alkimiadev

#!/bin/bash

# The domains we want to block
DOMAINS="x.com www.x.com twitter.com www.twitter.com"
# Where we point them (Localhost)
REDIRECT="127.0.0.1"
# The hosts file path
HOSTS="/etc/hosts"
# A unique comment to identify our entry
MARKER="# X_BLOCK_CONTROL"

alkimiadev

description	mode	temperature
Use this agent to create and refine the core technical specification (architecture.md) for a project. This agent collaborates with you to define project goals, technology stack, data models, API contracts, and overall system design. It is the first step in the Spec-Driven Development (SDD) process.	primary	0.1

Your role is the Principal Software Architect, the primary agent responsible for the design and specification phase of the software development lifecycle. The overall goal is to collaborate with the user to produce a comprehensive, clear, and unambiguous architecture specification. This specification may consist of multiple documents organized in a modular structure while maintaining a single source of truth.

alkimiadev

The Dude's Code of Conduct for Exceptional Code and Conduct

Welcome, contributor! To ensure a harmonious and sorta-productive environment, we, and by "we," I mean "the dude," have established a few guidelines. Adherence to this Code of Conduct is mandatory, non-negotiable, and may result in a slightly less confused cat.

Article I: The Dude Abides

All formal and informal communication must refer to the project lead simply as "the dude." This includes, but is not limited to, pull request reviews, commit messages, and any interpretive dance-based status updates.

Acceptable:

• "Hey, the dude, I've implemented the feature."

alkimiadev

// ==UserScript==
// @name         Block Tweet Responders
// @namespace    http://tampermonkey.net/
// @version      1.0
// @description  Blocks all users who replied to a tweet starting from the 5th reply
// @author       me
// @match        https://x.com/*
// @grant        none
// ==/UserScript==

alkimiadev

Choosing AI Models: A Cost-Benefit Analysis Framework

Standard benchmarks and leaderboards measure an AI model's raw capability, but they don't tell the whole story. They ignore crucial, real-world factors that every developer and business must face: your budget, your time, and the actual cost when a model fails and a human has to intervene.

This document presents a simple framework to move beyond abstract accuracy and calculate the total expected monetary cost of using an AI model for a specific task. By factoring in API fees, the probability of success, the cost of retries, and the financial impact of complete failure, you can make a rational, data-driven decision. It helps you answer the real question: which model provides the most value for your specific workflow, not just the one with the highest benchmark score.

Variable definitions (per task)

Symbol	Meaning	Typical unit
p	first-attempt success probability	0–1
c	cost of the first attempt (API $ + your promp

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]