







1/ New @Nature! We study how powerful institutions shape the information environment for LLMs. Commercial LLM training is opaque, so we trace a path from state-coordinated media -> training data -> model responses. pic.twitter.com/5LdFvzbFaf— Brandon Stewart (@b_m_stewart) May 13, 2026
Dan Shipper 📧 on Twitter / X
this is true and is a big reason why you don’t need to be a highly technical researcher to use LLMs in surprising and novel ways https://t.co/TuxNzXzToU— Dan Shipper 📧 (@danshipper) July 27, 2025
elvis on Twitter / X
Diagram of the LLM Knowledge Base system.Feed this to your favorite agent and get your own LLM knowledge base going. https://t.co/4AQSFOv4PV pic.twitter.com/nPSNi4Ayqv— elvis (@omarsar0) April 3, 2026

Curated retrieval versus open web search in public AI information...
Public institutions increasingly use large language models (LLMs) to answer citizens' questions, often pairing a curated knowledge base with live web search, yet whether the sources behind these...

Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Info Ops
LLM Knowledge Bases
A visual breakdown of Andrej Karpathy's approach to building personal knowledge bases powered by LLMs. Learn the 4-phase pipeline: ingest, compile, query, and maintain - with an interactive architecture diagram.

Guardian Angels: LLM Personalization for Productivity and Security
I propose an approach for highly personalized LLMs, for near-future productivity gains and personal info/cybersecurity against increasingly powerful LLMs: they should, in the spirit of uploading, try to emulate the user’s values and preferences in order to amplify the principal—not replace them. I discuss a package of techniques and proposals to accomplish such ‘guardian angels’; dynamic evaluation of LLMs combined with active learning and elicitation and heavy inner-monologue search/data-augmentation.

ImportAI 449: LLMs training other LLMs; 72B distributed training run; computer vision is harder than generative text
Will AI cause a political interregnum

Dria on Twitter / X
Introducing Inference Arena v2.0.An agentic experience that searches, analyzes, and delivers insights about LLM inference.When we first launched, our goal was simple: make it easier for developers to compare models, engines, and hardware without digging through scattered… pic.twitter.com/fgWgos48lW— Dria (@driaforall) September 30, 2025
The most important thing when working with LLMs
Blog post: The most important thing when working with LLMs by Steve Klabnik
Take caution in using LLMs as human surrogates | PNAS
Recent studies suggest large language models (LLMs) can generate human-like responses, aligning with human behavior in economic experiments, survey...

The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0)
AI Large Language Model Training: The Potential Risks of Ideological Skewing — PSG Consulting
LLMs (AI Large Language Models) have become part of everyday life. Systems such as ChatGPT, Claude, Gemini, Meta AI (Llama) and X.ai's Grok handle billions of interactions daily. They increasingly shape what information people encounter and in what order, subtly deciding what's important and even what is true, sometimes without users realizing it. Because LLMs wield growing power over information exposure, it is vital to recognize the political and ideological structures at multiple stages of their design, and to identify manipulation risks.

Training great LLMs entirely from ground up in the wilderness as a startup — Yi Tay
Chronicles of training strong LLMs from scratch in the wild

himanshu on Twitter / X
and here is the full architecture of the LLM Knowledge Base system covering every stage from ingest to future explorations. https://t.co/Wmn48gB0g0 pic.twitter.com/ObJet8Esfu— himanshu (@himanshustwts) April 2, 2026

Meet the Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases
Meet the Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases

1/4 Do LLMs understand? "They understand in a way that’s very different from how humans understand," Dileep George, @dileeplearning.bsky.social, of Google DeepMind at the Simons Institute workshop on The Future of Language Models and Transformers. Video: simons.berkeley.edu/talks/dileep-george-google-de…