<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>技术工具 on AI 实战派 · 从技术到赚钱</title><link>https://guijiagi.com/categories/%E6%8A%80%E6%9C%AF%E5%B7%A5%E5%85%B7/</link><description>Recent content in 技术工具 on AI 实战派 · 从技术到赚钱</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Fri, 23 Oct 2026 11:08:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/categories/%E6%8A%80%E6%9C%AF%E5%B7%A5%E5%85%B7/index.xml" rel="self" type="application/rss+xml"/><item><title>AI 内容流水线脚本：从采集到发布的自动化</title><link>https://guijiagi.com/posts/2026-10-23-68-ai-content-pipeline-scripts/</link><pubDate>Fri, 23 Oct 2026 11:08:00 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与 Cursor 进企业低代码</title><link>https://guijiagi.com/posts/2026-10-15-13-outsystems-agent-experience-coding/</link><pubDate>Thu, 15 Oct 2026 20:12:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-13-outsystems-agent-experience-coding/</guid><description>OutSystems 十月十二日在拉斯维加斯发布 Agent Experience，把 Mentor、Claude Code、Cursor、Codex、Kiro 接进低代码平台。</description></item><item><title>GPT-OSS 与 Nemotron 全家桶进 AWS GovCloud，合规账号怎么调</title><link>https://guijiagi.com/posts/2026-10-15-12-bedrock-govcloud-gptoss-nemotron/</link><pubDate>Thu, 15 Oct 2026 20:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-12-bedrock-govcloud-gptoss-nemotron/</guid><description>AWS GovCloud 十月十四日公告 GPT-OSS 20B 与 120B、GPT-5.4、Nemotron Nano 全系上线 Bedrock。</description></item><item><title>ComfyUI 十月更新，Veo 3 Lite 与十比特视频怎么接入</title><link>https://guijiagi.com/posts/2026-10-15-11-comfyui-veo3lite-gptimage2-10bit/</link><pubDate>Thu, 15 Oct 2026 20:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-11-comfyui-veo3lite-gptimage2-10bit/</guid><description>ComfyUI 更新日志十月十四日加入万相视频、Veo 3 Lite、GPT-Image 2 与核心节点十比特视频输入输出。</description></item><item><title>TensorRT-LLM 夜间版上架 CUDA 13：vLLM、SGLang、TRT-LLM 三家容器同天更新</title><link>https://guijiagi.com/posts/2026-10-15-12-tensorrtllm-nightly-cuda13-efa-oct15/</link><pubDate>Thu, 15 Oct 2026 16:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-12-tensorrtllm-nightly-cuda13-efa-oct15/</guid><description>十月十五日 NGC 上架 tensorrtllm-runtime-nightly 新标签，基于 CUDA 13，带 EFA 变体，三家推理后端共用同一套 Dynamo 运行时。</description></item><item><title>TrendForce：2027 年云厂商资本开支破 1.5 万亿美元，AI 服务器吃掉八成</title><link>https://guijiagi.com/posts/2026-10-15-11-trendforce-2027-csp-capex-1.5t-ai-server/</link><pubDate>Thu, 15 Oct 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-11-trendforce-2027-csp-capex-1.5t-ai-server/</guid><description>TrendForce 预估 2026 年前九大 CSP 资本开支超 9570 亿美元年增 106%，2027 年破 1.5 万亿，AI 服务器占整体服务器产值 81%。</description></item><item><title>谷歌工程师实测：AI 写了 75% 生产代码，但 88% 的改动仍要人把关</title><link>https://guijiagi.com/posts/2026-10-15-10-google-75pct-ai-code-88pct-oversight/</link><pubDate>Thu, 15 Oct 2026 15:54:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-10-google-75pct-ai-code-88pct-oversight/</guid><description>谷歌内部数据显示 AI 起草超 75% 生产代码，完全放手比例从 7.5% 升到 11.9%，Argon agent 跑 1389 次任务越复杂成功率越低。</description></item><item><title>Anthropic 七人小队自研 Scion：日均合并代码从 2500 涨到 3 万次，Git 扛不住了</title><link>https://guijiagi.com/posts/2026-10-15-09-anthropic-scion-version-control-30k-merges/</link><pubDate>Thu, 15 Oct 2026 15:48:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-09-anthropic-scion-version-control-30k-merges/</guid><description>AI 写代码把 Anthropic 单体仓库日合并推到 3 万次、日检出 2500 万次，Git 流量要超 600PB，七人团队五个月自研 Scion。</description></item><item><title>英伟达开放 Vera Rubin 设计规范：256 颗自研 CPU 机架、800V 直流供电</title><link>https://guijiagi.com/posts/2026-10-15-08-vera-rubin-open-design-256cpu-800v/</link><pubDate>Thu, 15 Oct 2026 15:42:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-08-vera-rubin-open-design-256cpu-800v/</guid><description>十月十五日英伟达向外部 OEM 开放下一代 AI 平台 Vera Rubin 服务器设计规范，推出 256 CPU 机架，直流电压提到 800 伏。</description></item><item><title>Zed 本周更新：BYOK 接 GPT-6.1 Sol，子代理自动压缩香吗？</title><link>https://guijiagi.com/posts/2026-10-15-16-zed-byok-gpt61-subagent/</link><pubDate>Thu, 15 Oct 2026 12:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-16-zed-byok-gpt61-subagent/</guid><description>Zed 稳定版本周更新加入 GPT-6.1 Sol 的自带 Key 支持、子代理运行中实时切模型、子代理自动压缩与命令行 diff 分屏开关，本文实测新工作流。</description></item><item><title>SGLang Omni 官方文档更新：Intel 显卡跑推理怎么装？</title><link>https://guijiagi.com/posts/2026-10-15-15-sglang-intel-xpu-install/</link><pubDate>Thu, 15 Oct 2026 12:24:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-15-sglang-intel-xpu-install/</guid><description>SGLang Omni 的 Intel XPU 安装文档 10 月 14 日更新，给出从轮子安装到源码编译的完整路径，且刻意不钉死 sglang 版本，本文梳理安装步骤与常见报错。</description></item><item><title>火山 LAS AI 1.6.0 上了敏感内容打码算子：数据出湖前怎么自动糊？</title><link>https://guijiagi.com/posts/2026-10-15-14-volcengine-las-sensitive-mask/</link><pubDate>Thu, 15 Oct 2026 12:18:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-14-volcengine-las-sensitive-mask/</guid><description>火山引擎 AI 数据湖服务 10 月 10 日发布 1.6.0 版本，新增敏感内容打码算子，可自动识别图片视频中的敏感画面、文字与语音，并对敏感区域做高斯模糊或静音处理。</description></item><item><title>claude-haiku-4-5 退役下限落到 10 月 15 日：路由表怎么改？</title><link>https://guijiagi.com/posts/2026-10-15-13-claude-haiku-4-5-deprecation-migration/</link><pubDate>Thu, 15 Oct 2026 12:12:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-13-claude-haiku-4-5-deprecation-migration/</guid><description>Anthropic 弃用表显示 claude-haiku-4-5-20251001 的退役评估下限为 2026 年 10 月 15 日，模型当前仍可用，但调用方现在就该规划降级路由。</description></item><item><title>Ollama 联手 Fly.io：云端智能体终于能安全跑了？</title><link>https://guijiagi.com/posts/2026-10-15-12-ollama-fly-agent-cloud/</link><pubDate>Thu, 15 Oct 2026 12:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-12-ollama-fly-agent-cloud/</guid><description>Ollama 10 月 13 日发布博客宣布，AI 智能体现在可以在 Fly.io 的云电脑中安全使用 Ollama 的云端模型，本地 Agent 与云模型之间第一次有了托管级通道。</description></item><item><title>vLLM 新博客：投机解码没有置信头也能自适应验证？</title><link>https://guijiagi.com/posts/2026-10-15-11-vllm-adaptive-verification-online-estimator/</link><pubDate>Thu, 15 Oct 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-15-11-vllm-adaptive-verification-online-estimator/</guid><description>vLLM 团队 10 月 14 日发布技术博客，推出在线拟合的接受率估计器，让没有置信头的草稿模型也能跑自适应投机解码，本文拆解原理与部署要点。</description></item><item><title>prefill 不等所有注意力副本就绪：CascadeEP 给 MoE 拆了哪三道门</title><link>https://guijiagi.com/posts/2026-10-10-18-cascadep-async-moe-prefill/</link><pubDate>Sat, 10 Oct 2026 16:42:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-18-cascadep-async-moe-prefill/</guid><description>CascadeEP 用异步专家并行、streamFFN 批处理、机会式权重拉取三招，治 MoE prefill 阶段注意力副本之间的长尾失衡。</description></item><item><title>让决策模型吃 100 局豆人：JEVMAN 把「快」和「对」放同一个记分板</title><link>https://guijiagi.com/posts/2026-10-10-16-jevman-pacman-decision-benchmark/</link><pubDate>Sat, 10 Oct 2026 16:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-16-jevman-pacman-decision-benchmark/</guid><description>Opper 十月九日发布 JEVMAN，让 Jev、Clef、GPT-6 Luna 等决策模型各玩 100 局吃豆人，按均分、延迟、成本排名，2 秒超时回退规则。</description></item><item><title>PrivateUse1 这步棋：Spyre 怎么从「外挂后端」变成 PyTorch 一等设备</title><link>https://guijiagi.com/posts/2026-10-10-12-pytorch-spyre-native-device/</link><pubDate>Sat, 10 Oct 2026 16:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-12-pytorch-spyre-native-device/</guid><description>十月六日 PyTorch 官方博客详解 torch-spyre，用 PrivateUse1 给 Spyre 真实设备身份，让张量常驻、流、编译器抽象全部原生对接。</description></item><item><title>把每个推理请求挂进实时拍卖：Architect Liquid Inference 想做 LLM 的交易所</title><link>https://guijiagi.com/posts/2026-10-10-07-architect-liquid-inference-auction/</link><pubDate>Sat, 10 Oct 2026 15:36:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-07-architect-liquid-inference-auction/</guid><description>十月七日发布的 Liquid Inference 是个对每个请求跑现场拍卖的 LLM 路由，供应商实时竞价，买方按满足约束的最低出价成交。</description></item><item><title>0.6B 查、9B 索：Perplexity 把多模态检索拆成两把不对称的刀</title><link>https://guijiagi.com/posts/2026-10-10-03-pplx-embed-v2-late-colbert/</link><pubDate>Sat, 10 Oct 2026 15:12:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-03-pplx-embed-v2-late-colbert/</guid><description>pplx-embed-v2-late 用 0.6B 边缘模型跑查询、9B 模型建索引，做免 OCR 的 PDF 页图检索，MADQA 拿到 92.4。</description></item><item><title>llm-openai-decisions 插件：用命令行调用 OpenAI 的 Jev 式决策 API</title><link>https://guijiagi.com/posts/2026-10-10-16-llm-openai-decisions-plugin-jev/</link><pubDate>Sat, 10 Oct 2026 12:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-16-llm-openai-decisions-plugin-jev/</guid><description>Simon Willison 用 GPT-6 Astra 读文档自动写了 llm-openai-decisions 插件，让终端用户直接调用 OpenAI 的 Decisions API。</description></item><item><title>vLLM CPU 推理 ZenDNN 后端：AMD EPYC 上跑大模型，性能到底提升了多少？</title><link>https://guijiagi.com/posts/2026-10-10-15-vllm-cpu-zendnn-amd-epyc/</link><pubDate>Sat, 10 Oct 2026 12:24:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-15-vllm-cpu-zendnn-amd-epyc/</guid><description>红帽 AI 3.4 引入 vLLM-CPU 的 ZenDNN 后端，为 AMD EPYC 处理器提供优化内核，让 CPU 推理性能逼近入门级 GPU。</description></item><item><title>Red Hat AI Inference 3.5：不用 GPU 也能跑 LLM，Podman 一条命令起服务</title><link>https://guijiagi.com/posts/2026-10-10-12-redhat-ai-inference-35-cpu-deploy/</link><pubDate>Sat, 10 Oct 2026 12:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-12-redhat-ai-inference-35-cpu-deploy/</guid><description>红帽十月六日发布完整教程，用容器在 x86 CPU 上部署大语言模型推理，配套 cpueval CLI 做自动化性能基准。</description></item><item><title>llm 0.36 发布：Simon Willison 的命令行工具原生支持 GPT-6 Sol 和 Luna</title><link>https://guijiagi.com/posts/2026-10-10-11-llm-036-cli-gpt6-sol-luna-single-turn/</link><pubDate>Sat, 10 Oct 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-11-llm-036-cli-gpt6-sol-luna-single-turn/</guid><description>llm 0.36 新增 gpt-6-sol 和 gpt-6-luna 模型 ID，插件可声明 supports_conversation=False 适配单轮模型。</description></item><item><title>JetBrains Mellum 2.1：为编码子代理训练的 12B 开源模型，Ollama 两行命令跑起来</title><link>https://guijiagi.com/posts/2026-10-10-06-jetbrains-mellum21-coding-agent-model/</link><pubDate>Sat, 10 Oct 2026 11:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-06-jetbrains-mellum21-coding-agent-model/</guid><description>Mellum 2.1 十月八日发布，12B 参数，Apache 2.0，专为路由、问答和子代理场景设计，用强化学习在真实编码环境中训练。</description></item><item><title>Saluki 27B：8GB 显存跑 2-bit 量化 Qwen3.8-27B，工具调用反超原版？</title><link>https://guijiagi.com/posts/2026-10-10-04-saluki-27b-2bit-gguf-local-tool-calling/</link><pubDate>Sat, 10 Oct 2026 11:18:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-10-04-saluki-27b-2bit-gguf-local-tool-calling/</guid><description>Conway Research 发布 Saluki 27B，2-bit GGUF 仅 7.89GB，Apache 2.0 协议，在工具调用基准上居然超过原版 Qwen3.8-27B。</description></item><item><title>P/D 分离之后：vLLM 与 TileRT 把 decode 侧做成可插拔引擎</title><link>https://guijiagi.com/posts/2026-10-09-12-tilert-pluggable-decode/</link><pubDate>Fri, 09 Oct 2026 16:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-12-tilert-pluggable-decode/</guid><description>一旦 prefill 与 decode 分开，decode 侧就变成可替换模块。vLLM x TileRT 示范了延迟敏感场景下的专用 decode 设计。</description></item><item><title>FP8 KV Cache：用精度换 HBM 容量，这笔账怎么算</title><link>https://guijiagi.com/posts/2026-10-09-11-fp8-kv-cache-capacity/</link><pubDate>Fri, 09 Oct 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-11-fp8-kv-cache-capacity/</guid><description>vLLM V1 已支持 FP8 KV Cache。把 KV 从 BF16 压到 FP8，显存占用直接减半，但要付出精度代价。本文给换算公式与取舍建议。</description></item><item><title>Qwen3.5 在 Blackwell 上跑到 25K TPS/GPU：混合架构的服务化难题</title><link>https://guijiagi.com/posts/2026-10-09-10-qwen35-gdn-25k-tps/</link><pubDate>Fri, 09 Oct 2026 15:54:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-10-qwen35-gdn-25k-tps/</guid><description>Qwen3.5 混合全注意力与 Gated Delta Network 层，vLLM 在 Blackwell 上单 GPU 冲到 25K TPS。难点在异构状态的跨 worker 传输。</description></item><item><title>llm-d：让路由层「认识」KV 缓存，prefix 命中不再靠运气</title><link>https://guijiagi.com/posts/2026-10-09-09-llmd-prefix-aware-routing/</link><pubDate>Fri, 09 Oct 2026 15:48:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-09-llmd-prefix-aware-routing/</guid><description>llm-d 读取 vLLM 的 KV-cache 事件做 prefix-aware 路由，并用全局索引与 CPU、SSD 分层卸载管理分布式 KV 缓存。</description></item><item><title>vLLM 的 Prefill 与 Decode 分离：把首 token 延迟与 token 间延迟分治</title><link>https://guijiagi.com/posts/2026-10-09-08-vllm-pd-disaggregation/</link><pubDate>Fri, 09 Oct 2026 15:42:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-08-vllm-pd-disaggregation/</guid><description>vLLM 把计算密集的 prefill 与带宽密集的 decode 拆到不同实例，分别调优 TTFT 与 ITL。本文讲清什么时候该拆、拆了怎么调度。</description></item><item><title>imatrix 量化实战：为什么你的 GGUF 小模型总是差点意思</title><link>https://guijiagi.com/posts/2026-10-09-16-imatrix-gguf-quant/</link><pubDate>Fri, 09 Oct 2026 12:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-16-imatrix-gguf-quant/</guid><description>同样是 4bit，带 importance matrix 的量化明显更稳。本文记录 imatrix 数据采集、量化与质量回测的完整流程。</description></item><item><title>Portkey 网关多供应商兜底：一个 API 地址背后挂三家模型</title><link>https://guijiagi.com/posts/2026-10-09-15-portkey-gateway-fallback/</link><pubDate>Fri, 09 Oct 2026 12:24:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-15-portkey-gateway-fallback/</guid><description>单家模型挂了或涨价怎么办。本文用 Portkey 在前面加一层网关，挂多家供应商，自动 fallback 与重试，上层代码零改动。</description></item><item><title>把 LLM 评测写进 pytest：DeepEval 让每次改提示词都有据可查</title><link>https://guijiagi.com/posts/2026-10-09-14-deepeval-pytest-ci/</link><pubDate>Fri, 09 Oct 2026 12:18:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-14-deepeval-pytest-ci/</guid><description>提示词一改就翻车，是因为没有回归集。本文用 DeepEval 把评测做成 pytest 用例，接进 CI，每次改动自动跑黄金集。</description></item><item><title>HuggingFace TGI 上线新模型怎么做金丝雀：滚动更新不中断服务</title><link>https://guijiagi.com/posts/2026-10-09-13-tgi-canary-rolling/</link><pubDate>Fri, 09 Oct 2026 12:12:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-13-tgi-canary-rolling/</guid><description>换模型最怕全量重启引发雪崩。本文用 HuggingFace TGI 加一层轻量网关，实现新模型金丝雀发布与滚动回滚。</description></item><item><title>ExLlamaV2 配 TabbyAPI 多卡跑长上下文：比你想的省显存</title><link>https://guijiagi.com/posts/2026-10-09-12-exl2-tabbyapi-multigpu/</link><pubDate>Fri, 09 Oct 2026 12:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-12-exl2-tabbyapi-multigpu/</guid><description>高并发推理框架之外，ExLlamaV2 配 TabbyAPI 是本地多卡跑长上下文的另一套选择。本文记录其启动参数、显存占用与分工。</description></item><item><title>TensorRT-LLM 在 Blackwell 上 build engine：别再照搬常用框架的启动参数</title><link>https://guijiagi.com/posts/2026-10-09-11-tensorrtllm-blackwell-build/</link><pubDate>Fri, 09 Oct 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-11-tensorrtllm-blackwell-build/</guid><description>GB300 的 $0.123 单价背后是 TensorRT-LLM 编译出的 engine。本文记录在 Blackwell 上 build engine 的流程、关键参数与常见编译错误。</description></item><item><title>MoE 推理的隐形瓶颈：专家加载与 WideEP 为什么要把专家摊到更多卡上</title><link>https://guijiagi.com/posts/2026-10-08-12-moe-wideep-expert-parallel/</link><pubDate>Thu, 08 Oct 2026 16:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-12-moe-wideep-expert-parallel/</guid><description>MoE 模型 decode 阶段被专家权重加载时间卡住。NVIDIA 的 WideEP 把专家跨 NVL72 节点分片，是 2026 年 MoE 部署的关键工程。</description></item><item><title>NVIDIA Dynamo：分离式推理为什么到 2026 才在机架级落地</title><link>https://guijiagi.com/posts/2026-10-08-11-nvidia-dynamo-disaggregation/</link><pubDate>Thu, 08 Oct 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-11-nvidia-dynamo-disaggregation/</guid><description>Dynamo 1.0 四月发布、六月迭代到 v1.2，把 prefill 与 decode 拆到不同资源池。本文解释这个早就被讨论的概念为什么今年才真正可用。</description></item><item><title>KV Cache 的 2026 生存指南：量化、分层与驱逐怎么组合用</title><link>https://guijiagi.com/posts/2026-10-08-10-kv-cache-compression-2026/</link><pubDate>Thu, 08 Oct 2026 15:54:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-10-kv-cache-compression-2026/</guid><description>长上下文与 Agent 并发把 KV cache 推成推理成本第二大项。本文梳理 2026 年可用的 KV 压缩组合拳，不重复 FlashAttention 旧账。</description></item><item><title>从 GPTQ/AWQ 到 NVFP4：2026 年为什么量化重新变成硬件原生</title><link>https://guijiagi.com/posts/2026-10-08-09-nvfp4-quantization-blackwell/</link><pubDate>Thu, 08 Oct 2026 15:48:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-09-nvfp4-quantization-blackwell/</guid><description>Blackwell 之后，FP4 从后训练量化技巧变成硬件原生格式。本文梳理 NVFP4、MXFP4 与 DeepGEMM 这条新量化路线。</description></item><item><title>SGLang 的 RadixAttention：Agent 时代为什么前缀缓存是头等大事</title><link>https://guijiagi.com/posts/2026-10-08-08-sglang-radix-prefix-cache/</link><pubDate>Thu, 08 Oct 2026 15:42:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-08-sglang-radix-prefix-cache/</guid><description>SGLang 用基数树做 KV 前缀缓存，并在 DeepSeek-V4、Qwen3.8 上做到 day-0 支持。本文拆解这套机制为什么对多轮 Agent 至关重要。</description></item><item><title>一条命令把视频转成带时间轴的字幕：yt-dlp 加 faster-whisper 流水线</title><link>https://guijiagi.com/posts/2026-10-08-whisper-subtitle-pipeline/</link><pubDate>Thu, 08 Oct 2026 12:54:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-whisper-subtitle-pipeline/</guid><description>想要一批视频的文字稿，不用手工听写。yt-dlp 下载、faster-whisper 转写，串起来就是一条本地可复现的字幕流水线。</description></item><item><title>提示词也能回归测试：用 promptfoo 把提示词改动纳入 CI</title><link>https://guijiagi.com/posts/2026-10-08-promptfoo-prompt-regression/</link><pubDate>Thu, 08 Oct 2026 12:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-promptfoo-prompt-regression/</guid><description>改一行提示词，回答质量可能悄悄崩。promptfoo 把提示词评测做成可重复跑的命令，让你像测代码一样测提示词。</description></item><item><title>给团队搭一个私有聊天入口：Open WebUI 接本地与云端模型</title><link>https://guijiagi.com/posts/2026-10-08-openwebui-local-team-chat/</link><pubDate>Thu, 08 Oct 2026 12:24:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-openwebui-local-team-chat/</guid><description>成员各自装 ChatGPT、各自充会员，是最乱的用法。Open WebUI 一个网页，背后同时接本地模型和云端 API，团队共用。</description></item><item><title>用 LiteLLM 把十几家大模型收进一个网关：统一 key、限流与记账</title><link>https://guijiagi.com/posts/2026-10-08-litellm-gateway-proxy/</link><pubDate>Thu, 08 Oct 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-litellm-gateway-proxy/</guid><description>团队同时接 OpenAI、DeepSeek、智谱、本地模型时，各自适配是噩梦。LiteLLM 用一套 OpenAI 协议把它们统一成一个入口。</description></item><item><title>给你的 Agent 装个黑匣子：Langfuse 调用追踪实战</title><link>https://guijiagi.com/posts/2026-10-08-langfuse-agent-tracing/</link><pubDate>Thu, 08 Oct 2026 11:54:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-langfuse-agent-tracing/</guid><description>Agent 出了错，最难的是复现「它当时看到了什么、调了哪个工具」。Langfuse 这类开源观测工具能把每一步轨迹完整录下来。</description></item><item><title>给 GitHub 仓库接一个 AI Code Review 机器人：Actions 实战配置</title><link>https://guijiagi.com/posts/2026-10-08-ai-code-review-github-actions/</link><pubDate>Thu, 08 Oct 2026 11:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-08-ai-code-review-github-actions/</guid><description>用 GitHub Actions 加一个 PR 自动评审工作流，调大模型 API 对 diff 逐条点评。本文给出可直接复制的 yml 与提示词要点。</description></item><item><title>GPTQ 与 AWQ：权重量化两派，到底该选哪个</title><link>https://guijiagi.com/posts/2026-10-07-gptq-awq-quantization-deep-dive/</link><pubDate>Wed, 07 Oct 2026 18:54:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-gptq-awq-quantization-deep-dive/</guid><description>同样是把权重压到 4bit，GPTQ 用二阶误差补偿，AWQ 保护显著权重。拆解两派原理、质量差异与选型建议。</description></item><item><title>FlashAttention 与 KV Cache：长上下文为什么这么吃显存</title><link>https://guijiagi.com/posts/2026-10-07-flashattention-kv-cache-deep-dive/</link><pubDate>Wed, 07 Oct 2026 18:48:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-flashattention-kv-cache-deep-dive/</guid><description>注意力是 O(N²) 的显存怪兽，KV Cache 又随并发与长度膨胀。拆解 FlashAttention 怎么省显存，以及长上下文的成本账。</description></item><item><title>让模型稳定吐 JSON：结构化输出的三层防线</title><link>https://guijiagi.com/posts/2026-10-07-prompt-structured-output-json/</link><pubDate>Wed, 07 Oct 2026 18:42:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-prompt-structured-output-json/</guid><description>模型偶尔吐出的 JSON 缺括号、带多余文字。从 prompt 约束到 JSON mode 再到语法约束，三层手段把格式锁死。</description></item><item><title>Ollama 与 LM Studio 上手避坑：本地模型怎么调才顺</title><link>https://guijiagi.com/posts/2026-10-07-ollama-lmstudio-local-tips/</link><pubDate>Wed, 07 Oct 2026 18:36:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-ollama-lmstudio-local-tips/</guid><description>Ollama 偏命令行、LM Studio 偏图形界面。从模型选型、上下文长度到 API 调用，讲清两个本地工具的分工与坑。</description></item><item><title>投机解码实操：本地推理翻倍提速的正确姿势与坑</title><link>https://guijiagi.com/posts/2026-10-07-speculative-decoding-speedup/</link><pubDate>Wed, 07 Oct 2026 18:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-speculative-decoding-speedup/</guid><description>draft 模型先猜、主模型批量验证，吞吐能提 1.5 到 3 倍。但词表不匹配会反噬。手把手讲清怎么开、怎么调。</description></item><item><title>打工人的 AI 效率工具链：把重复办公动作脚本化的三个切入点</title><link>https://guijiagi.com/posts/2026-10-07-office-ai-automation-toolchain/</link><pubDate>Wed, 07 Oct 2026 12:42:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-office-ai-automation-toolchain/</guid><description>每周花三小时整理报表、回邮件、归档文件？这些重复动作才是脚本和 AI 该接管的。三个切入点，照做就能省时间。</description></item><item><title>AMD 显卡跑本地模型：ROCm 装对版本，比 CUDA 省钱一半</title><link>https://guijiagi.com/posts/2026-10-07-amd-roc-local-llm-deploy/</link><pubDate>Wed, 07 Oct 2026 12:12:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-amd-roc-local-llm-deploy/</guid><description>AMD 卡不是不能跑大模型，是 ROCm 版本和显卡型号要对得上。这篇讲清支持列表、安装坑和 Ollama/LM Studio 的调用方式。</description></item><item><title>Ollama 常用命令清单：从 run、pull 到挂 API 服务的实战笔记</title><link>https://guijiagi.com/posts/2026-10-07-ollama-common-commands-api/</link><pubDate>Wed, 07 Oct 2026 12:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-ollama-common-commands-api/</guid><description>ollama run 一条命令跑模型，pull 拉取，serve 起服务。这篇把高频命令、Modelfile 自定义和常见报错一次整理。</description></item><item><title>LM Studio 上手：图形界面跑本地模型，从下载模型到开 API 服务</title><link>https://guijiagi.com/posts/2026-10-07-lm-studio-gui-local-model-api/</link><pubDate>Wed, 07 Oct 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-lm-studio-gui-local-model-api/</guid><description>不想敲命令？LM Studio 把下载、量化选择、对话、开 OpenAI 兼容 API 全做成了图形界面。新手本地跑模型的最短路径。</description></item><item><title>Claude Desktop 接 MCP 实战：配置、排查与常用官方服务器清单</title><link>https://guijiagi.com/posts/2026-10-07-claude-desktop-mcp-config-troubleshoot/</link><pubDate>Wed, 07 Oct 2026 11:42:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-claude-desktop-mcp-config-troubleshoot/</guid><description>配置文件写错一个字符，MCP 工具就静默消失。这篇把 json 配置模板、日志排查路径和几个开箱即用的官方服务器一次讲清。</description></item><item><title>向量库别一上来就 Milvus：Chroma、Qdrant、pgvector、Milvus 怎么选</title><link>https://guijiagi.com/posts/2026-10-07-vector-database-selection-guide/</link><pubDate>Wed, 07 Oct 2026 11:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-vector-database-selection-guide/</guid><description>百万级以内用 pgvector 或 Chroma 就够，Qdrant 适合生产混合检索，Milvus 留给亿级规模。选错库，运维成本比省下的查询时间还贵。</description></item><item><title>GPT-Live-1语音API：全双工对话，把'打电话'这件事重做一遍</title><link>https://guijiagi.com/posts/25-gpt-live-1-full-duplex-voice-api/</link><pubDate>Mon, 14 Sep 2026 00:00:00 +0000</pubDate><guid>https://guijiagi.com/posts/25-gpt-live-1-full-duplex-voice-api/</guid><description>2026年9月10日，OpenAI发布GPT-Live-1语音对话API。 如果你只看新闻标题，&amp;#34;OpenAI又发新模型&amp;#34;似乎并不稀奇。但只要亲自用过它的Demo，就会立刻意识到这次不一样：你说话的时候，它在听；它说话的时候，你可以随时打断；你停顿半秒，它会接话；你说&amp;#34;等一下&amp;#34;，它真的会等。</description></item></channel></rss>