Connected to AI

🕸️

[Khairallah AL-Awady @eng_khairallah1]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

In 45 minutes she shows exactly how to build an agent that improves itself.

Most people are still doing all of this by hand.

Watch the session, then save the guide below.

[DENIRO @polyesp]

🚨 CLAUDE AHORA PUEDE HABLAR CON TU PROPIA VOZ. EN TODOS LOS IDIOMAS. GRATIS

un desarrollador subio a Github un repo llamado jamiepine/voicebox

te clona el tono exacto con un par de segundos de audio

funciona directo en tu pc asi que tus datos son cien por ciento privados

puedes armar contenido en japones arabe o polaco sin mover la boca

guárdalo para cuando lo necesites

[Codez @0xCodez]

Anthropic just dropped 5 workshops, revealing the latest capabilities of Fable 5:

• 00:00 - deep look into Fable 5
• 11:22 - Fable 5 and the capability curve
• 30:54 - building managed agents with Fable 5
• 44:29 - real use cases of Fable 5 by teams
• 57:43 - how to deploy agents with Fable 5

These 1-hour of sessions will replace 100 articles on how to actually use Fable 5.

Watch them today, then read the best practices from the sessions in the article below.

[Anatoli Kopadze @AnatoliKopadze]

Anthropic engineers just showed how they build a full app from scratch, using a loop of agents

40 minutes from the team behind Claude Code

they used three agents: one to plan, one to build, one to judge, cycling until the app actually works

the winners won't have the smartest model, they'll have the best loop

watch it, then read the full guide on how to actually use loops below

[Kylian Mbappé @KMbappe]

Madame Celeste Amarilla,
Vous êtes une femme méprisable et indigne de sa fonction.
Vous ne représentez pas le Paraguay, ce pays qui a transpiré la passion et l’honneur tout au long de la compétition. Par votre inconscience et votre racisme décomplexé, le monde entier a déjà oublié le parcours et l’effort historique que vos joueurs ont réalisés durant cette coupe du monde pour laisser place à une dame incompétente donnant la pire image possible de son pays.
Je ne laisserai jamais aux gens comme elle, la liberté de laisser propager leur haine et leur racisme à travers le monde.

[Connected to AI @ConnectedtoAI]

🤣

[0xMarioNawfal @RoundtableSpace]

The Hugging bay, The Pirate bay for open LLM's, model weights downloadable with torrents.

This is a game changer.

https://t.co/nuZQ68aluH

[Connected to AI @ConnectedtoAI]

@OfficialStve @tchebaloo A l’époque = 2025 🤣

[Connected to AI @ConnectedtoAI]

@2Dinu83028 C'est une vidéo IA
Si tu remarques bien la première voiture qui roule à contresens dans la voie d'arrêt d'urgence (c'est les barrières d'autoroute visible qui me l'indique) et si tu zoomes bien sur le passager de cette voiture, c'est flou et ça roule sur la ligne du tracé au sol.

[Connected to AI @ConnectedtoAI]

La compétence la plus sous coté du monde : copier coller

[Sac @Saccc_c]

这是我见过最好的Claude Code+Obsidian的教学视频。

花一个小时看完，你将学会用 Obsidian 打造第二大脑——让 AI 记住你所有的想法，帮你挖掘规律、生成创意、落地执行。

[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.

[Sabronx | Deban GMC @sabronx]

Ah boooooooooon ?????????

[Connected to AI @ConnectedtoAI]

@BastiUi Incroyable

[Connected to AI @ConnectedtoAI]

@Cobey_Ecom @LucJager T’es bloqué ou

[Connected to AI @ConnectedtoAI]

@Cobey_Ecom @LucJager Si tu peux