
Alex Kessinger
8K posts

Alex Kessinger
@voidfiles
code@Stripe after work Water Polo Coach. Systems, People, Outcomes. Views are my own. co-host https://t.co/MlhQyKuHIe





If you're looking to improve your writing game, Anh is one of the most consistent heavy hitters I know in devtools HN and she literally just open sourced her writing Skills template for you to use below!

Not surprised that everyone is finding the Claude-Obsidian setup mind blowing. I've been using this set up for the last few months and it's totally changed my daily habits and how I think about using AI. Some tips: 1. You HAVE TO build a daily commonplace practice. The more observations, epiphanies, thoughts, and questions you can jot down, the more context your agents will have on how you think, make decisions, what matters to you, etc. These notes don't need to be clean or structured. Optimize for whatever lets you just get the thing down on paper. You're doing it right if your notes are all over the place...that's how our brain works. My rec is to voice transcribe as much as you can because it's easier to say more. Say your ifs, buts, and ums. They're actually more helpful in context. You're letting your agent know what you are and aren't sure about. The key thing to note about @karpathy's LLM wiki base is that it's fantastic for research projects - where it makes sense to have synthetically produced artifacts. But if you're trying to use "Claude + Obsidian" as a personal thinking tool, then you can't outsource your own life experiences to AI. That personal context you add is quite literally what makes the tool useful for you. After doing this for 9 months, I can say for sure the context compounds. If you can just keep the habit going for a few weeks, you'll start noticing patterns that surprise you. 2. Go learn about @andy_matuschak's atomic note taking practice. My big takeaway here is that thinking in atomic notes makes it easier to pull contexts from different parts of your life that you would normally modularize. The notes aren't facts/names/dates but rather concepts. I love to think about them like API endpoints you call when trying to solve through problems in your professional or personal life. Remember, as @paulg says "writing is thinking". Just write these notes for yourself in a way that makes sense to YOU. You'll start changing how you perceive your own thoughts and thinking style. Eventually, you'll get to a point where you have so many atomic notes that you dictate a train of thought to Claude and it'll go through your vault and pull your own thoughts from the past to help you out...it's a magical experience. 3. Connect @readwise or Obsidian Web Clipper to build an intentional reading habit. Since starting this setup, I've realized how much of my content consumption in the past was cope. I thought I was being productive by listening to podcasts all the time or scrolling on Substack instead of X. I now am *extremely intentional* about the content I read and how I read it. @phokarlsson has a post on this called "Structure your milieu". Ask yourself, who gets access to my brain? Read their writing religiously and block everyone else out. Your time scrolling should equate to searching for potential reads you want to add to the backlog, that's it. And your "consumption time" should be you in Reader highlighting and taking notes obsessively. Even a simple 5 min Substack read should be treated as an opportunity to create new atomic notes, connect previous ideas, etc. The Readwise Obsidian plugin then lets you pull all your highlights that you can then connect to other parts of your vaults in meaningful ways. Remember, the more you read with intention, the more context you'll have to add to your "second brain". You can't automate that part. I haven't used @kepano's Obsidian Web clipper yet but going to try this week. His recent Youtube demo looked amazing. 4. Build an agentic constitution (.md) This is effectively the portal between your Obsidian and Claude. I have a detailed post on how I did this + a template you can feed to your own AI of choice to build for yourself (see in reply below). But this file is you letting your agent know all the context it needs to know about you at a high level: who you are, your big question, how you like to work, your main goals, current problems, context on your team, and so on. Anytime I have the agents do work for me, the first step is to go read the constitution so it acts in alignment with what I'm looking for at this stage in my life. Key note here is that it's a constant WIP doc. I'm almost updating this weekly so that Claude knows what's changing in terms of focus and prioritization. The more you think about this doc like you're updating your team, the more useful it will be. 5. Create Knowledge Work PRs This is the coolest part in terms of Claude integration but it only works if you have meaningfully spent time on the steps above. I've slowly been adding subroutines to my vault. Call them skills or agents it doesn't matter. To me, they're equivalent of writing methods in a code repo. Each subroutine has a specific set of instructions on what to do. And they all start by reading the agentic constitution. For example, one of mine is called "Tend the Vault". Its job is to check all my daily commonplace entries since I last triggered it, notice any key threads and insights, tell me which atomic notes I need to update, and create a draft of what to add. Then I'll go through a 5 minute voice session where it takes me through edits one by one and I'll approve, tweak, or reject them. Once we go through the changes, it will go and updates all the respective md files. I call this section "Knowledge Work PRs" because that's exactly what they are. You run your subroutines, the agents go do the work, and then you sit down and just like a manager go through all the PRs and decide what gets to go through production. Ok this is getting pretty long so I'll stop here but if you made this far, I HIGHLY RECOMMEND you go through my Substack Engineering Agency. I've detailed every single one of my steps on how I build a better thinking, commonplacing, reading, writing, and second brain practice. See the replies below for links.



A look inside an engineering team @OpenAI with @dylanvee Dylan Vassallo is an engineering manager at OpenAI leading the People Innovation Labs team -- an eng, product, and design team embedded within the People org, building AI-powered tools for recruiting, HR, internal comms, and the employee experience. In this episode, Dylan talks about what it actually looks like to be an engineering team inside a frontier AI company, how non-engineers are becoming builders, and what the "graduation" process looks like when a prototype needs to become a production system. With hosts @voidfiles and @davidnoelromas



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.


Last week I wrote about the hardware side of the AI memory problem: the HBM bottleneck and why you can’t just build your way out of the supply crunch. Immediately after, Google published something that attacks the exact same problem from the other direction. They realised AI might not need more RAM, but better math. This week, I went down the rabbit hole of TurboQuant, Google’s answer to LLM KV cache compression. Think of it like Pied Piper’s middle-out algorithm from Silicon Valley, but for AI memory. It aims to achieve massive compression without trading off accuracy. The same way Claude releases kill the market cap of some sectors immediately, this announcement from Google has crashed memory companies stock prices. Is this an over-reaction, or a complete shift in the economics and resource requirements of AI labs? Link to post: adlrocha.substack.com/p/adlrocha-wha…







