Kiwiii

Stay happy!

[Puppy Nation 🐶 @PuppyNationHQ]

This is the cutest thing you'll see today

[𝕐o̴g̴ @Yoda4ever]

Dachshund zoo..🐾😅

[Claude code研究ラボ @claudecode84]

Claude Codeは、これを入れるだけで別物になります。

Anthropic公式のプラグイン
「claude-code-setup」が静かに登場。
これがかなり強い。

プロジェクトを自動でスキャンして、

・必要なフック
・使うべきスキル
・MCPサーバー
・サブエージェント
・自動化の組み方

まで提案してくれます。

しかも、ただ提案するだけじゃなく、
セットアップまで順番に案内してくれる。

多くの人はClaude Codeを、ほぼ初期状態のまま使っています。

だから便利なはずなのに、
どこか散らかった使い心��になる。

でも本当に強いのは、Claude Code単���ではなく
その周りに組むエコシステム。

これは入れておいた方がいいです。

インストール：
/plugin install claude-code-setup@claude-plugins-official

忘れる前にブックマーク推奨

[Ramin Nasibov @RaminNasibov]

bonjour

[マジ大道芸人HAMAR(宇治市民) @magicianhamar]

暇を持て余した宇治市民の…遊び
#響けユーフォニアム
#久美子ベンチ

[みず @_32Mizu_]

スバベアかわいいーーーーー❣️🧡🩷

[Wallpapers @ioswallpaperx]

Cute wallpaper

[ななし @fv5b9x]

無限ピカチュウ

[Andrew Ng @AndrewYNg]

How we prompt AI is very different in 2026 than 2022 when ChatGPT came out.

I'm teaching a new course, AI Prompting for Everyone, to help you become an AI power user — whatever your current skill level.

It covers skills that apply across ChatGPT, Gemini, Claude, and other AI tools. How to use deep research mode for well-researched reports on complex questions. How to give AI the right context, including more documents and images than most people realize you can provide. When to ask AI to think hard for several minutes on important decisions like what car to buy, what to study, or what job to take. And how to use AI to generate images, analyze data, and build simple games and websites.

I also cover intuitions about how these models work under the hood, so you know when to trust an answer and when not to.

Along the way, you'll see flying squirrels, a creativity test, some of my old family photos, and fireworks.

Join me at https://t.co/tcQc4iJAJG

[コウ @kouh_sh]

北の地に大輪の花が咲きました🌸
五稜郭の桜、これが見たかった…🥰

[Kanata @asagi_photo0921]

廃駅に吹く初夏の風が優しい。

[『Re:ゼロから始める異世界生活』公式 @Rezero_official]

デジコン配布◪

TVアニメ「Re:ゼロから始める異世界生活」
4th season 放送を記念して
待ち受け壁紙をプレゼント✨

ラム・レム ソロver
ぜひご使用ください✨

▼ノンクレジットED▼
https://t.co/Cp6sVdFVVs
#リゼロ #rezero

[Corey Ganim @coreyganim]

Karpathy dropped a viral post about building a "second brain" with AI. We built it live in under 20 minutes.

The system:
1. Three folders: raw/ (brain dump), wiki/ (AI organizes it), outputs/ (query results)
2. One-click install: npx skills add NicholasSpisak/second-brain
3. Obsidian Web Clipper to capture any article with one click
4. Run "ingest" and the AI reads everything in raw/, builds wiki pages, and maps connections automatically
5. Query your own knowledge base like talking to an expert
6. Monthly lint command catches contradictions and gaps before they compound

Day 1 it's basic. Day 90 it's a company asset nobody else has.

Free skill in the YouTube description that scaffolds the whole thing in 60 seconds.

Video below, YouTube link in the replies.

[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.

[Wallpaper Atelier @Eryth_Studio]

Phone, iPad & Desktop Backgrounds 💻

[VIA @Via00Via]

Gemini愛用者です、あざます！
取り急ぎブクマぁぁぁぁぁぁぁああ！！！

[国山ハセン @hasenkuniyama]

AI時代、勉強量より「質問力」が価値になる。

MITの大学院生が
「1学期分の学習を48時間で終わらせる方法」を見せてくれた。

彼がやったことは意外だった。

教科書1冊ではなく

・教科書 6冊
・研究論文 15本
・講義の文字起こし

全部を NotebookLMに入れる。

そしてこう聞く。

「この分野の専門家が共有している
5つのメンタルモデルは何か？」

次に聞く。

「専門家が根本的に意見が割れている論点は何か？
それぞ��の最強の主張は？」

20分で
その分野の 知的地図 が出来上がる。

さらに彼はこう聞いた。

「この分野を
深く理解している人と
暗記だけの人を見分ける質問を10個作って」

そして6時間
その質問に答え続ける。

間違えるたびにAIに聞く。

「なぜ間違いか？
何を見落としている？」

48時間後
彼は教授と議論できるレベルになっていた。

AI時代の差は

コンテンツ量ではない。

質問の質。

多くの人はAIを
「高性能な蛍光ペン」として使っている。

でも本当は

“全ての文献を読んだ家庭教師” として使える。