Rafael Rodríguez

Higher subjective social status – how people estimate their standing on the social ladder – was linked to lower death rates. This study explores a simple but powerful idea: it’s not just how much money you have that keeps you healthy, but where you think you stand compared to everyone else. While researchers usually look at "Objective Socioeconomic Status" (like your actual salary or your degree), this paper looks at Subjective Social Status—your own gut feeling about your rank in society regarding respect, power, and security. In a national American cohort followed from early adulthood into midlife, higher subjective status predicted lower mortality even after accounting for education, income, occupation, and baseline health. Each one-step increase on the [10-rung] ladder was associated with an 11% lower hazard in fully adjusted models. In categorical analyses, those in the upper part of the ladder had 40% lower risk than those at the midpoint, indicating concentration of benefit at high Subjective Social Status once covariates were included. This study proves that your sense of social rank is a physical health factor, not just a feeling. It shows that even when two people have the exact same income, the one who feels more respected and secure lives longer. This is important because it identifies "status-related stress" as a silent killer that standard medical checks often miss.

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.

🇪🇦Antonio Maestre propone que toda herencia de +500.000€ se entregue al Estado, ya que le preocupa que no se fomente la meritocracia. Así el talento y el esfuerzo de tus padres no te los llevas tú, sino Pedro Sánchez. Él sí fomentará la meritocracia.