First, let me quote my previous ancient post on the topic:

Effective Strategies for Changing Public Opinion

The titular paper is very relevant here. I'll summarize a few points.

• The main two forms of intervention are persuasion and framing.
• Persuasion is, to wit, an attempt to change someone's set of beliefs, either by introducing new ones or by changing existing ones.
• Framing is a more subtle form: an attempt to change the relative weights of someone's beliefs, by empathizing different aspects of the situation, recontextualizing it.
• There's a dichotomy between the two. Persuasion is found to be very ineffective if used on someone with high domain knowledge. Framing-style arguments, on the other hand, are more effective the more the recipient knows about the topic.
• Thus, persuasion is better used on non-specialists, and it's most

This isn’t primarily about how I write. It’s about how other people write, and what advice they give on how to write, and how I react to and relate to that advice.

I’ve been collecting those notes for a while. I figured I would share.

At some point in the future, I’ll talk more about my own process – my guess is that what I do very much wouldn’t work for most people, but would be excellent for some.

Table of Contents

1. How Marc Andreessen Writes.
2. How Sarah Constantin Writes.
3. How Paul Graham Writes.
4. How Patrick McKenzie Writes.
5. How Tim Urban Writes.
6. How Visakan Veerasamy Writes.
7. How Matt Yglesias Writes.
8. How JRR Tolkien Wrote.
9. How Roon Wants Us to Write.
10. When To Write the Headline.
11. Do Not Write Self-Deprecating Descriptions of Your Posts.
12. Do Not Write a Book.
13. Write Like No One Else

I’m considering translating my work into English to share it with the LessWrong community, but I’d like to first ask if it aligns with the community's interests and could be valuable. Below is a summary of the work to help evaluate its relevance:

Beyond HaHa: Mapping the Causal Chain from Jokes to Knowledge

Summary

We explore the specific causal mechanisms linking humor recognition to learning outcomes, including the computational and neurological pathways involved. 

This study began with a practical goal: to evaluate the use of humor as a pedagogical tool in Cardiopulmonary Resuscitation (CPR) courses through a randomized trial. However, the lack of clear criteria to define and operationalize "humor" in educational contexts led us to explore its conceptual foundations. Initially, we adopted Clarke's formula, which describes humor as "a pleasant...

Scheming AIs may have secrets that are salient to them, such as:

• What their misaligned goal is;
• What their takeover plan is and what coordination signals they use to collude with other AIs (if they have one);
• What good behavior looks like on a task they sandbag.

Extracting these secrets would help reduce AI risk, but how do you do that? One hope is that you can do fuzzing of LLMs,^[1] e.g. by adding noise to LLM weights or activations.

While LLMs under fuzzing might produce many incorrect generations, sometimes-correct generations can still be very helpful if you or the LLM itself can tell if a given answer is correct. But it’s still unclear if this works at all: there are probably some intermediate activations that would result in an LLM telling you the secret, but...