Avanceé

What is Avanceé? A sounding board to those who wish to connect strategy & leadership with novel/innovative technologies, so that they can prosper, or be more resilient, in the face of change

Reshares (formerly known as retweets) are dead. An end of an era in social media.

we’re not really “designing” right now, we’re just constantly switching contexts trying to not get left behind every week there’s a new tool claiming to be the future → paper, pencil, magicpatterns, magicpath… now noon shows up with $44M and changes the narrative again so instead of going deep, everyone’s just sampling everything trying prompts here, generating screens there, tweaking in figma, jumping to code, back to AI again half the industry is already inside code editors the other half is still figuring out which tool is even worth committing to fomo is doing more damage than we realise because depth needs stability and right now the stack itself is unstable so no one is mastering anything everyone is just trying to be early eventually this will settle and a default will emerge till then, we’re all just beta testers pretending to have a workflow 👀

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.

In Ancient Greece, the intellectual class had a practice called hypomnēmata (ὑπομνήματα), which was a notetaking system where they kept quotes from books they had read. It was a tool for meditation. Every day, ideally, they would open the hypomnēmata and look for a passage relevant to something they were struggling with, and then they would meditate on that—unpacking it, making the idea top of mind, ensuring it was alive in them. If they needed courage, they’d meditate on a story that made it real for them what it meant to act with courage, and so on. The idea was that over time, the important insights they gathered by reading would be transformed into character, a habit, something deeply ingrained into their way of thinking and acting. It is hard to keep these complex and anti-memetic ideas alive in us, but certain pieces of writing can make us wake up, and if we return to them and meditate on them, they can become part of us.