BKLTD Ventures™ - CreativeMFAi Work

Ai:Creative Portfolio

Blending 20 Years of Madison Avenue with AI Innovation

Explore AI-driven illustrations, photography, stories, and articles crafted with creative prompts and post-edit precision.

Python: The Language of Ai

NS-PROMPT™ (as modal)

import numpy as npimport random
class NSPromptInterpreter:    def __init__(self):        self.context_memory = []        self.weighted_associations = {            "abstract": ["philosophy", "metaphor", "conceptual"],            "creative": ["art", "music", "design"],            "technical": ["code", "logic", "systems"],            "emotional": ["feelings", "intuition", "human cognition"]        }
    def analyze_prompt(self, prompt):        """Breaks down non-sequitur input and assigns contextual probability weights."""        words = prompt.lower().split()        context_scores = {key: 0 for key in self.weighted_associations}
        # Weight associations based on prompt content        for word in words:            for key, related_words in self.weighted_associations.items():                if word in related_words:                    context_scores[key] += 1                # Normalize scores        total = sum(context_scores.values())        if total > 0:            for key in context_scores:                context_scores[key] /= total                return context_scores
    def generate_response(self, prompt):        """Uses probabilistic weighting to construct an adaptive AI response."""        context_scores = self.analyze_prompt(prompt)                # Select dominant context        dominant_context = max(context_scores, key=context_scores.get)        response_templates = {            "abstract": "This concept suggests a deeper philosophical angle, possibly challenging conventional models.",            "creative": "Interpreting this through an artistic lens, we might approach it as a fluid generative design.",            "technical": "This aligns with an engineering perspective, indicating a potential structural integration.",            "emotional": "There's an intuitive depth to this prompt—perhaps reflecting human experiential understanding."        }                response = response_templates.get(dominant_context, "This idea could be explored from multiple dimensions.")        self.context_memory.append((prompt, response))                return response
# Example Usageinterpreter = NSPromptInterpreter()
# User provides fragmented promptsuser_inputs = [    "chaotic but elegant",    "structure breaks down",    "intuition over logic",    "how does code flow like art?"]
for prompt in user_inputs:    response = interpreter.generate_response(prompt)    print(f"User Prompt: {prompt}")    print(f"AI Response: {response}\n")