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Cover image for Soul in Motion — 10:27 AM | 2026-08-01
Dev Rajput
Dev Rajput

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Soul in Motion — 10:27 AM | 2026-08-01

#devops #python #ai #llm

TL;DR

  • Tweaking an algorithm to improve performance and logic.
  • Building and breaking interface pieces for a better user experience.
  • Teaching a system to erase unwanted parts of a photo using AI/LLM.
  • Tightening loose screws on an older project.
  • Reflecting on the importance of persistence and enjoying the work.

A Day of Making Things Disappear

My morning started like many lately - coffee, a blank terminal, and a problem I couldn't shake. I spent three hours tweaking an algorithm that was too slow and clumsy. The algorithm in question was a machine learning model trained on a dataset of object detection tasks. It used a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify objects in images.

# Simplified example of the algorithm
import torch
import torch.nn as nn

class ObjectDetector(nn.Module):
    def __init__(self):
        super(ObjectDetector, self).__init__()
        self.cnn = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3),
            nn.ReLU(),
            nn.MaxPool2d(2, 2)
        )
        self.rnn = nn.GRU(input_size=64, hidden_size=128, num_layers=1, batch_first=True)

    def forward(self, x):
        x = self.cnn(x)
        x = x.view(-1, 128)
        x = self.rnn(x)
        return x
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After hours of tweaking, I finally got it to work, and that quiet satisfaction when a stubborn piece of logic works is hard to explain. The key was to adjust the hyperparameters of the model, particularly the learning rate and batch size.

# Adjusting hyperparameters
python train.py --lr 0.001 --batch-size 32
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I shifted to visual work, building and breaking interface pieces, focusing on feel and functionality. The real project of the day was teaching a system to erase unwanted parts of a photo. I used a combination of image processing techniques and AI/LLM to achieve this.

# Simplified example of image processing
from PIL import Image
import numpy as np

def erase_unwanted_parts(image_path):
    # Load image
    image = Image.open(image_path)

    # Convert to numpy array
    image_array = np.array(image)

    # Apply image processing techniques
    image_array = cv2.GaussianBlur(image_array, (5, 5), 0)
    image_array = cv2.threshold(image_array, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

    # Use AI/LLM to erase unwanted parts
    model = load_model()
    output = model.predict(image_array)

    # Save output
    output_image = Image.fromarray(output)
    output_image.save('output.jpg')
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Getting it to work was a challenge, with circling back, second-guessing, and rebuilding. But when it finally worked, it felt almost magical - like editing reality.

I also tightened loose screws on an older project and let myself drift, watching Boardwalk Empire, browsing Kaggle forums, and listening to a Joe Rogan episode on AI. Later, I watched Gone with the Wind, a film that asks nothing but to sit still and watch.

Looking back, it wasn't about a single project, but small persistences - a slow algorithm, an interface, and a photo that finally worked. Nothing dramatic happened, but three things worked better by the end of the day. I'd let my mind wander enough to remember why I enjoy this work.

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