Fabio Sammarco

Maximizing regretted user-minutes.

[Fabio Sammarco @F_Sammarco]

@ModeledBehavior @paulkrugman

[Fabio Sammarco @F_Sammarco]

@BoringBiz_ Why?

[Fabio Sammarco @F_Sammarco]

@TheStalwart @jackclarkSF What did Jack see in 2016?

[Fabio Sammarco @F_Sammarco]

@TheStalwart This is it folks. This is why we all get up in the morning. This is why we're all here.

[Fabio Sammarco @F_Sammarco]

@JohnHCochrane Prof. Cochrane: Are you becoming chartist? 😀

[Fabio Sammarco @F_Sammarco]

@chamath Sounds like Cursor auto mode.

[vik @vikhyatk]

you are only as good as your AGENTS.md

[Fabio Sammarco @F_Sammarco]

@paulg I remember reading one of your essays a few months ago and then asking an LLM to summarize it. Reading the essay itself gave me a lot of insights and new ideas, the summary had nothing interesting to say.

[Fabio Sammarco @F_Sammarco]

@Noahpinion If you get a PhD in 2026, get a STEM PhD.

[Fabio Sammarco @F_Sammarco]

@stephsmithio gaanntt

[Fabio Sammarco @F_Sammarco]

@patrickc Predictions might work in Mediocristan, but not in Extremistan @nntaleb

[Fabio Sammarco @F_Sammarco]

@fchollet A sign you need to open a new instance! @dexhorthy

[Effective Accelerationism @effectiveaccel]

What did you get done this week?

[Sam Altman @sama]

@BasedBeff you cannot outaccelerate me

[Cursor @cursor_ai]

We're partnering with SpaceX to improve Composer.

https://t.co/2mUZyykeJ7

[Lenny Rachitsky @lennysan]

"Using coding agents well is taking every inch of my 25 years of experience as a software engineer."

Simon Willison (@simonw) is one of the most prolific independent software engineers and most trusted voices on how AI is changing the craft of building software. He co-created Django, coined the term "prompt injection," and popularized the terms "agentic engineering" and "AI slop."

In our in-depth conversation, we discuss:
🔸 Why November 2025 was an inflection point
🔸 The "dark factory" pattern
🔸 Why mid-career engineers (not juniors) are the most at risk right now
🔸 Three agentic engineering patterns he uses daily: red/green TDD, thin templates, hoarding
🔸 Why he writes 95% of his code from his phone while walking the dog
🔸 Why he thinks we're headed for an AI Challenger disaster
🔸 How a pelican riding a bicycle became the unofficial benchmark for AI model quality

Listen now 👇
https://t.co/wlEIyOehU8

[Fabio Sammarco @F_Sammarco]

@EmanuelDerman What would that guy say today about models built by computer scientists?

[Fabio Sammarco @F_Sammarco]

@__paleologo You can get it used for $189.

[Marc Andreessen 🇺🇸 @pmarca]

The "AI job loss" narratives are all fake. AI = massive ramp in productivity = massive ramp in demand = massive jobs boom. Watch.

[Andrej Karpathy @karpathy]

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.

[Fabio Sammarco @F_Sammarco]

@karpathy @peteskomoroch Sounds like a sort of Grokipedia?