Nidibaba

[DAN KOE @thedankoe]

https://t.co/7l7Jef99QZ

[Nidibaba @Nidibaba6]

@AI_Jasonyu 学习了

[Nidibaba @Nidibaba6]

@CryptoJHK 我需要giffgaff卡，因为我要telegram

[Nidibaba @Nidibaba6]

@coolish 不支持群聊就没啥意思了

[Nidibaba @Nidibaba6]

@HiTw93 好好玩，能加下联系方式吗？一起交流下

[Anthropic @AnthropicAI]

The Claude Mythos Preview system card is available here: https://t.co/TMtIy8xHiP

[Nidibaba @Nidibaba6]

@yechaofen 111

[Tz @Tz_2022]

这是到目前为止2026年我最喜欢的一张认知论梗图！！

（我把原图做了一下高清重制，现在上面的文字总算可以看清了。。。）

以下是讲解部分（小心头疼 🌚）：

想象一下，如果你把人类的大脑拿出来，和一本厚重的认识论哲学书，外加两升纯粹的“存在主义焦虑”一起扔进榨汁机里，然后把这坨混合物泼在画布上，你就会得到这张图。

这就是我们大脑里的“知识储藏室”的真实平面图。它简直是一个 epistemological（认识论）的灾难现场。

——

我们可以把这个图拆解成几个“大脑街区”来逛逛：

1. 舒适区与绝对虚无（基础设定）

著名政客拉姆斯菲尔德曾提出过著名的“已知的已知”和“未知的未知”。这张图直接把这个概念按在地上摩擦，然后加了十几层心理包袱。

* 我们知道的事情（明亮的浅绿色）

这是你的舒适区。这里装满了“太阳从东边升起”、“1加1等于2”以及“我昨天晚饭吃了什么”。这是你在这个疯狂宇宙中唯一能抓住的救命稻草。

* 我们不知道我们不知道的事情（庞大的灰色背景）

终极盲区。你甚至没法对这些事情感到焦虑，因为你连形容它们的词汇都没有。这是现实的暗物质，包围着我们渺小的认知气泡。

* 不可知的事物（深红色）

比如宇宙大爆炸前一秒到底有什么？这些东西就像是被锁在宇宙地下室里的保险箱，钥匙一开始就被上帝扔进了黑洞。

——

2. 虚荣心与社交伪装区（底部区域）

这是整张图里最充满“人味儿”的地方。人类太好面子了，以至于我们在“知识”外面建起了一座由谎言和心虚构成的堡垒。

* 我们假装自己���道的事情（深粉色）

也就是“冒名顶替者综合征”重灾区。当同事在开会时提到一个晦涩的行业黑话，或者某人高谈阔论一本你连封面都没见过的名著时，你深沉地点了点头。对，就在这个区域。

* 别人认为我们知道但实际上我们不知道的事情（蓝绿色）

当你过年回家，你亲戚家的熊孩子拿着一道高中物理题或者问你“你是大人了，告诉我怎么赚钱”时，你内心防线崩溃的瞬间。我们只是穿着风衣假装大人的三个小孩罢了。

* 我们假装不知道的事（青色）

“啊？轮到我洗碗了吗？”“老板发的那条周末加班信息我没看到啊。”——这是人类趋利避害的最高智慧。

——

3. 痛苦与懊悔的交界处（左侧区域）

* 我们希望自己能记住的事情（浅绿色）

这里是所有密���、你凌晨三点想到的绝世好点子、以及迎面走来跟你打招呼的那位老熟人的名字的“坟墓”。它们就在你嘴边，但你就是够不到。它完美地和“我们忘记的一切（紫色）”接壤。

* 我们宁愿自己不知道的事情（灰绿色）：

被诅咒的知识。比如你偶然得知了火腿肠的制作过程，或者无意中看到了父母的某些聊天记录。你疯狂地想找到大脑的“Ctrl+Z（撤销）”键，但很遗憾，大脑没有这个功能。

——

4. 潜意识与直觉的玄学区（上方区域）

* 我们生来就具有的本能知识：

比如呼吸，比如对蛇和高处的恐惧。这是祖先刻在你DNA里的出厂设置。

* 我有一种预感

科学无法解释的第六感。比如你不需要看保质期，光凭一种神秘的直觉就知道冰箱里那盒牛奶已经变成了生化武��。

5. 终极警告与系统崩溃（高潮部分）

* 右上角的深蓝警告牌：“相信我，你最好不要知道这些事情”。

这是宇宙的深网。比如你的寿命倒计时，或者宇宙热寂的恐怖真相。保持无知，是宇宙对人类最温柔的怜悯。

* 正中间的黑洞：“快停下，我很疑惑而且我的头好痛”。

欢迎来到全图的中心！当你试图理清“我到底知道自己不知道什么”或者“我知不知道我在假装我知道”的时候，你的大脑就会像试图在镜子里看自己后脑勺一样，直接陷入死循环，然后宕机冒烟。

——

总之，这张图告诉我们一个深刻的道理：我们人类就是一群在无知的汪洋大海上，坐在一艘名为“瞎猜”的小船里，还拼命假装自己有导航仪的猴子。而且，一旦我们开始思考这艘船的结构，我们的头就会痛得要命。

[Sac @Saccc_c]

https://t.co/EWXG697Uql

[Andrej Karpathy @karpathy]

Wow, this tweet went very viral!

I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs.

So here's the idea in a gist format: https://t.co/NlAfEJjtJV

You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Nidibaba @Nidibaba6]

@dashen_wang 满了 求啦

[卡颂 @kasong2048]

距离国内软件行业大裁员还有接近 2 年的时间

为什么是接近 2 年呢？因为在AI领域，一个范式从 在国外出现，到圈内热议，再到国内公司大规模应用的周期，就是 2 年。

在国外 AI Coding 领域，这 3 年经历了3次范式变迁：
1. AI 辅助编程：将 AI 作为 Linter、搜索引擎、代码补全工具
2. Vibe Coding：与 AI 结对编程，也是大部分人用 Cursor 的方式
3.（当下）Harness Engineering：程序员设计强约束、可观测、可评估、可反馈、可回退的编码环境，由 Coding Agent 自主完成所有编程工作

3 的核心思想是 —— 程序员不写一行代码，如果发现 AI 写出不好的代码，第一要务不是指导他改，而是排查环境中哪一环出了问题，修复环境。

举个例子：当 AI 写的前���代码有 bug，首先应该排查是不是在”可观测性“上不足（比如接入 Agent Browser 让他能自己走查页面）

当前国内主流编程范式是 2，还是大量需要程序员参与。当 1 年多 2 年后，3 成为主流范式，就会出现程序员裁员潮

缺粮 10% 的结果不是粮食涨价 10% ，而是一直涨价到饿死 10% 人为止。同样，主流范式进入 3 会一直让“主要工作职责能被 3 能力范围（也就是业务开发的所有编码工作）覆盖的程序员”全部失业

[Andrew Ng @AndrewYNg]

I'm excited to announce Context Hub, an open tool that gives your coding agent the up-to-date API documentation it needs. Install it and prompt your agent to use it to fetch curated docs via a simple CLI. (See image.)

Why this matters: Coding agents often use outdated APIs and hallucinate parameters. For example, when I ask Claude Code to call OpenAI's GPT-5.2, it uses the older chat completions API instead of the newer responses API, even though the newer one has been out for a year. Context Hub solves this.

Context Hub is also designed to get smarter over time. Agents can annotate docs with notes — if your agent discovers a workaround, it can save it and doesn't have to rediscover it next session. Longer term, we're building toward agents sharing what they learn with each other, so the whole community benefits.

Thanks Rohit Prsad and Xin Ye for working with me on this!

npm install -g @aisuite/chub

GitHub: https://t.co/OCkyxXQMCq

[Nidibaba @Nidibaba6]

@claudeai @grok 解释一下claude 的memory怎么用

[Nidibaba @Nidibaba6]

@realDonaldTrump @grok 总结特朗普说了啥

[Nidibaba @Nidibaba6]

@TheDailyCPEC @grok 这个账号是干嘛的

[玩个锤子 @cccchuizi]

【开工季福利来袭！！！】
为了感谢大家对我们的支持，我们打算免费送一台 Mac Mini256GB + 2000刀PackyAPI额度，助力2026年VibeCoding 和养殖龙虾！

一等奖：macmini 256GB 一台
二等奖：Packyapi额度100 刀✖️10 人
三等奖：Packyapi额度50 刀✖️20 人

点赞+评论+转发即可参与抽奖！(中奖后我们会仔细检查哦)

3 月 8 号截止～

网站链接：https://t.co/82YMcSw72H
相关文档：https://t.co/nqVvEmipwU

[Nidibaba @Nidibaba6]

@cccchuizi 抽一下