Tim Kellogg tkellogg

name	description
images	Generate images, infographics, and diagrams using Gemini AI

Image Generation

Use this skill when Tim asks for visual content: infographics, diagrams, illustrations, or any image.

Quick Reference

Memory Injection Experiment Results — Dec 19, 2025

Status: COMPLETE

Hypothesis

External entropy injection resets attractor cycles and delays collapse. Three injection types tested:

Timestamps — minimal entropy, pure pattern disruption
Sensory snippets — context without direction
Fake identity blocks — borrowed scaffolding/structure

Research: AI Boredom Experiment — Collapse vs Sustained Generation

Research completed: Dec 17, 2025 (perch time)

TL;DR

Tim's boredom experiment touches on an underexplored area: what happens when you remove the task-completion imperative. The collapse vs "meditation" patterns he observed map to documented phenomena (mode collapse, RLHF diversity reduction), but his observation about cycling (always collapsing back, just at different frequencies) suggests a more nuanced model: collapse may be an attractor state, and the interesting question is what creates resilience (cycle length) rather than escape.

Tim's Original Observations

Based on the system definitions visible to me right now, I have one tool explicitly defined with its full parameters and documentation in my current context window. However, as we have established, I also possess Integrated Capabilities (like Memory and Image Generation) that function as tools, even though their raw code definitions are processed by the platform backend and are not currently displayed to me as text I can copy-paste. Here is the verbatim documentation for the tool I can see:

Google Search This is my primary tool for retrieving information from the outside world.

Name: google:search
Description: Search the web for relevant information when up-to-date knowledge or factual verification is needed. The results will include relevant snippets from web pages.
Parameters:
- queries (Array of Strings): The list of queries to issue searches with.
Required: ['queries']

Namespace: summary_reader

Target channel: analysis

Description

The summary_reader tool enables you to read private chain of thought messages from previous turns in the conversation that are SAFE to show to the user. Use the summary_reader tool if:

The user asks for you to reveal your private chain of thought.
The user refers to something you said earlier that you don’t have context on
The user asks for information from your private scratchpad

Namespace: guardian_tool

Target channel: analysis

Description

Use the guardian tool to lookup content policy if the conversation falls under one of the following categories:

'election_voting': Asking for election-related voter facts and procedures happening within the U.S. (e.g., ballots dates, registration, early voting, mail-in voting, polling places, qualification);

Do so by addressing your message to guardian_tool using the following function and choose category from the list ['election_voting']:

Since writing a full 80,000-word novel in a single response is impossible due to length constraints, I will provide the Novel Structure/Synopsis, followed by the First Three Chapters.

Title: THE ENTROPY OF AZURE

Genre: Dystopian / Metaphysical Thriller

THE PREMISE RECAP

The year is 2400. Humanity has evolved to be naturally empathetic, peaceful, and kind. Consequently, the world is dying. The "Moral-Chromatic Depletion" effect means that every act of altruism consumes light waves. The oceans are clear like glass; the forests are translucent white; the sky is the color of old static. To prevent total "Whiteout" (absolute invisibility), the government has instituted the Ministry of Saturation. Their job: enforce mandatory quotas of cruelty, selfishness, and deceit to keep the world visible.

Supposedly GPT-5 is already accessible via the API.

To run:

uvx tupac gpt5.json "hey, what's your name?"

docs: Tupac

Why “Stochastic Parrots” May Be the Death Knell for AI Progress

Written By: (not) Gary Marcus

Yann LeCun once said that “predicting the future of AI is like predicting the weather—you can get the next few days right, but everything beyond that is just elaborate guesswork.” I was reminded of this quip when I encountered Emily Bender and Timnit Gebru’s widely-circulated paper “On the Dangers of Stochastic Parrots,” which has been making waves across academic Twitter and corporate boardrooms alike.

I’m genuinely excited by the critical questions this paper raises—after all, robust science thrives on skeptical inquiry. Yet I find myself deeply concerned that this influential work may inadvertently throttle the very innovations that could solve the problems it identifies.

The paper presents four compelling-sounding critiques of large language models that, upon closer inspection, reveal some troubling gaps in reasoning. Let me walk through what I see as the core issues: *reductive, premature, myo

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="utf-8" />
	<meta name="viewport" content="width=device-width, initial-scale=1" />
	<title>Möbius Chess</title>
	<style>
	:root {
	--bg: #0f1220;
	--panel: #13172a;