Edmo Lima @soedmo

@biimolina

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.

Que espetáculo

Corinthians @Corinthians

4 months ago

O mais novo louco do bando! Bem-vindo ao hospício, Jesse Lingard. 🖤🤍 Garanta o manto do novo camisa 7️⃣7️⃣ do Timão: bit.ly/Lingard-77-Cor… #BemVindoJesseLingard #DoReinoÀFavela #VaiCorinthians

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I've spent 2.54 BILLION tokens perfecting OpenClaw. The use cases I discovered have changed the way I live and work. ...and now I'm sharing them with the world. Here are 21 use cases I use daily: 0:00 Intro 0:50 What is OpenClaw? 1:35 MD Files 2:14 Memory System 3:55 CRM System 7:19 Fathom Pipeline 9:18 Meeting to Action Items 10:46 Knowledge Base System 13:51 X Ingestion Pipeline 14:31 Business Advisory Council 16:13 Security Council 18:21 Social Media Tracking 19:18 Video Idea Pipeline 21:40 Daily Briefing Flow 22:23 Three Councils 22:57 Automation Schedule 24:15 Security Layers 26:09 Databases and Backups 28:00 Video/Image Gen 29:14 Self Updates 29:56 Usage & Cost Tracking 30:15 Prompt Engineering 31:15 Developer Infrastructure 32:06 Food Journal

@luissfabiani Ele e Jacaré são 2 moleques que parecem ser muito, mas muito gostaveis

@luissfabiani @JoaoJacetti sabem muito!

@CR7Brasil Não volta, ele tá vivendo o sonho dele 😅 cnnbrasil.com.br/esportes/futeb…

@Vessoni Pensei que era mensagem do papa

@tracklist @AnnenMayKanTr

@igorprvieira Boa, o que faço pra evitar esse CI ficar distante, eu uso essa pratica + trunk based: world.hey.com/niko.heikkila/… Garante que mesmo em draft tenha o beneficio da visibilidade que você citou e CI/CD constante já que os ciclos se mantem curto

@igorprvieira No fim são problemas diferentes, mas vale pensar em práticas de engenharia que evitem o overload do time. Importante é garantir que vocês achem o que funcionam pra vocês e fico feliz de saber que vocês vem construindo com um olhar mais sustentável

@igorprvieira Eu só não curto muito esse tópico do draft, a teoria de pegar feedback faz sentido... Mas o que acontece é que essa PR fica longe da branch main por muito tempo. Então se você tem uma cultura de CI forte, talvez esse draft não faça tanto sentido

@whindersson @Poobretion te ensinou bem hein!

@LeFreit4s E depois Pé De Serrita, ai quando tiver ali na malvadeza você põe ali um Dominguinho

Introducing docdiff 🛠️ A compact Rust command-line tool for comparing `.txt` files using the document distance algorithm. Ideal for swift similarity assessments. 🔗 Check it out: github.com/edmolima/docdi… #RustLang #CLI

@mattpocockuk What about documentation for this cli/lib/package? I suppose that in general, you have a choice also, right? I loved your video in 2023, maybe a new upgrade should be added with a documentation option to this guide. And last but not least, thanks by this article

São apenas palavras que descrevem uma passagem de 8 meses que vai marcar pra sempre minha vida! Obrigado Fiel, Tamo Junto, vocês ganharam mais um maluco para esse bando! Até Breve! #vaicorinthians #lomejorestaporvenir

@brunoformiga Dos que vi jogar: Maldini, respeitado fora e dentro do campo, líder técnico e tático

@Jujuca1987 Cinema puro !

@tatimantovani 😂😂😂😂 Tati eu vou torcer, orar para todos os deuses pra um dia você fazer uma exclusiva com ele! Ele é muitooo bommm 😂😂😂😂😂