[This is a guest post by Nestor Guillen, crossposted from his blog. This blog post was initially written in a different file format and converted using AI. — T.] Keywords: LLMs, cultural tech…
How Do We Talk about LLMs?
By Jim Clifford It is never fun to watch friends argue. Generative AI has created multiple fractures across our universities and the community of historians. The stakes are high and we are all deal…
The most important thing when working with LLMs
Blog post: The most important thing when working with LLMs by Steve Klabnik
Why do LLMs make stuff up? New research peers under the hood.
Claude's faulty "known entity" neurons sometimes override its "don't answer" circuitry.
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
Brandon Stewart on Twitter / X
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
Scientists Say LLMs Appear to Be Acting as a Cognitive Virus Among Humans
AI's wide proliferation shares some striking similarities with the ways viruses spread, as an international team of researchers argue.
Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via...
Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptation is generally...
The two worlds of programming: why developers who make the same observations about LLMs come to opposite conclusions
Writing at the end of the world, from Hveragerði, Iceland
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)
Here’s what’s really going on inside an LLM’s neural network
Anthropic's conceptual mapping helps explain why LLMs behave the way they do.
LLMs Are Accelerating the Open Source Sustainability Crisis
Adam Wathan, the creator of Tailwind CSS, has been speaking about how LLMs have made his project more popular than ever…while also making it fall apart financially, causing him to fire 75% of his engineers. “I think AI is a huge reason why our business is struggling, even though it’s making Tailwind more popular than ever.” — Adam Wathan (12:03) How is this possible? A major cause is that LLMs are blocking Tailwind’s creators and users from forming a relationship with each other.
Illusions of Understanding from Outsourcing Thinking to LLMs
Some illusions of understanding are an inevitable part of the research process, while others can be avoided or overcome by careful critical thinking and observation. We are facing an increased risk of avoidable illusions as more research activities are delegated to large language models (LMM). LLMs can be useful but they cannot think, and their use can undermine our thinking and understanding. Thinking for ourselves is hard and error prone but worthwhile - and there are no shortcuts to understanding.
