🗂 项目名字:unclecode/crawl4ai 📡 来源:GitHub ⭐ Stars:77.2k 📌 项目简介 Crawl4AI是一款专为大语言模型(LLM)优化的开源网页爬取与 scraper 工具。它支持异步爬取,能高效提取结构化数据,直接对接AI应用,降低数据清洗成本。 ⚙️ 核心技术 Python,pydantic,asyncio,fastapi,playwright,crawl4ai,ai,adb,ai,api,artificial,ai 🔥 应用场景 专为大语言模型训练与微调设计,SCENARIO描述生成失败。 🏷️ #Web爬取 #AI数据 #Python

Channel
开源模型
@xsont
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
752subscribers
+4 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1003501776109 |
|---|---|
| Type | Channel |
| Username | @xsont |
| Created | Between 1 December 2025 and 31 May 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 9 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 9 August 2026 |
| On Telegram | t.me/xsont |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 9 Aug 2026, 12:32 | 752 | +4 |
| 8 Aug 2026, 19:45 | 748 | no change |
| 8 Aug 2026, 19:32 | 748 | first reading |
Engagement
17 posts held, back to 7 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 2.55%
- avg views ÷ 752 subscribers
- Avg views / post
- 19.2
- 17 posts measured
- Reaction rate
- 5.26%
- reactions ÷ views · ER floor
- Posts in window
- 17
- of 17 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 1 of 17 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 8 August 2026 |
|---|---|
| Posts held | 17 (7 August 2026 – 8 August 2026) |
| Views total | 326 |
| Reactions total | 1 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 19:45 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Reaction mix
1 reaction across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 1 | 100.0% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 1 of the 17 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 1reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 17 most recent posts we hold, published 7 August 2026 to 8 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Recent posts
🗂 项目名字:WeKnora 📡 来源:GitHub ⭐ Stars:19.5k 📌 项目简介 WeKnora是腾讯推出的开源LLM知识平台,旨在将原始文档转化为可查询的RAG知识库、自主推理智能体以及自维护Wiki,全面提升知识管理效率。 ⚙️ 核心技术 Python, LLM, RAG, 知识图谱, 智能体, 自维护机制 🔥 应用场景 适用于企业级知识库构建、自动化文档处理与问答系统、基于大模型的自主推理代理开发、以及需要持续更新和维护的自动化维基平台场景。 🏷️ #LLM应用 #RAG知识库 #智能体
🗂 项目名字:louislam/uptime-kuma 📡 来源:GitHub ⭐ Stars:89.9k 📌 项目简介 Uptime Kuma 是一款免费开源、界面精美的自托管监控工具。它提供直观的现代 UI,支持 HTTP、TCP、DNS 等多种监控协议,具备实时状态面板和多种通知渠道集成,是个人开发者与中小团队进行服务器及应用健康状态监控的理想选择。 ⚙️ 核心技术 Node.js 🔥 应用场景 适用于个人博客、企业官网、微服务集群及云资源的实时监控。可作为服务器宕机预警系统,配合 Telegram、邮件等通知渠道,确保故障第一时间被感知,提升系统可用性与运维效率。 🏷️ #监控运维 #自托管 #开源工具
❤1
🗂 项目名字:Snailclimb/JavaGuide 📡 来源:GitHub ⭐ Stars:157.6k 📌 项目简介 JavaGuide是广受好评的Java学习指南,不仅覆盖计算机基础、数据库和分布式等高并发场景,还融入系统设计与AI应用开发内容,是后端开发者必备的资源库。 ⚙️ 核心技术 Java, 计算机网络, 操作系统, 数据库, 分布式系统, 高并发编程, 系统设计, AI应用开发 🔥 应用场景 适用于Java初学者构建完整知识体系,开发者备战大厂技术面试,以及后端工程师深入理解分布式架构、高并发处理及新兴AI技术在实际业务中的落地应用。 🏷️ #Java #后端开发 #面试指南
🗂 项目名字:RustDesk 📡 来源:GitHub ⭐ Stars:119.8k 📌 项目简介 RustDesk是一款开源的远程桌面应用,旨在提供完全自托管的解决方案,作为TeamViewer的免费替代方案,支持隐私和安全优先的远程连接。 ⚙️ 核心技术 Rust, Qt, WebRTC, Tokio 🔥 应用场景 适用于需私有化部署的远程办公、IT运维及技术支持场景,用户可自建中继服务器,确保数据完全掌控,避免第三方服务限制或隐私泄露风险。 🏷️ #远程桌面 #自托管 #开源安全
🗂 项目名字:google/guava 📡 来源:GitHub ⭐ Stars:51.7k 📌 项目简介 Google Guava是Java开发的核心标准库集合,广泛供Google及众多企业使用,大幅简化Java开发流程。 ⚙️ 核心技术 Java 1.8+,涵盖多映射、多集合、不可变集合、图处理、并发生死锁检测、哈希计算、I/O及字符串处理工具。 🔥 应用场景 适用于构建企业级Java应用,特别是需要高效集合操作、复杂图算法、高并发场景优化及通用工具封装的开发任务。 🏷️ #Java生态 #核心工具库 #企业级开发
🗂 项目名字:nodejs/node 📡 来源:GitHub ⭐ Stars:118.8k 📌 项目简介 Node.js 是一个基于 Chrome V8 引擎的开源、跨平台 JavaScript 运行时环境。它使服务器端 JavaScript 开发成为可能,广泛应用于构建高性能的网络应用和网络服务,拥有活跃的社区和开放治理模式。 ⚙️ 核心技术 JavaScript, C++, V8引擎, libuv 🔥 应用场景 适用于后端服务开发、API网关构建、实时通信应用、CLI工具开发以及边缘计算场景。凭借非阻塞I/O模型,特别适合高并发、数据密集型实时应用,是现代Web后端架构的核心基础组件。 🏷️ #JavaScript #后端开发 #运行时环境
🗂 项目名字:zed-industries/zed 📡 来源:GitHub ⭐ Stars:88.2k 📌 项目简介 Zed是一款高性能、支持多人协作的代码编辑器,由著名的Atom编辑器和Tree-sitter解析器的创造者团队最新研发,旨在提供极致的编码体验。 ⚙️ 核心技术 Rust, Zig, React, Go。底层采用Rust构建以确保极致性能,前端结合Zig与React提升渲染效率,后端利用Go处理并发协作,实现毫秒级响应。 🔥 应用场景 适用于追求极致性能的开发者日常编码,支持实时多人协作编程,提升团队开发效率;特别适合对编辑器启动速度和代码补全延迟有极高要求的用户。 🏷️ #代码编辑器 #高性能 #多人协作
🗂 项目名字:vercel/next.js 📡 来源:GitHub ⭐ Stars:141.7k 📌 项目简介 Next.js是一个基于React的企业级全栈Web应用框架。它提供了智能化的构建工具、文件系统路由、服务端渲染及静态生成能力,旨在帮助开发者高效构建高性能、SEO友好的现代Web应用,降低开发复杂度。 ⚙️ 核心技术 React, TypeScript, SWC, Webpack, Turbopack, Node.js。通过集成先进的编译器和构建工具,实现极速开发体验;支持TypeScript原生集成与类型安全,结合Server Components提升渲染性能与加载速度。 🔥 应用场景 适用于构建企业级官网、复杂电子商务平台、SaaS应用及高流量博客系统。理想的场景包括需要无缝SSR/SSG混合渲染、API路由开发、图像优化及全局状态管理的动态Web应用,助力提升用户体验与搜索引擎排名。 🏷️ #React…
🗂 项目名字:lyogavin/airllm 📡 来源:GitHub ⭐ Stars:29.5k 📌 项目简介 AirLLM是一个创新项目,通过动态卸载机制大幅降低LLM推理内存占用,无需量化、蒸馏或剪枝,即可让70B模型在单张4GB显卡上流畅运行,甚至支持405B模型在8GB显存下推理。 ⚙️ 核心技术 主要使用Python开发,依托PyTorch等深度学习框架,采用动态卸载(Dynamic Offloading)技术,将模型参数分块加载到显存,按需交换CPU与GPU内存,显著降低硬件门槛。 🔥 应用场景 适用于消费级GPU硬件环境,如仅配备4GB显存的显卡用户。适合本地private部署大型语言模型、AI教育学习、低功耗边缘计算设备以及无法承担高昂集群成本的独立开发者进行LLM推理测试。 🏷️ #大模型推理 #低显存部署 #AI工具
🗂 项目名字:docker/compose 📡 来源:GitHub ⭐ Stars:38.0k 📌 项目简介 Docker Compose是官方提供的工具,通过简单的YAML配置文件定义多容器应用程序。它支持创建和启动所有所需的服务,极大地简化了本地开发和测试环境中复杂应用的部署流程,提升开发效率。 ⚙️ 核心技术 Go语言开发,基于Docker Engine API,使用YAML格式进行服务编排配置。支持跨平台运行于Linux、Windows和macOS,提供命令行界面以实现对多容器生命周期的集中管理与调度。 🔥 应用场景 广泛应用于本地开发环境搭建、持续集成/持续部署(CI/CD)流水线以及测试环境演示。开发者可利用它一键启动数据库、缓存及后端服务组成的完整微服务架构,实现开发、测试与生产环境的一致性。 🏷️ #Docker #容器编排 #开发工具
🗂 项目名字:pola-rs/polars 📡 来源:GitHub ⭐ Stars:39.3k 📌 项目简介 Polars是一个极速的DataFrame查询引擎,完全使用Rust语言编写。它支持多语言接口,包括Python、Rust、Node.js等,旨在为大数据处理提供高性能解决方案,适合对查询速度有高要求的场景。 ⚙️ 核心技术 Rust, Python, Node.js, Polars DataFrame API 🔥 应用场景 适用于需要高性能数据分析的场景,如大规模数据处理、实时查询、BI报表生成以及数据分析管道。多语言支持使其能灵活集成到各类数据科学和工程工作流中。 🏷️ #Rust #数据分析 #高性能
Showing the 12 most recent of 17 posts we hold for @xsont. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank
Citation-graph rank — 1,000,878 of 1,183,361entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Mentions
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 9 August 2026 — this entry's latest reading, not the date you are reading this.
“开源模型” (@xsont), 752 subscribers as measured 9 August 2026. Telegram Register, tgregister.com/channel/xsont.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.