I've 2x’d my productivity as a VP of Product
@mercury by creating a "Second Brain" using 5 years of work history, 15k docs with 3.5 million words, and every tool in my stack. It runs locally, is a core part of my every use of LLM, and gets better everyday.
Today, I want to share the stack, the workflow, and the prompt to build it:
Background
I am a VP of Product for
@mercury, which is a long way of saying I'm in a lot of meetings, consuming a lot of content across different tools (linear, slack, notion, data analyses), and trying to make sure I actually get stuff done. Working at a company for 5 years and being an information addict, I am essentially a walking encyclopedia for Mercury post 2021-today -- but I've recently found that my scope + workload means I can't keep every plate spinning.
One day, I was scrolling X and came across a series of posts that caught my attention, starting with
@tobi's QMD. QMD is a local vector search, and then a few other posts started to show up that connected a few dots for me:
Claude Code launched hooks (per-event prompt injections)
GasTown / OpenClaw launched with the power of orchestrators writing memory + delegating to sub-agents (among many other patterns)
MCPs/CLIs hit a critical mass, and enough of my core tools were available without having to ask admins to give me API keys
@tylercowen did an interview and talked extensively about "writing for AI" in a way that struck a chord - how much output of work already exists that I'm not using?
I decided that it was time to build
Prep work (~1-2 hours end to end)
To start, I needed a library of all the content I could know about... so I downloaded every document I've ever created for my job at Mercury + any relevant product strategy, analysis, retro, reflection on execution, etc. This netted out to over 15k documents and 3.5 million words. Maybe I've read them all, but I've forgotten most. These became a folder that I just called "raw data", and I ran QMD to index this on my computer.
To see if this worked, I used Claude Code to ask about random memories and surprising insights from this knowledge base - the amount of delight/surprise I experienced in seeing how much more capable vector search was than text-based search gave me the confidence to keep going. I asked one questions about books that it would think I like, and it was spooky how good of recommendations it gave me. I think this is my best advice in this journey: test every step of the way! Easy to get caught in hill climbing a local maxima
Train my brain and connect it to my tools (~2 hours)
With all the raw data, I needed to help it make sense of me + what my goals are + the tools I used, so pursued three paths:
Explain myself - to be able to create a second brain, it needed to know what mine was doing. I wrote up a me.md explaining who I am (work + life), gave it my goals + performance reviews for the last 5 years + set of personal priorities. The most humbling part was the system pointing out that I've been making the same strategic mistake for years, according to my own performance reviews, and was making it that week as I was setting up the system
"Distill" the data - I spun up an agent team to use the me.md + the knowledge base to create a set of docs between me <> raw knowledge base. This idea largely came from the idea that LLMs regularly distill down smaller models to take tasks, and I had no idea if it would help me in this, but Agent Teams had just launched and so I had a swarm of them find the main "themes" we've worked on from the knowledge, give sourced histories of this, and summarize key lessons. These created a context.md folder
Tools - I use a few tools (Google Docs, Linear, Notion, Metabase) , and luckily most have connectors on Claude Code or these companies are actively launching MCPs/CLIs. A few didn't, but I spun up specific skills that crafted direct API calls to be able to complete tasks like "run a query for XYZ".
Claude had access to all the information about me + the tools I used + had a massive library of all my work, but did it really know anything? Does anyone?
Wire it up (<1 hour)
At this point, I had so many words + documents that it was time to actually find use or abandon ship. But I didn't want to have to go search this every time and that's when "hooks" caught my attention.
Hooks from Claude Code let you insert content into your prompt without needing to ask (or when a session starts, after a tool use, or when a session stops). Using the UserPromptSubmit hook, I enabled my Claude Code to use qmd to find names + topics + specific documents related to my prompt.
This is a nerd-out moment, but when searching for files in Finder, it is mostly a name + raw text search.... but QMD can help bring context into searches. My system is tuned to figure out a query, then returns results using one of two techniques:
vsearch (semantic/vector) — understands meaning of my question. "How's the funnel performing?" finds …
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