Roadmap Towards a Framework for Finnish Higher Education Institutes
  https://arxiv.org/html/2503.22238v1
• Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation - InfoQ
  https://www.infoq.com/news/2026/07/stripe-ai-agents-benchmark/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global
• X 上的 Jerry Liu：“The Best Model Routing is Task Specific” / X
  https://x.com/jerryjliu0/status/2077537847951945742
• X 上的 Hanako：“there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two
  https://x.com/hanakoxbt/status/2077518405624644023