Connor Tluck

Project Overview

Vinyl ID sits next to a turntable and identifies every record as it plays, so the listening history builds itself. It started with an old Raspberry Pi, a screen, and a few components. I designed and built all of it: the device and its 7-inch display, the recognition pipeline, the iOS app, the backend, and the site.

Key Features

• • Recognizes a record within seconds of the needle dropping, using acoustic fingerprinting
• • 7-inch always-on display showing what's playing and listening stats
• • iOS companion app with now playing, a collection that builds itself, and stats by genre, era, and streak
• • Friends can see what you're spinning, private by default
• • Works with any turntable, amp, or speaker setup
• • Founder batch of 30 hand-assembled units, built in Brooklyn

Project Overview

A LaunchDarkly demo built on a Mapbox map of Canada, tiled with hexes and grouped into 41 named service areas. A boolean flag under a progressive rollout turns the areas from red to yellow to green, which makes a percentage rollout easy to explain to a room.

Key Features

• • Live mode evaluates the flag for every service area with the LaunchDarkly Node SDK, so changes made in LaunchDarkly show up on the map
• • Simulated mode steps through 1% to 100% with no SDK key needed
• • A targeting rule holds the three northern territories back for the whole rollout
• • Each service area is evaluated as its own context kind

Project Overview

Enterprise releases usually span several teams and services. This demo draws those dependencies as an interactive graph backed by LaunchDarkly prerequisite flags. Toggle a flag and watch the effect cascade through everything downstream.

Key Features

• • A flag cannot evaluate to true until all of its prerequisites are met
• • Turning off an upstream flag blocks everything that depends on it
• • A master release gate rolls up readiness across the whole feature
• • Simulation mode runs without a LaunchDarkly account

Project Overview

A K-7 homework worksheet generator built to demo LaunchDarkly AgentControl. An intake agent interviews the teacher, a generator agent writes the worksheet, and a reviewer checks it. The agents, their tools, and the models behind them are all managed from LaunchDarkly instead of in code.

Key Features

• • Agent graph defines the intake, generator, and reviewer handoffs outside the codebase
• • Three generator variations on different Claude models, picked by targeting rules, with a side-by-side compare mode
• • A judge scores every worksheet for grade appropriateness
• • Feature flags gate compare mode, the PDF theme, the answer key, and the reviewer step

Project Overview

FastAPI service for querying and analyzing OpenStreetMap data for machine learning annotation workflows and civil engineering applications.

Key Features

• • 91+ OSM Feature Types with comprehensive coverage
• • Interactive Documentation and web interface
• • Data Export: CSV rollups and visualizations
• • Rate Limited API with secure endpoints
• • Feature Categories: Transportation, Buildings, Natural, Services

Project Overview

Python toolkit for generating synthetic LiDAR point cloud data in LAS/LAZ format for testing, training, and development in machine learning applications.

Key Features

• • 30+ Pre-built scenes with urban infrastructure elements
• • Realistic materials with proper intensity values and RGB colors
• • ASPRS classification standards compliance
• • High density (~400 points per square meter)
• • Multiple export formats (LAS and LAZ)
• • 3D visualization with Open3D preview

Project Overview

Interactive web application for visualizing and analyzing trajectory data using Mapbox GL JS with satellite view and Street View integration.

Key Features

• • Interactive map visualization with Mapbox GL JS
• • Trajectory data filtering and search
• • Satellite view toggle with opacity control
• • Google Street View integration
• • Real-time database statistics

Project Overview

Comprehensive Grafana reporting system with automated PDF snapshots and CSV exports for offline access to critical project metrics.

Key Features

• • Automated PDF dashboard snapshots
• • CSV data exports for analysis
• • Scheduled weekly and monthly reporting
• • Secure email delivery to stakeholders
• • Account-specific filtering and permissions

Project Overview

Mapbox-based application for visualizing extracted traffic signs with full MUTCD sign library integration for enhanced user experience.

Key Features

• • Interactive Mapbox map interface
• • Advanced filtering by sign code
• • Real-time sign display on map
• • Full MUTCD sign library integration
• • User-friendly interface design

Project Overview

Developed a Python-based vectorization algorithm for utility line networks. This innovative solution addresses the challenge of converting raster line data to vector format for utility infrastructure mapping.

Key Features

• • Pole-to-pole connection analysis
• • Raster-to-vector conversion algorithm
• • Automated line connection verification
• • Improved utility client adoption rates

Technical Approach

Created a model using raster data and a searching algorithm that goes pole-by-pole to check for nearest pole options, then verifies line connections in the raster dataset. This algorithm worked well and improved utility client adoption while the engineering team developed an official solution.

Project Overview

Built a comprehensive Python backend solution to address the gap between Nearmap's SaaS model and engineering firms' budget constraints. This system enables bulk downloads of maximum resolution areas with intelligent cost tracking.

Key Features

• • Tile API integration for bulk area downloads
• • Shapefile-based area tracking via MongoDB
• • Cost-tracking dashboard for budget management
• • PyQt frontend with OpenLayers exploration
• • S3 delivery service integration

Project Overview

Developed a KNN classification model using Scikit-Learn to automate roadway parametric design. This innovative solution processes unclassified XYZ point data from field surveys to establish clean centerline geometry that fits road design curve and tangent standards.

Key Features

• • Automated classification of survey points (left edge, right edge, embankment safety methods)
• • Semi-automated road safety improvements and design processes
• • Reduced manual work for roadway engineers
• • Advanced visualization with color-coded classifications

Technical Details

Visualization shows classified tangents in orange, curves in blue, Points of Intersection in light blue, and Curve Centerpoints in green.

Project Overview

Created a comprehensive demo of Nearmap content rendered in Infraworks, establishing a new value demonstration for the Nearmap Content Stack in engineering applications. This project showcases the power of combining high-resolution geospatial data with advanced 3D modeling tools.

Key Features

• • High-resolution orthoimagery integration
• • Terrain data visualization
• • Vector AI data rendering
• • High-fidelity 3D renderings for enterprise clients

Business Impact

Infraworks is an early planning tool, and for enterprise clients, having access to a national dataset for high-resolution orthoimagery, terrain data, and vector AI to create high-fidelity renderings is extremely important for project planning and stakeholder communication.

Project Overview

Built a comprehensive Three.js and HTML product for SE conferences and external demos. This local HTML application provides a powerful viewer and data aggregation platform for demonstrating Nearmap content and products.

Key Features

• • Local HTML app for offline conference use
• • Three.js-powered 3D content viewer
• • Data aggregation platform for product demonstrations
• • Optimized for slow conference internet connections

Project Overview

Enhanced visualization tool for Nearmap API JSON response tree structures using the open-source JsonCrack library.

Key Features

• • Interactive JSON tree structure visualization
• • Enhanced API response understanding
• • Developer-friendly debugging interface

Project Overview

Initial investigation phase for handling massive point cloud datasets using optimized cloud viewing libraries, then handed off to engineering team for evaluation.

Key Features

• • Optimized cloud viewing libraries evaluation
• • Large-scale point cloud processing investigation
• • Performance optimization for massive datasets
• • Cloud-based rendering solutions research

Project Overview

Custom-built internal dashboard for tracking annotation efforts, performance metrics, and operational efficiency beyond standard dashboard capabilities.

Key Features

• • Real-time annotation tracking and performance metrics
• • User performance rankings and activity tracking
• • Project comparison and dataset analysis
• • Export functionality for PNG reports
• • Advanced filtering by layers, projects, and annotators

Project Overview

An artistic and dynamic version of the trajectory viewer designed for enhanced visualization experiences. This version features animated views that jump between different projects, creating a more engaging and visually appealing presentation of trajectory data.

Key Features

• • Animated view transitions between projects
• • Enhanced visual effects and rendering
• • Artistic visualization techniques
• • Dynamic camera movements
• • Interactive project switching
• • Real-time animation controls
• • Custom visual styling

Technical Approach

Built upon the base trajectory viewer with additional animation layers and artistic rendering capabilities. Focuses on creating compelling visual narratives through dynamic camera work and enhanced visual effects.