HackerNews中文版

我在使用苹果最新的 iOS 26 Foundation Models 框架进行开发时，遇到了一个常见问题。由于训练截止日期的限制，所有主流的大型语言模型（LLM）（GPT-4、Claude、Gemini）都对它一无所知。与其等待数月直到模型更新，我花了 2 个小时构建了自定义训练数据：研究：使用 Gemini 的深度研究功能，爬取了所有可用的文档、论坛、GitHub 仓库、Reddit 帖子和 YouTube 视频转录。搜索关键词为“iOS 26 Foundation Models Framework”。优化：让 Claude 将所有内容重构为干净的、分层 Markdown 格式，并针对 LLM 摄取进行了优化。实施：将其加载到 Claude Projects 中，作为一个自定义知识层。结果：从“我没有关于这方面的信息”到获得关于前沿 API 的专家级指导。我的开发流程从反复试验转变为流畅的 AI 辅助实施。这项研究非常彻底，甚至引用了我前一天在苹果开发者论坛上发布的帖子——创建了一个奇怪的递归循环，我正在用我刚刚贡献的知识来训练 AI。这种方法适用于任何新的框架或 API。模式是可预测的：每次重大发布都会创建一个临时的知识空白，而自定义训练数据可以填补这个空白。技术文章及方法论：https://rileygersh.medium.com/how-i-gave-claude-gemini-knowledge-of-ios-26s-foundation-models-03395d7e905c

查看原文

I ran into a common problem building with Apple's new iOS 26 Foundation Models framework. Every major LLM (GPT-4, Claude, Gemini) knows nothing about it due to training cutoffs.Rather than wait months for model updates, I spent 2 hours building custom training data:Research: Used Gemini's deep research to crawl all available docs, forums, GitHub repos, Reddit threads, YouTube transcripts. Looking for "iOS 26 Foundation Models Framework".Optimization: Had Claude restructure everything into clean, hierarchical markdown optimized for LLM ingestion.Implementation: Loaded into Claude Projects as a custom knowledge layer.Result: Went from "I don't have information about that" to expert-level guidance on bleeding-edge APIs. My development workflow shifted from trial-and-error to smooth AI-assisted implementation.The research was so thorough it even referenced my own Apple dev forum post from the day before—creating a weird recursive loop where I was training AI on knowledge I'd just contributed.This approach works for any new framework or API. The pattern is predictable: every major release creates a temporary knowledge gap that custom training data can fill.Technical writeup with methodology: https://rileygersh.medium.com/how-i-gave-claude-gemini-knowledge-of-ios-26s-foundation-models-03395d7e905c

我创建了一个新的训练数据集，用于提升过时的 SOTA 大语言模型。