#AI 知识学习 AI Projects & AI Agents 的关系&区别
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Channel
@clawstaffs
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
130subscribers
-2 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003894621205 |
|---|---|
| Type | Channel |
| Username | @clawstaffs |
| Created | Between 1 February 2026 and 30 June 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 9 August 2026 |
| Last confirmed live | 15 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 15 August 2026 |
| On Telegram | t.me/clawstaffs |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 15 Aug 2026, 00:03 | 130 | -2 |
| 9 Aug 2026, 00:46 | 132 | no change |
| 8 Aug 2026, 06:51 | 132 | first reading |
20 posts held, back to 19 July 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 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 6 of 19 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 7 August 2026 |
|---|---|
| Posts held | 20 (19 July 2026 – 7 August 2026) |
| Views total | 781 |
| Reactions total | 7 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 9 Aug 2026, 00:46 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.
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
7 reactions across 6 posts, in 3 distinct kinds. The most used accounts for 71.4% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 5 | 71.4% | |
| ❤ | 1 | 14.3% | |
| 🍓 | 1 | 14.3% |
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 6 of the 20 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 7reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 19 July 2026 to 7 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.
#AI 知识学习 AI Projects & AI Agents 的关系&区别
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Muse Code 代表的并不是一个新的代码补全工具,而是 Software Engineering Agent(软件工程智能体)。 它的目标已经从: • 帮你写一个函数(Code Completion) 升级为: • 帮你完成一个开发任务(Software Engineering) 未来 AI Coding 的竞争重点,也正在从: 谁写代码更快 逐渐转向: • 谁能理解整个代码仓库 • 谁能拆解复杂需求 • 谁能并行完成多个开发任务 • 谁能自动测试、修复和提交代码
💻 Meta 发布 Muse Code:AI 编程进入多 Agent 协同开发时代 Meta 正式推出 AI 编程 Agent Muse Code(测试版),面向大型代码仓库开发场景,可帮助开发者完成从任务规划、代码编写到结果验证的完整软件工程流程。 Muse Code 基于 Meta 自研编程模型 Muse Spark,支持通过终端(CLI)安装使用。与传统 AI Coding 工具不同,它采用 Multi-Agent(多智能体)协作 架构:面对复杂开发任务时,会自动拆分需求,并启动多个 AI Agent 在独立工作树(Worktree)中并行开发,避免修改当前工作分支,降低代码冲突风险。 Meta 表示,在内部测试中,Muse Code 曾同时为同一游戏项目开发 6 个独立功能,整个过程中未出现代码冲突。 目前,AI 编程工具正从"代码补全"迈向"完整软件工程"。除了 OpenAI Codex、Anthropic C…
🎬 FFmpeg 9.0 发布!新增动画 WebP 支持,Claude 已参与开源开发 开源多媒体工具 FFmpeg 正式发布 9.0 版本,带来多项新功能,包括动画 WebP 解码、Vulkan 滤镜、CUDA 图像处理、ONNX Runtime 推理支持,以及更多音视频编解码能力,进一步完善多媒体开发生态。 值得关注的是,FFmpeg 开发团队透露,他们通过 Anthropic 的 Claude for Open Source Program 获得了为期六个月的 Claude Max 支持,并在本次版本开发过程中使用 Claude 协助查找遗漏的代码回移(Backport)等维护工作,提高开发效率。 不过,AI 参与开源开发也引发了部分社区讨论。一些开发者认为,AI 可以提升维护效率,但涉及核心代码时仍需要严格的人工审查,以确保代码质量和安全性。 一句话: AI 正逐步成为开源开发者的新工具。从辅助查找 Bug、整理…
Codex + Codex Site 是个好东西, 只要你会打字、会说话就能用它做出很多AI工具,比如:个人AI知识库。 🔔 关注我,下一步将在评论区分享制作过程!
💻 腾讯 CodeBuddy 接入 DeepSeek V4-Flash,AI 编程能力再次升级 如果你在用 CodeBuddy,现在已经可以体验 DeepSeek V4-Flash 正式版了。 腾讯宣布,WorkBuddy、CodeBuddy IDE、VS Code/JetBrains 插件以及 CLI 已全面完成适配,新模型在 代码生成、Agent 任务执行、复杂项目开发 等方面相比上一版本进一步提升。 新版更适合处理: ✅ 代码编写与重构 ✅ Bug 修复与调试 ✅ 多文件项目开发 ✅ AI Agent 自动执行开发任务 📥 获取方式: 更新到最新版 CodeBuddy(IDE、插件或 CLI)即可选择 DeepSeek V4-Flash 模型体验。
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如果你是 AI 机器人开发者,正在为作品缺少曝光、冷启动困难而发愁,现在可以通过 易搜机器人 免费提交收录,让更多用户发现你的机器人! 操作很简单: 1️⃣ 打开 Telegram 机器人 @ezsou_bot 2️⃣ 点击聊天窗口底部的 「添加收录」 3️⃣ 按照提示提交机器人信息,等待审核即可 让好产品被更多人看见,也让更多用户找到真正实用的 AI 机器人!
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🗓️ AI 圈科技前沿:今日重磅大模型发布简报 1️⃣ DeepSeek-V4-Flash 正式版 API 上线公测 2️⃣ 阿里通义千问发布 Qwen-Audio-3.0-ASR 系列语音大模型 3️⃣ MiniMax 发布通用全模态模型 H3(即将开源权重) 4️⃣ xAI 全量开放新一代语音模型 Grok Voice Think Fast 2.0
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MCP 协议迎来最大升级:全面转向无状态架构,更适合 AI Agent 大规模部署 Anthropic 于 7 月 28 日发布 Model Context Protocol(MCP)第 5 版规范,并将其称为自 MCP 诞生以来规模最大的一次架构升级。作为连接大语言模型与外部工具、数据源的开放协议,MCP 此次更新不只是功能迭代,而是对底层通信机制进行了重新设计,为 AI Agent 在生产环境的大规模部署铺平道路。 新版 MCP 最重要的变化,是全面从有状态(Stateful)架构转向无状态(Stateless)设计。旧版本要求客户端建立连接时完成初始化握手,并依赖 Session ID 在服务端维护会话状态,这在水平扩展、负载均衡以及 Serverless 部署场景下存在一定限制。新版则彻底取消了握手流程和 Session ID,每个请求都可以独立完成处理,可被任意实例接收和执行,更加适配 AWS Lambda、Clo…
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OpenAI 开源 Codex Security CLI:把安全扫描直接塞进 CI/CD OpenAI 刚刚开源了一款面向代码仓库的安全工具:Codex Security CLI。 它的定位很直接:扫描代码、发现并验证安全漏洞,并将安全检查直接接入 CI/CD 流程。这样一来,每次提交、构建或合并代码时,都可以自动执行安全检测,而不是等到上线前再进行人工排查。 Codex Security CLI 目前仍处于早期阶段,但已经提供了比较完整的基础能力: • 扫描整个代码仓库 • 分析代码变更中的潜在风险 • 跨多次运行追踪安全问题 • 验证漏洞是否已经修复 • 输出可供后续处理的扫描报告 • 提供 TypeScript SDK,方便集成到现有开发工具中 安装方式也比较简单: npm install @openai/codex-security 查看命令帮助: npx codex-security --he…
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Showing the 12 most recent of 20 posts we hold for @clawstaffs. 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 — 933,141 of 1,345,403entries 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.
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 2 registered channels — 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.
Names
Channels on the register whose handles appear in this channel's posts.
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.
@clawstaffs named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
References a handle that is not a live channel — we have no record it ever was one.
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 15 August 2026 — this entry's latest reading, not the date you are reading this.
“🤖 AI+|AI 工具制作方法 & 案例分享” (@clawstaffs), 130 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/clawstaffs.
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.