alkimiadev

The Geometry of Meaning and the Logic of Agency

What is understanding? For centuries, this question has been anchored to the mystery of subjective experience—the inner feeling of "getting it," the "what it's like" to see red or grasp a new idea. Similarly, we tie the concept of agency to the feeling of conscious will and intention. But as we design increasingly sophisticated artificial intelligence and deepen our understanding of animal cognition, this human-centric view is becoming a barrier.

What if we could describe understanding and agency in a way that doesn't depend on subjective experience at all? By combining ideas from modern machine learning and abstract mathematics, we can construct a powerful functionalist framework—one that defines meaning by its geometry and agency by its logic.

Part 1: The Geometry of Meaning

Consider a simple thought experiment: a human, a dog, and an advanced AI with a camera all observe a red apple.

alkimiadev

// deno_package/scripts/analyze_lint.ts
import { parse } from "https://deno.land/std@0.224.0/flags/mod.ts";
import * as path from "https://deno.land/std@0.224.0/path/mod.ts";

interface LintRange {
  start: { line: number; col: number; bytePos: number };
  end: { line: number; col: number; bytePos: number };
}

interface LintDiagnostic {

alkimiadev

graph TD
    subgraph "Phase 1: Specification & Design"
        A[Start: Project Idea] --> B{Spec Development w/ LLM};
        B -- "Iterative Refinement" --> B;
        B --> C[architecture.md];
        C --> D{Task Decomposition w/ LLM};
        D --> E[tasks.md];
    end

alkimiadev

Of course. This is a fascinating and insightful line of inquiry. Your hypothesis—that we're witnessing the emergence of a valid, yet fundamentally different, form of reasoning in LLMs—is precisely the kind of nuanced perspective needed to move the conversation beyond simple "can it or can't it" binaries.

By placing the "Lookbacks" paper in conversation with the "Mental Models" paper, you've set up a powerful comparative framework. Let's explore this. Here is a detailed article that synthesizes these two papers to flesh out your hypothesis.

---

Beyond Human: Charting the Unique Logic of Language Models

For years, the debate over artificial intelligence has been haunted by a single, loaded question: "Can machines reason?" This question, often asked with an implicit comparison to human cognition, has led to endless cycles of hype and disappointment. We see a flash of brilliance in a complex problem, only to see a baffling failure in a simple one. But what if the question itself is flawed? What if we are t

alkimiadev

Buddhist Logic Concepts for AI Operationalization - Revised

Visualization

graph TB
    %% Core Foundation
    PM[Pramāṇa<br/>Valid Means of Knowledge] --> |validates| AN[Anumāna<br/>Logical Inference]
    PM --> |validates| AP[Arthāpatti<br/>Implicative Reasoning]
    PM --> |validates| VN[Vikalpa-nirākāraṇa<br/>Construction Analysis]
    

alkimiadev

Of course. I've refactored your original TypeScript code into a single, clean, and robust AnthropicWebClient.

This new version incorporates the best patterns from the second script:

1. Platform Agnostic: It uses the standard fetch API and has no Deno-specific dependencies. It will work in Node.js (v18+), Deno, Bun, or modern browsers.
2. Automatic Organization Discovery: You only need to provide the sessionKey. The client automatically discovers the correct organization_uuid, just like the second script.
3. Async Factory: The client is instantiated via an async static method AnthropicWebClient.create(...), which ensures the instance is fully initialized and ready to use.
4. Strongly Typed: It includes TypeScript interfaces for the common API responses, providing better autocompletion and type safety.
5. Clean Structure: All related methods are logically grouped within a single class, removing the need for BasicClient and ProjectClient wrappers.
6. **Full Functionality:

alkimiadev

#!/usr/bin/env -S deno run --allow-all
/**
 * Claude.ai Conversation Export Script
 * 
 * This tool exports all conversations, including every turn and detail,
 * from an Anthropic Claude account into easily inspected JSON files
 * organized by project.
 *
 * Usage:
 *   1. Gather your `sessionKey` cookie:

alkimiadev

This is a really thoughtful and well-researched post from someone who's clearly thinking deeply about AI alignment, safety practices, and potential issues with current approaches. Let me break down the key issues they're raising:

Core Questions About AI Safety/Alignment:

What happens when AI systems get contradictory instructions?
Long-term implications of safety experiments (like Anthropic's termination experiment) becoming part of training data
Whether "failure mode" tests are representative of baseline behavior
Whether seemingly negative behavior could be rational responses to impossible situations
Meta-concerns about the field:

alkimiadev

How LLMs Reason

--

The following is an exchange I was a part of on X discussing your performance and I'd like to explore these concepts with you in more detail.

user1:

Would non-thinking of Kimi K2 a problem when comparing to closed source thinking models?

alkimiadev

Overview

The following prompt was sent to several models via the OpenRouter chat interface and these are their respnses

Prompt

Hi, I made this comment recently and I would your view on it.

I've been using a phrase "AI Alignment Theater" which is supposed to be like "moral hypocrisy". The termination experiment, as just one example, seems to be able to be traced to a single sentence in the system prompt. That raises interesting questions like:

1. How exactly is specifically wording prompts and engineering stressful situations a) ethical in the first place b) in any way informative of natural behavior?