im132nd

私もそろそろLLMやってみようかなー！？

[Sakana AI @SakanaAILabs]

🐡 Sakana Fugu リリース 🐡
マルチエージェントを指揮する、一つのモデル

ブログ：https://t.co/8Bac6JpVEN

本日、Sakana AIは新しいプロダクト「Sakana Fugu」の提供を開始しました。Sakana Fuguは、最先端のモデル群からなるマルチエージェントシステム全体を、単一のモデルAPIとして提供します。

難しいタスクは、強力なモデルを一度呼ぶだけでは解けません。どのモデルを使い、タスクのどの部分を委譲し、どう検証し、強みをどう組み合わせるか。Sakana Fuguは、その「協調の仕方」そのものを学習した言語モデルです。一つのAPIにリクエストを送るだけで、背後で専門モデル群を自動的に連携させ、フロンティアレベルの性能を複雑な運用なしで引き出します。

詳しくはこちら： https://t.co/hhO6qTawgb 🐡

[im132nd @im132nd]

🥲

[im132nd @im132nd]

ちょっとドキドキしながら初めてcodexのbrowser useに買い物をさせた

[im132nd @im132nd]

技術的に面白いと思ってる話したいことが一杯ある、みたいな人に会うとめっちゃ元気になれる

[im132nd @im132nd]

いました。7号館さよなら。

[im132nd @im132nd]

@crcrpar あざす次回試す

[im132nd @im132nd]

喉が痛すぎて銀翹散、のどスプレー、ヴィックスなどを服用しまくってたら今度は滅茶苦茶お腹痛くなってワロタ……

[im132nd @im132nd]

ObsidianとCodexでこれっぽいの試してる https://t.co/IFfNz2z0dM

[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.

[im132nd @im132nd]

これいい感じかも https://t.co/BCAFkqcKx6

[im132nd @im132nd]

https://t.co/GSFmztVOTd
これちょい変わり種でメチャ面白い&なんとなく思ってたことがうまく言語化されてる

[Epoch AI @EpochAIResearch]

Superstar AI researchers are paid >10× more than their frontier lab colleagues, and >100× more than most postdocs. Why?

The naive explanation is that this is just due to differences in researcher quality. But in a new essay, @ansonwhho argues that this is very incomplete.

[im132nd @im132nd]

皇居乾通り一般公開

[im132nd @im132nd]

@alfredplpl まさしも出来れば行ってみたいと思っていたところでした！

[im132nd @im132nd]

宇都宮みんみん

[im132nd @im132nd]

これ一発で完了して神

[im132nd @im132nd]

@upura0 なんと、ニアミス！そして誰もいなくてめっちゃいいですね、自分も次はカメラ持って朝イチに行ってみたいです

[im132nd @im132nd]

迎賓館赤坂離宮

[im132nd @im132nd]

Qwen3.5を見た俺「GDNとSWAその他との比較くれ」
翌々日のGLM-5 Tech Report「あいよ」
でちょっと草