…And The Winner Is Python

THE CRAFT™️ EXPERIMENT :: WEEK 1 :: POST 5

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The Future of CRAFT™️: Python will be the base starting point

The first question of this experiment is which existing programming language should I use as a base. The language that I used in the initial experiment was intentionally vague and included very little syntax. I wanted to test the concept before deciding on a programming language as a base to start with. In addition, I wanted the A.I. chat tools to have the freedom to chose any language.

PROMPT: “Please review the information in this chat and list the 3 programming languages that could be a good base that we can expand on while developing a new way to use these concepts with A.I. chat services. Please explain the reasoning.”

Response: ChatGPT

1. Python

• Why It Works:

  • AI‑First Ecosystem: Python is the lingua franca of machine learning and NLP. Almost every major AI library (from the OpenAI SDK to Hugging Face Transformers) offers first‑class Python support.

  • Dynamic & DSL‑Friendly: Its dynamic typing, first‑class functions, and metaprogramming hooks (e.g. decorators) let you craft a clean, Pythonic DSL for comments, variables, functions, and objects.

  • Rapid Prototyping: A huge standard library and vibrant community mean you can stand up services or CLI tools in hours, not days.

• Everyday Analogy:
  Think of Python as the Swiss Army knife of AI—ready for everything from quick scripting to full‑blown web services.

2. TypeScript

• Why It Works:

  • End‑to‑End Web Integration: Since much of your audience will interact through browsers or Node.js back‑ends, TypeScript bridges front‑end (React/Vue) and back‑end (Express, serverless) seamlessly.

  • Static Safety for DSLs: Its type system (interfaces, generics) lets you define schemas for your chat variables and functions—catching mistakes at compile time rather than in production.

  • Rich Tooling: IDE autocompletion, linting, and build‑time checks make onboarding easier for teams.

• Everyday Analogy:
  Imagine TypeScript as a well‑lit factory floor: everything’s labelled, wired up correctly, and errors get flagged before you even start the machines.

3. Ruby

• Why It Works:

  • Built‑for DSLs: Ruby’s syntax (blocks, method_missing, open classes) is practically tailor‑made for writing human‑readable DSLs—think Rakefiles or Rails routes.

  • Expressive & Readable: You can define chat functions and objects in code that almost reads like plain English, making your instruction files accessible to non‑programmers.

  • Mature Ecosystem: Gems like Thor (for CLIs) or Sinatra (lightweight web apps) let you wrap your DSL in friendly interfaces.

• Everyday Analogy:
  Ruby is like a customizable workshop bench: whichever tool you need, you can bolt it on in a way that feels natural and intuitive.

Response: Gemini

Based on the framework concepts outlined in chat-instructions-ketai-001.txt and our discussion, here are three programming languages that could serve as a good base for developing and expanding this structured way to interact with AI chat services, along with the reasoning:

1. Python:

   • Reasoning: This is perhaps the most obvious choice. Your framework explicitly borrows the # symbol for comments from Python's syntax, and the def FUNCT_Name syntax for functions also closely resembles Python's function definition. Beyond syntax familiarity, Python boasts an unparalleled ecosystem for AI and Natural Language Processing (NLP) with libraries like Hugging Face Transformers, spaCy, NLTK, TensorFlow, and PyTorch. It excels at text manipulation and parsing, which are crucial for interpreting your custom variables, functions, and instructions. Its extensive libraries for making API calls (requests) simplify integration with various AI chat services (ChatGPT, Gemini, Claude, etc.). Python's readability and rapid development cycle make it well-suited for entrepreneurs building practical tools.  

2. JavaScript / TypeScript:

   • Reasoning: If the goal is to integrate this framework directly into a web application, browser extension, or a cross-platform desktop app (using frameworks like Electron), JavaScript (or its typed superset, TypeScript) is essential. It's the language of the web front-end and is very capable on the back-end via Node.js. JavaScript has mature libraries for handling asynchronous operations (vital when dealing with API calls to AI services) and text manipulation. While its native AI/ML library support isn't as historically deep as Python's, it's rapidly growing (TensorFlow.js, ONNX.js), and interfacing with Python backends or external AI APIs is straightforward. JSON, JavaScript's native object notation, is the de facto standard for data exchange in web APIs. TypeScript adds static typing, which can be very beneficial for managing the complexity of a growing framework with defined structures like Objects, Functions, and Variables.

3. Lisp (e.g., Common Lisp, Clojure, Racket):

   • Reasoning: Lisp and its dialects have a long history in AI research and excel at metaprogramming – writing code that manipulates other code. Your framework, with its custom syntax for VAR_, FUNCT_, OBJ_, is essentially a Domain-Specific Language (DSL) for interacting with AI. Lisp's core strength lies in its ability to easily define, parse, and extend such languages. The "code is data" philosophy means the structure of your commands could be represented directly as Lisp data structures, making processing very elegant. While having a steeper learning curve than Python or JavaScript for many, its power in language creation and manipulation makes it a strong, albeit less mainstream, candidate for building and robustly extending a highly customized interaction framework like the one you've envisioned. Modern Lisps like Clojure (runs on JVM/JS) or Racket offer extensive libraries and good communities.

Each of these languages offers distinct advantages depending on the primary development environment (web, backend service, standalone tool) and the emphasis (rapid development vs. language extensibility). Python offers the most direct path given the existing syntax choices and AI ecosystem, JavaScript/TypeScript is key for web integration, and Lisp provides powerful tools for treating the framework itself as a language to be manipulated and extended.

Response: Claude

Based on our discussions about the CRAFT™️ framework and its design principles, here are three programming languages that could serve as an excellent foundation for developing this new approach to AI chat interactions:

1. Python

Reasoning: Python offers several advantages that align perfectly with CRAFT's goals:

• Python's syntax is remarkably readable and accessible to non-programmers

• Its comment structure (using #) has already been incorporated into your framework

• Python's approach to functions and objects is straightforward yet powerful

• The language emphasizes clean, readable code which matches CRAFT's goal of clarity

• Python's widespread adoption means many entrepreneurs already have some familiarity with its concepts

• Its extensive use in AI and data science creates natural synergies for users working in these domains

2. JavaScript

Reasoning: JavaScript brings several beneficial aspects:

• Its object-oriented approach provides a robust model for the OBJ_ components

• JavaScript's function architecture allows for both simple and complex implementations

• The language supports both procedural and functional programming paradigms

• Its ubiquity in web development makes it accessible to a wide audience

• The JSON data structure format provides an excellent model for structured data exchange

• JavaScript's event-driven architecture offers conceptual parallels to how AI chat interactions work

3. Ruby

Reasoning: Ruby offers unique characteristics that complement CRAFT:

• Ruby was explicitly designed with programmer happiness and productivity in mind

• Its elegant, natural language-like syntax makes it approachable for non-technical users

• Ruby's "everything is an object" philosophy aligns with CRAFT's object-oriented approach

• The language offers powerful metaprogramming capabilities that could inspire advanced CRAFT features

• Ruby's block structure provides a useful model for encapsulated instructions

• Its focus on expressiveness over verbosity fits well with CRAFT's efficiency goals

Each of these languages offers valuable concepts that could inform the development of CRAFT's syntax, semantics, and overall architecture. By drawing inspiration from multiple languages rather than strictly adhering to one, you can create a hybrid approach that takes the most intuitive and effective elements from each while remaining optimized for human-AI communication.

---

THE NEW RESULT:

PROMPT: “Please revise the contents of chat-instructions-ketai-001.txt to use the syntax and semantics of the Python language. Please ask any questions needed to accomplish this task.”

RESPONSE:

# ==============================================

# Ketelsen AI Chat Framework Instructions (Pythonic Version)

# ==============================================

# ----------------------------------------------

# 001 - A.I. CHAT COMMENTS

# ----------------------------------------------

# Standard Python comments (using #) are used for personal notes,

# reminders, or context within the chat. They are ignored by the AI

# during processing, just like in a Python script.

# Example:

# Reference: https://www.ketelsen.ai

# ----------------------------------------------

# 002 - A.I. CHAT VARIABLES (Constants/Config)

# ----------------------------------------------

# Define reusable pieces of information as Python variables.

# Using ALL_CAPS_SNAKE_CASE suggests they are constants or configuration

# values for the scope of the chat project.

MY_WEBSITE = "www.ketelsen.ai"

MY_NAME = "Richard Ketelsen"

COLOR_PALETTE = "gray #DCDCDC, orange #FF4E00, black #000000"

TARGET_AUDIENCE_DESCRIPTION = """

Ages 25–45, global (English-speaking), professionals or entrepreneurs

with moderate to high discretionary income. Enthusiastic about AI-driven

innovation and efficiency... (etc.)

"""

# ----------------------------------------------

# 003 - A.I. CHAT FUNCTIONS

# ----------------------------------------------

# Define reusable operations or sequences of instructions as Python functions.

# Use standard 'def function_name(parameters):' syntax.

# Use docstrings ('"""Docstring goes here"""') immediately after the def line

# to explain purpose, arguments (INPUT), and expected return value (OUTPUT/DELIVERABLE).

# Example 1: Function derived from FUNCT_Summary3PromptVariations

def summarize_prompt_variations(variation1_desc, variation2_desc, variation3_desc):

"""

Provides a title and brief summary of 3 prompt variations and how they differ.

Args:

variation1_desc (str): Description of the first variation's focus.

variation2_desc (str): Description of the second variation's focus.

variation3_desc (str): Description of the third variation's focus.

Returns:

str: A formatted summary comparing the three variations.

Example Output:

A Quick Summary of How These Prompt Variations Differ

Variation 1 focuses on getting a straightforward confirmation (“Before providing any solution, confirm…”).

Variation 2 emphasizes restating the question with nuances and assumptions (“Please restate your understanding…”).

Variation 3 uses plain English or user-friendly language to reduce jargon and improve clarity (“Summarize in plain English…”).

All three variations maintain the same fundamental goal…

"""

# AI would implement the logic to generate the summary based on inputs

print("--- Generating Summary ---")

summary = f"""A Quick Summary of How These Prompt Variations Differ

Variation 1 focuses on {variation1_desc}.

Variation 2 emphasizes {variation2_desc}.

Variation 3 uses {variation3_desc}.

All three variations maintain the same fundamental goal..."""

print(summary)

return summary # In a real scenario, AI generates this text

# Example 2: Function derived from FUNCT_MainBlogImage

def generate_main_blog_image_ideas(blog_content_summary):

"""

Generates prompt ideas for a main blog post image based on content.

The image should relate to the content of the post. This function provides

prompt options for use with any A.I. image generation tool that are

visually compelling and interesting. It does not create visuals, only prompts.

Args:

blog_content_summary (str): A summary or key themes of the blog post.

Returns:

list[str]: A list of 10 prompts for the main blog image, representing

the main topic for the entire blog post. No extra text.

"""

# AI would implement the logic to generate 10 prompts based on the summary

print(f"--- Generating 10 image prompts based on: {blog_content_summary[:50]}... ---")

prompts = [f"Prompt idea {i+1} related to blog content" for i in range(10)]

print(prompts)

return prompts # In a real scenario, AI generates these prompts

# ----------------------------------------------

# 004 - A.I. CHAT OBJECTS (Classes)

# ----------------------------------------------

# Define complex structures that bundle data (attributes) and

# actions (methods) using Python classes. Use PascalCase for class names.

class ChatProject:

"""

Represents a specific project within the AI chat environment.

Combines project-specific data and relevant actions.

"""

def __init__(self, project_name, project_goal, budget=None):

"""

Initializes the ChatProject object.

Args:

project_name (str): The name of the project.

project_goal (str): The main objective of the project.

budget (float, optional): Associated budget, if any. Defaults to None.

"""

self.project_name = project_name

self.project_goal = project_goal

self.budget = budget

self.related_variables = {'website': MY_WEBSITE} # Example linking to global vars

self.tasks = []

def add_task(self, task_description):

"""Adds a task to the project's task list."""

self.tasks.append(task_description)

print(f"Task added to {self.project_name}: {task_description}")

def generate_status_report(self):

"""Generates a simple status report for the project."""

report = f"""

Project Status Report: {self.project_name}

Goal: {self.project_goal}

Budget: {self.budget if self.budget else 'N/A'}

Tasks ({len(self.tasks)}):

"""

for i, task in enumerate(self.tasks):

report += f" {i+1}. {task}\n"

print(report)

return report # In a real scenario, AI might generate a more detailed report

# Example Usage (conceptual within chat):

# project_alpha = ChatProject("Alpha Launch", "Launch new feature", 5000)

# project_alpha.add_task("Draft announcement blog post")

# project_alpha.generate_status_report()

# ----------------------------------------------

# 005 - CHAT INSTRUCTIONS (Configuration)

# ----------------------------------------------

# Define global rules or configurations for the chat session

# using Python variables, constants, or configuration objects/dictionaries.

# Example: Using simple boolean flags

ENABLE_FACT_CHECKING = True

PRIORITIZE_NOVELTY = False # If True, AI should favor unconventional ideas

# Example: Using a configuration dictionary

CHAT_CONFIG = {

'output_style': 'concise', # Options: 'concise', 'detailed', 'bullet_points'

'ai_persona': 'helpful_assistant', # Options: 'helpful_assistant', 'skeptic', 'coach'

'language_model_preference': 'latest' # Options: 'latest', 'gpt-4', 'gemini-pro'

}

# Example: How these might be referenced conceptually

# if ENABLE_FACT_CHECKING:

# # AI performs fact-checking step

# pass

#

# ai_response_style = CHAT_CONFIG['output_style']

# # AI adjusts response verbosity based on ai_response_style