Nikola Caplar developed a source detection analysis and visualization feature for the lsst-sitcom/sitcomtn-149 difference imaging campaign, focusing on the distribution of detected sources across observing bands and weekly processing runs. Using Python for data analysis and visualization, Nikola incorporated figures and descriptive text to highlight trends at both the bright and faint ends of the magnitude distribution. The work included initial integration of a real-bogus classifier to assess candidate quality, enabling cross-field diagnostic comparisons and supporting data-driven decisions for quality assurance. All contributions were thoroughly documented in LaTeX and managed with robust version control, demonstrating depth in scientific writing and astronomy.