
Dreamer, Writer. Media Observer [email protected] #StandWithUkraine
genesis
Joined July 2008
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中国版的《SNL》(官方名称:《周六夜现场》中国版,由优酷、欢乐传媒和笑果文化在 2018 年斥巨资联合打造,陈赫、岳云鹏担任首席主咖),最终的下场可以用八个字概括:“播出一季、中途下架整改、豆瓣不及格、彻底绝后(无第二季)”。
具体是怎么一步步走向惨淡收场的,核心有以下几个原因:
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1. 灵魂被阉割:只有“壳”,没有“刺”
* 美版 SNL 的灵魂:其长盛不衰 50 年的核心在于“真直播(Live)” + “无下限的政治与时事辛辣讽刺(Political Satire)”(比如恶搞总统、政客、当周社会热点丑闻)。
* 中国版的妥协:首先它根本不敢做真直播,而是改成了“准直播(录播剪辑)”;其次,在审查尺度和创作环境下,它完全无法触碰硬核时政与尖锐社会议题。结果只能把全部火力集中在“年轻人加班、催婚、买房、打车、追星”这几个被嚼烂了的安全话题上,变成了加长版的“职场相声小品合集”。
2. 口碑崩盘与“水土不服”
* 节目組其实非常舍得砸钱:不仅请了美版 SNL 原班导师团飞到国内指导舞美、灯光和一镜到底的转场,甚至把原版标志性的圆钟改成了“北京西站方形大钟”。
* 但中美喜剧土壤差异太大,生搬硬套美式 Sketch(素描喜剧)的节奏,配上网络烂梗,让老美剧迷觉得“太尬、太温和”,而普通下沉市场观众又看不懂美式荒诞幽默。最终豆瓣评分跌至 4.9~5.1 分,两边不讨好。
3. 中途遭监管重锤“停播整改”
* 2018 年 7 月底(播到第 5 期后),恰逢广电总局对网络视听节目进行暑期集中整治,中国版《周六夜现场》因为部分段子被指“低俗、导向问题”,一度在优酷平台遭遇紧急停更、正片全面下架整改。
* 虽然删减整改后勉强把第一季剩下的集数(共10期)播完,但这次下架彻底断送了广告商的信心,优酷自此直接砍掉了该项目,再也没有第二季。
Anthropic 分享了个Claude Code 的用法,核心就是让Sonnet、Haiku、Opus各司其职
省钱关键不在分工本身,而是啥时候才该掏钱让Opus出手
命令超简单:
claude --model sonnet --advisor opus
分工长这样:
→ Sonnet 跑主流程写代码
→ Haiku 并行查文件和文档
→ Opus 只在关键时刻参与
Opus 就三个时刻出手:计划确定前、同样错误出现两次、任务完成前
这样省额度的细节都藏在里面了
mp.weixin.qq.com/s/yiLOktNiq…
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以后再也不用请家教了!🤐
港大 HKUDS 开源了一个 AI 私教,叫 DeepTutor,GitHub 已经 4.3 万 star
它不是那种问一道答一道的聊天框
把课本、讲义、笔记丢进去,它就围绕你自己的资料来讲
讲题、出题、做深度研究、画可视化讲解,全在同一个工作区里
最狠的是它会记住你学到哪了
换个会话,进度和错题也接得上
记忆还拆成三层,原始记录、每个场景的要点、对你的整体画像,都能打开看,也能自己改
看网课也能用,B 站和 YouTube 链接贴进去,哪一秒没听懂就问哪一秒
装起来就三行
pip install -U deeptutor
deeptutor init
deeptutor start
懒得配的话,直接把仓库丢给 Claude Code 或 Codex,它根目录有一份 SKILL.md,agent 读完就能自己帮你装好
算一笔账就知道了
以前我去给别人上家教,两个小时 200 块
现在一个月的 Plus 都不用 200 块,随时想问就问,而且还很有耐心
AI 第一个冲击的就是教育行业啊😱
传送门 👉 github.com/HKUDS/DeepTutor
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最近看到一堂斯坦福公开课,用大约两小时,系统梳理了大语言模型从文本输入到训练、推理和生成结果的完整链路。
它不会让你两小时造出一个 Claude 或 ChatGPT,但能帮你建立一张非常重要的“大模型知识地图”。
课程重点:
00:00|LLM分词机制 文本进入模型前,为什么必须先被拆成 token?不同分词方式又会怎样影响训练效率与模型表现?
25:44|模型如何解码提示词 大模型并不是一次“理解”完整句话,而是根据上下文计算概率,逐个预测接下来最可能出现的 token。
35:40|大模型训练流程 从训练数据、预训练到参数更新,了解模型能力究竟是怎样一步步形成的。
1:16:47|从零理解LLM架构 拆解嵌入层、注意力机制、Transformer 模块和输出层,看清语言模型内部的数据流动方式。
我最大的感受是:
很多人每天都在研究提示词和新工具,却没有真正理解模型底层是怎样工作的。
当你理解了 token、注意力、训练和推理之后,很多原本模糊的概念都会连起来:
为什么上下文长度有限? 为什么模型会产生幻觉? 为什么推理速度如此重要? 为什么训练一个大模型这么昂贵?
建议先完整看一遍建立框架,再根据时间点回看不懂的部分。两小时不能让你成为大模型工程师,但足以帮你判断下一步应该学什么。
从YouTube搬运视频也可以骗流量
Holy f*ck, Google just released a free 1-hour course on agentic knowledge graphs
"Your agents don't need a bigger model. They need a graph that remembers everything"
Prompts → Agents → Knowledge Graphs → Self-Improving Agents
04:01 - Build your first GraphRAG agent from scratch
30:00 - Agentic search: agents that find the answer on their own
35:48 - Graph engineering in practice, live
47:06 - Self-improving agents that get smarter with every run
While you scroll, someone else is learning Google's $750,000 skill
This free course is better than any $1,500 graph engineering bootcamp
Bookmark and watch it today
Then read the full harness engineering guide below ↓
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准备面试 AI 岗位的看看这个GitHub仓库!
有人把 35 家公司的面试题整理成了仓库,按公司分类,高频题还附了解答链接
GitHub:github.com/pallavi-shekhar/a…
想往大模型应用、Agent 方向投简历的,先把那 10 类高频题过一遍,收藏这个仓库
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Anthropic senior engineer:
“Prompting is no longer the job. 99% of people still open one chat and call it agents.
I run agents inside /loops + /graphs + /harness — same model, same tokens, 10x more completed work.
The system plans, verifies, and keeps shipping while I’m offline.”
Prompts → Agents → Loops → Graphs → Harness
In this 1-hour course she shows exactly how Anthropic builds a team of self-improving agents that recover and improve without you.
Worth more than every $1500 agent engineering course combined.
Watch it today before it gets buried.
Then read the full guide for Opus 5.5 loop + jev engineering ↓
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THIS IS A PURE F*CKING GOLD
Anthropic made Agent Skills an open spec and GitHub filled up with entire Claude Code engineering teams for free
one repo ships 380 skills, 30+ agents and 70+ commands. another one watches your spend in real time so the team you just installed doesn't quietly eat your weekly limit. nobody is charging for any of it
10 GitHub repos worth opening tonight:
01 wshobson/agents → a plugin marketplace that installs the same agents into Claude Code, Codex, Cursor and Copilot, so your setup survives switching tools
github.com/wshobson/agents
02 subinium/awesome-claude-code → a curated list of tools, skills, plugins and MCP servers. the map before you start downloading
github.com/subinium/awesome-…
03 rohitg00/awesome-claude-code-toolkit → 135 agents, 35 curated skills, 42 commands, 176+ plugins, 20 hooks and 14 MCP configs in one place
github.com/rohitg00/awesome-…
04 anthropics/skills → the official Agent Skills repo. start here to see the shape of a working SKILL.md before you copy anyone else's
github.com/anthropics/skills
05 alirezarezvani/claude-skills → 380+ skills, 30+ agents and 70+ commands, portable to Codex, Gemini CLI, Cursor and eight more agents
github.com/alirezarezvani/cl…
06 disler/claude-code-hooks-mastery → the hooks reference. this is where you learn to make the agent stop on your rules instead of stopping to ask you
github.com/disler/claude-cod…
07 muhammadusmangm/claude-code-best-practices → setup, CLAUDE.md templates, multi-agent patterns and cost optimization, written as a wiki instead of a dump
github.com/muhammadusmangm/c…
08 Maciek-roboblog/Claude-Code-Usage-Monitor → real-time usage monitor with predictions and warnings, so you find the wall before you hit it
github.com/Maciek-roboblog/C…
09 Scorpio3310/claude-code-usage-statusline → daily and monthly spend, 5h and weekly limit meters, per-model quotas, all local, no API key
github.com/Scorpio3310/claud…
10 eugeniughelbur/obsidian-second-brain → persistent memory as plain markdown in an Obsidian vault, shared across Claude Code, Codex and six other agents
github.com/eugeniughelbur/ob…
install the statusline before you install the agents. you want to watch what the fleet costs while it runs, not find out on Sunday when the weekly meter is already flat
then hand the first team one job with an obvious finish line: one reviewed PR, one brief, one red test turned green
keep the pieces that changed the result and delete the rest the same day. a config you did not write is a config you cannot debug at 2am
the repos cost nothing. four agents repeating the same investigation is where the money actually goes
save this one, the list only gets longer from here
the article below shows how to keep context small, reuse finished work and contain retries, so more of your budget reaches the finished result ↓
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现在最值得做的创业方向,很多其实已经明显到摆在桌面上了。
Greg Isenberg 一口气列出 39 个想法:围绕 AI Agent、数据、机器人和个人 AI,把已经验证过的商业模式重新做一遍。
完整清单👇
1. 企业数据版 Zestimate:估算一家公司数据对 AI 实验室值多少钱
2. Agent 版 Google Analytics:查看哪些 Agent 访问网站,以及它们想找什么
3. 一人公司版 Y Combinator:投资带着一支 Agent 团队创业的独立创始人
4. Agent 版 Okta:管理每个 Agent 的身份和权限
5. 企业数据版 Marie Kondo:清理数据,让 Agent 真正用起来
6. Agent 版 FICO:Agent 花钱前先查看信任评分
7. Agent 版 Geico:为 Agent 犯下的昂贵错误提供保险
8. Agent 版 Datadog:看清 Agent 执行任务的每一步
9. Agent 版 Twilio:为 Agent 提供电话号码和短信服务
10. Agent 版 Upwork:按任务雇用 Agent,而不是自由职业者
11. AI 版 ESPN:像报道大型比赛一样报道每次模型发布
12. 机器人训练版 Shutterstock:拍摄自己做家务的视频并获得报酬
13. AI 数据版 Credit Karma:查看、修改或删除 AI 掌握的个人信息
14. AI 诈骗版 Norton:保护父母免受伪造语音和邮件欺骗
15. Agent 购物版 PayPal Buyer Protection:Agent 买错东西时负责处理
16. AI 版 Geek Squad:为家庭和小企业安装、配置个人 Agent
17. 私有 AI 版 Dell:为不愿使用云服务的企业提供盒装 AI
18. 二手 GPU 版 Carfax:购买前先查看 GPU 健康报告
19. 闲置 GPU 版 Airbnb:出租没有使用的算力
20. AI 模型版 Rotten Tomatoes:用真实评价替代跑分榜
21. Skills 版 G2:帮助用户找到最适合 Agent 的 Skills
22. AI 记忆版 Dropbox:把一个 Agent 对你的了解迁移到其他 Agent
23. 实体产品版 Canva:用 AI 完成设计,并直接生产出来
24. 个人 Agent 版 ADT:当 Agent 做出越权行为时及时报警
25. 机器人版 Hertz:搬家、大扫除或办活动时临时租一台机器人
26. 互联网创业版 Harvard Business School:系统教授如何建立线上业务
27. Agent 版 Workday:入职、管理和停用数字员工
28. 个人肖像版 Getty Images:安全授权自己的脸和声音给 AI 使用
29. 老年人版 Meta Muse:为高 LTV 的垂直人群定制个人 AI
30. Agent 版 Google Ads:付费成为 Agent 优先推荐的答案
31. Agent 版 Loom:自动回放 Agent 完成任务的全过程
32. Agent 版 Plaid:让 Agent 获得银行、航空公司和商店的授权访问
33. Vibe Coding 应用版 Jiffy Lube:快速修复 AI 写出的故障应用
34. 数据集版 Ancestry:追踪训练数据到底来自哪里
35. 人类创作内容的“有机认证”:证明内容确实由真人制作
36. 创业者版 Strava:记录每周真正发布了什么
37. 会砍价的 Honey:让 Agent 直接替用户谈价格
38. 个人 Agent 平台版 SEMrush:分析 Muse 等平台上的流量与竞争
39. Agent 版航空里程:专门为 Agent 设计忠诚度和奖励计划
This is f**king insane...
Andrej Karpathy spent 8 years inside OpenAI and Tesla.
Last week he compressed all of it into one free 2-hour lecture.
Agents -> Harness -> Loops -> Graphs
There are bootcamps charging $15K that teach less than this.
You probably don't have 2 hours today.
Bookmark it and watch it before the feed buries it.
Then read the guide below and build your first agent harness ↓
Google just dropped a free 2-hour masterclass on Loop and Graph Engineering
From one prompt to 200+ agents that check and fix their own work:
0% → 00:15 - Learn graph engineering from scratch
30% → 30:13 - Build your first agent graph, step by step
45% → 42:41 - Spin up as many agents as you need, all in parallel
75% → 59:47 - Loop engineering: send bad work back until it passes
100% → 1:23:03 - Build a graph that fixes itself while you sleep
Prompts → Agents → Loops → Graphs → Self-Improving Systems
Most people still stuff every instruction into one agent and hope it listens
Worth more than any $2,000 agentic engineering course
Bookmark and watch it today
Then read the full harness engineering guide below ↓





























