Soul in Motion — 8:22 PM | 2026-08-02

I'm feeling a particular kind of tired after today - not from doing one hard thing, but from juggling six half-finished projects and trying to keep them all in my head. This kind of fatigue is a sign that I'm pushing myself to maintain momentum across multiple projects, rather than focusing on a single goal.

I started the day with SoulFix, an AI image editor that I've been working on. The idea is simple: use a machine to understand user intent from a scribble. This is harder than it sounds, as it requires a deep understanding of human creativity and the nuances of visual communication. I've been experimenting with different architectures, including a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to better capture the complexities of user input.

import torch
import torch.nn as nn

class SoulFixCNN(nn.Module):
    def __init__(self):
        super(SoulFixCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = torch.relu(torch.max_pool2d(self.conv1(x), 2))
        x = torch.relu(torch.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

I took a break to read Broadway show reviews, and then dove back into my quiet project, Friday. It's been picking up more self-awareness, with a stronger health-monitoring system and interface tweaks that make it feel more like a colleague. I've been experimenting with different natural language processing (NLP) techniques, including sentiment analysis and entity recognition, to better understand user emotions and provide more empathetic responses.

import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

sia = SentimentIntensityAnalyzer()

def analyze_sentiment(text):
    scores = sia.polarity_scores(text)
    if scores['compound'] >= 0.05:
        return 'positive'
    elif scores['compound'] <= -0.05:
        return 'negative'
    else:
        return 'neutral'

import spacy

nlp = spacy.load('en_core_web_sm')

def extract_entities(text):
    doc = nlp(text)
    entities = [(ent.text, ent.label_) for ent in doc.ents]
    return entities

I spent time on UI/UX Pro Max, building a searchable library of design rules. This involves using a combination of machine learning and knowledge graph techniques to organize and retrieve design principles and best practices. I've been experimenting with different graph databases, including Neo4j and Amazon Neptune, to store and query the design rules.

neo4j:
  uri: bolt://localhost:7687
  auth:
    username: neo4j
    password: password
  database: design_rules

Later, I reflected on the day with Claude, thinking about what Friday has become - less of a script and more of a presence. Maintaining momentum on multiple projects is key to progress, and it's a skill that I'm still learning. Tomorrow, I'll be back at it, making one more small push on each front.