写了三年博客，多多少少对写博客、建博客有一点了解，之前在 V2EX 看到这样一条帖子：想给一个女性作家朋友搭建一个博客网站，有什么成熟点的方案 网友评论了很多，有价值的也不少，我在此摘录几条我认为有用的： 1️⃣作家应该不是程序员吧？ 不明白为何那么多人还无脑推要用 git 的静态博客…… wordpress 吧，要是觉得卡顿，安利一下 zblogphp，很好用，主题也不少。 2️⃣Wordpress 插件多 技术成熟。。别信楼什么什么 github+hexo 的 别人又不是程序员，markdown 也做不了复杂的页面布局 3️⃣虚拟空间 + Typecho / Wordpress 或者博客平台.不要净搞些幺蛾子,还不好使(你朋友表示) 你以为很秀的操作在外行人眼中看来即不实际也不好使. 4️⃣大家还是偏极客了吧，程序员之外，愿意写 markdown，自己 git 提交的用户多吗？ 还是有一个易用的后台，广泛的插件、主题选择，也真没有比 wordpress 更好的了。 5️⃣推静态博客的不是蠢就是坏，又不是人人都是程序员。 也不建议自己搭，因为没人做 SEO，没啥用。 建议直接买成熟的平台，或者就地取材。 #博客
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@Isaiahsystem · Post #1203 · 2024/06/30 07:23
Related Subreddits丨Reddit 探索神器 https://anvaka.github.io/sayit/?query=Nietzsche 最近发现的爆炸网站，Reddit 相关论坛探索，Reddit 关键词图谱。它能根据你给出的任意论坛名字或关键词，编织出整个论坛与之相关的子板图谱，与之关联。点击图谱按钮，再点击弹出的侧边栏子板名字，即可跳转。 网站提到这种图谱关系生成由「发布到此 Subreddit 的用户也发布过的其它板块」决定，并通过 Jaccard Similarity（衡量两个集合相似度的指标）来最终确定板块相似度。 Reddit 作为我 RSS 常驻网站之一，挖掘得比较少，这个网站可以好好用用。它类似前面用过的 music map 以及各种论文网站图谱，详情见下面 refs. 相关链接 1 可视化图谱 2 搜索引擎终极索引 #search#graph#tools
@githubtrending · Post #14632 · 2025/04/25 12:00
#python#agents#graph#llms#rag Graphiti helps AI systems handle constantly changing information by building real-time knowledge graphs that track relationships and historical data, allowing them to integrate user interactions, business data, and external sources seamlessly. Unlike traditional methods, it updates information instantly without needing full recomputations, enabling precise historical queries and efficient hybrid searches. This helps AI applications stay context-aware, automate tasks effectively, and manage complex, evolving data with minimal delay. https://github.com/getzep/graphiti
Data structures 101: graphs, a visual introduction for beginners. #article#tutorial#data#ds#graph @thedevs https://kutt.it/4S6HBr
#twitter#animals#dogs#science#physics#graph#mongolia
@cosplayuploadtest2 · Post #102714 · 2025/03/23 03:36
Title: Iori_Sagara_相楽伊織,_GIRLS_graph._デジタル写真集_「泡沫の宵夢」_Set.02 Authors: #None Tags: #None#Iori_Sagara_相楽伊織#Nogizaka46_乃木坂46#デジタル写真集#Iori#Sagara#相楽伊織#GIRLS#graph#デジタル写真集 #「泡沫の宵夢」 #Set #02 recommendation: None TelegraphLinks:page-0-32
@cosplayuploadtest2 · Post #102682 · 2025/03/23 03:34
Title: Iori_Sagara_相楽伊織,_GIRLS_graph._デジタル写真集_「泡沫の宵夢」_Set.01 Authors: #None Tags: #None#Iori_Sagara_相楽伊織#Nogizaka46_乃木坂46#デジタル写真集#Iori#Sagara#相楽伊織#GIRLS#graph#デジタル写真集 #「泡沫の宵夢」 #Set #01 recommendation: None TelegraphLinks:page-0-28
@githubtrending · Post #14649 · 2025/04/30 13:00
#typescript#csv#diagrams#graph#json#nextjs#react#tool#visualization#yaml JSON Crack is a free, open-source tool that instantly turns complex JSON, YAML, CSV, XML, or TOML data into clear, interactive graphs, making it easier to explore and understand your information. It lets you convert between formats, validate data, generate code (like TypeScript interfaces), run queries, and export visuals as images—all while keeping your data private since everything processes locally on your device[1][2][5]. https://github.com/AykutSarac/jsoncrack.com
@githubtrending · Post #14878 · 2025/06/28 14:00
#cplusplus#arduino#cansat#csv#embedded#graph#ground_station#iot#microcontroller#network#projects#qt#serial#serial_studio Serial Studio is a free, easy-to-use tool that lets you visualize real-time data from devices like microcontrollers via serial ports, Bluetooth, or network connections. It works on Windows, macOS, and Linux, and offers customizable dashboards with various widgets to monitor sensor data, debug info, or telemetry. You can quickly plot data, export it as CSV for analysis, and even use advanced features like checksum validation and JavaScript data processing. It supports hobbyists, educators, and professionals by simplifying data monitoring and debugging, saving you time and effort in understanding your device’s output. Pro versions add commercial use and extra features[1][4][5]. https://github.com/Serial-Studio/Serial-Studio
@githubtrending · Post #15518 · 2026/02/24 11:30
#rust#ai#ai_ocr#attention_mechanism#gnn#gnn_model#gnns#graph#graph_neural_networks#llm_inference#low_latency#mincut#neo4j#ocr#onnx#rust#vector#wasm RuVector is a free, open-source vector database that gets smarter with every query. Unlike static databases, it learns from usage via GNN layers, runs LLMs locally with no cloud costs, supports graph queries like Neo4j, scales freely across nodes, and deploys as a single self-booting file (125ms startup). Run with `npx ruvector`. You benefit from faster, more accurate AI search that improves automatically, zero operating costs, full offline/privacy control, and easy scaling—perfect for RAG, agents, or edge apps without vendor lock-in. https://github.com/ruvnet/ruvector