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github.com/Arindam200/awe… 抽空可以跑跑学习下，该仓库系统性地收集了多种LLM的应用案例，覆盖从入门到高级的AI代理、RAG、工作流等范畴，既有面向新手的快速上手代理，也有复杂的端到端流程与多Agent协作范例。
今年在 A 股犯了最大的错误就是轻易的交出自己的筹码了。机器人 ETF 和人工智能 ETF 在上升期初期的时候就因情绪引导下场了。😮💨
视觉模型 OCR 是终态
LLM 输出无效 JSON 已成“玄学”了：括号丢了、引号乱飞；找到github json_repair 这样的库，它能自动修复语法错误、补全缺失值、清理非 JSON 字符，直接提升解析效率，省去无谓的 try/except 嵌套。对于 AI 开发者来说，这就是让 LLM 输出“可用化”的一把瑞士军刀。 github.com/mangiucugna/js…
Human in the loop (HITL) 怎么翻译呢？人在回路？ “Human in the loop”（HITL）是一种系统设计理念，强调在自动化或Agent流程的某些关键环节，必须让人参与决策或操作。 在一些 Agent 里，可以是 [自动流程] → [人类确认/干预] → [继续执行]
Claude code 一个毛病就是给你过度设计，有时候 CLUADE.md 都约束不住，只能再其一个会话让其最小化实现。
我想知道对于 LLM 输出 JSON 超出max_tokens 被截断了，大家都是怎么处理的？注意有些模型 continue 并不会从当前截断的 JSON 处恢复。
Qwen code 是真不敢用啊，它真的扫你的目录，包含三方库的，一会十来块都没了，这谁敢用啊。。
传统OCR 像拆解剧本：OCR、版面检测、分块、嵌入……每一步都可能出错，表格、图例、空间关系全都丢失，信息碎片化严重。 基于视觉模型新范式：直接把每页文档当作图片输入大模型，利用视觉模型和多模态语言模型，保留全部空间和视觉细节，像人一样理解文档。 morphik.ai/blog/stop-pars…
Docker Hub 大陆已死，国内 docker 镜像的生意开始风起云涌了。这不，充值了 5 块钱给镜像加加速。
标准是很重要的，OpenAI 可谓是很多LLM SDK 标准的开创者，很庆幸的是各大 LLM Provider 也遵循这一套标准，这样对接起来就很容易。例如 extra_body 是用于向 API 请求中添加自定义字段的参数， OpenAI 和火山都支持(OpenAI Like)，extra_body 就像 HTTP 请求的“扩展插槽”一样允许任何自定义参数。
🪜 backup 必须要做，否则一个挂了就很难受了。
Louis Young @LouisYoung45813
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Unsloth AI @UnslothAI
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109K Followers 1 Following Claude is an AI assistant built by @anthropicai to be safe, accurate, and secure. Talk to Claude on https://t.co/ZhTwG8dz3D or download the app.
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