Brook Miller

I've been working similar to this for ~4 months. Definitely try it because I think it's the combination of features that makes it work so well. NotebookLM is really close to this on a subject by subject basis, but... The obsidian web clipper is part of the magic (so much better than notion). I also had claude write a gui over ffmpeg that let's me record all my meetings (I have a claude skill that creates transcripts with Deepgram).

Here's what I'm currently pondering: This idea, but implemented totally in the cloud for normies. One could imagine building a virtual file system on top of the cloud-hosted data (so it looked to the LLM like a navigable directory tree of files). That could be done with a super simple SKILL implementing the base file system primitives. Next step would be to make it multi-player so businesses/teams could use it. Content is default private (each user has their own directory). Any individual directory/file could be tagged/shared to a team, to the company or to the world (public). Make it available via API, MCP, CLI etc. so you could unless something like OpenClaw against it if you wanted. I even have the domain for it: secondbrain .com What do folks think?

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.

@asanwal Tyler Cowen's pretty good at this @tylercowen

What’s the podcast where the interviewer pushes back and asks hard questions but in a non-combative, gotcha way? So it’s a good discussion and not just fluff. Anyone?

@patio11 Have you filled out complete medical history ten times to send kids to various summer camps?

This has been metastasizing in private industry, too, in part as regulations obligate suppliers to bind their own suppliers to the regs, and since many large firms sell to the government in at least one division and can't internally segregate processes, it propagates outwards.

So much this... Read Atlas Shrugged so you can have burned into your mind "show don't tell" as Rand delivers unreadable multi-page speeches of her philosophy over and over.

Wrote up about my personal journey from AI skeptic to someone who finds a lot of value in it daily. My goal is to share a more measured approach to finding value in AI rather than the typical overly dramatic, hyped bait out there. mitchellh.com/writing/my-ai-…

💫 Agentic Coding Trends Report by @AnthropicAI → AI compresses SDLC into shorter loops → Multi-agent systems replace single-agent flows → Agents execute multi-day build cycles → Human review focuses on edge cases... 👇 resources.anthropic.com/hubfs/2026%20A…

@TheStalwart If academic papers aren't your thing you can hear @twiecki (one of the authors) and @hugobowne discuss on the vanishing gradients pod. Building GenAI Systems That Make Business Decisions with Thomas Wiecki (PyMC Labs) youtube.com/watch?v=so4AzE…

Great observation, Joe! "Large language models (LLMs) [removed] produce unrealistic response distributions when asked directly for numerical ratings." - from arxiv.org/abs/2510.08338 That's a really interesting solution you (and claude) developed. The paper does a good job of explaining other techniques to get LLMs to come up with more nuanced quantitative scores:

1/ This is actually a very good question, and answering this helped me learn something important about model training. The problem, one will encounter, if they do this enough, is that AI models will end up being very bi-model. Almost everything will score either hi or low...

Who is he talking to? Himself?

Apple has donated millions to the Trump Administration in the past year. Tim Cook's last post was honoring MLK a week ago. Pretty galling to offer a tribute to a long-dead icon's "commitment to justice" when you are funding injustice today.

I try to not talk about politics. I generally believe the best way I can serve the world is as a non-partisan expert, and my genuine beliefs are quite moderate. So the bar is very high for me to comment. But recent events – a federal agent killing an ICU nurse for seemingly no reason and with no provocation – shock the conscience. My deep loyalty is to the principles of classical liberal democracy: freedom of speech, the rule of law, the dignity of the human person. I immigrated to the United States – and eventually cofounded Anthropic here – believing it was a pillar of these principles. I feel very sad today.

after working with claude all morning on a detailed plan... can you run each of the steps in the plan as a sub agent where each gets a a fork in the conversation from this point while you monitor and don't pollute your context with the details thanks @trq212 for your great tweets on getting the most from claude

@johnofa @ada_rob Don't hold your breath...

Thanks @ada_rob for speaking out. No matter how large or small your following, if you’re in tech and you’re appalled by this barbarism, please post or retweet! Decent people have been Silicon Valley’s silent majority for too long.

Please more tech leaders speak out

@egrefen Maybe some of these guys could be like Jeff?

These are times which separate the wheat from the chaff. Those brave enough to speak their moral revulsion to the unwinding of a society based on law and order and decency are to be lauded. Those who stay silent or defend it in the pursuit of wealth or worse are not. Be like Jeff

Lonsdale and Sacks and Luckey get a lot of attention for their Trump sycophancy. But the vast majority of tech leaders are decent humans and know they must patriotically speak up. Thank you, Jeff.

A bunch of chronically online tech leaders suddenly quiet.

So there's a small SaaS I pay ~$100/year that shows testimonials on my site. I could have build it, but didn't seem worth the time. The site got sold to someone else, and they cannot send me my invoice for the life of them. So I am now rebuilding it with an LLM, ripping it out.

World's richest man.