The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
eBay Competitor Tracker & Product Data Extraction Tool
Built a Python-based web scraping and browser automation tool for extracting structured product and competitor data from eBay.
The tool accepts a search keyword, eBay country/market, and product limit, then uses Playwright and Chromium to collect detailed public listing information, including product titles, prices, shipping costs, total prices, currency, condition, seller information, feedback, item location, number sold, watchers, listing type, time remaining, product URLs, and image URLs.
It supports 8 eBay markets and handles pagination, duplicate listings, locale-specific price formats, retries, and logging.
The scraper generates Excel, CSV, and JSON outputs. The Excel workbook also includes an analytics sheet for summarizing pricing and seller information.
I also implemented responsible scraping practices, including configurable delays and retry/backoff handling, without using account login, CAPTCHA solving, fingerprint spoofing, or proxy rotation to bypass restrictions.
Tech: Python, Playwright, Chromium, BeautifulSoup, openpyxl, JSON, CSV.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Another late night. Building a quant trading system an agentic model where different AI agents handle research, testing and risk, and none of them is allowed to place a trade on its own.
The hardest part so far? Being honest when the results say "not yet." Most strategies I've tested failed once real costs were included. That's exactly what testing is for.
Keeping The Psychology of Money and The Diary of a CEO close while I figure it out. What are you reading these days?
Curious how you’ve separated the agents from execution. When you say none can place a trade on its own, is that enforced through a separate execution service with fixed risk checks, human approval or both? I’d love to understand where the agent's decision-making ends and the hard rules take over.
I designed and developed a Spotify-inspired Music Analytics Dashboard in Microsoft Power BI, combining data modelling, DAX, interactive navigation, and custom dashboard design into a single analytical experience.
The dashboard allows users to explore music data from different perspectives — from overall performance and popularity to individual artists, songs, albums, and trends over time.
What I built
- Overview Dashboard — high-level KPIs and music performance trends
- Artist Analytics — artist rankings, song counts, popularity and catalogue analysis
- Song Analytics — song popularity, duration, year-wise analysis and album insights
- Interactive Navigation — seamless movement between dashboard sections
- Dynamic Visual Analysis — monthly trends, popularity patterns and category comparisons
- Detailed Data Tables — deeper exploration beyond summary-level KPIs
- Spotify-inspired UI — dark, modern interface designed around the visual identity of a music streaming platform
Key metrics explored
- 342 Artists
- 789 Distinct Songs
- 562 Albums
- Average Song Duration: 3.28 minutes
- Average Popularity: 90
Tools & Skills
Power BI · DAX · Data Modeling · Data Visualization · Interactive Dashboard Design · KPI Development · Business Intelligence
The goal wasn't simply to create another Power BI report — it was to make the data explorable, intuitive and visually engaging.
Interested in turning your own data into an interactive Power BI dashboard? Let's connect.
Agent Evaluation Dashboard — Full-Stack LLM Observability Tool
Teams running AI agents in production need visibility into how those agents actually perform not just whether they respond, but how accurate, fast, and cost-efficient they are. I built a full-stack observability dashboard to solve exactly that: a single place to track accuracy, latency, API cost, and schema compliance across multiple LLM models and agent types.
What I built
A responsive Next.js (App Router) frontend styled with Tailwind CSS — deep purple sidebar, live accuracy/latency charts (Recharts), filterable eval-run tables, and per-model performance comparisons
A Python FastAPI backend serving the dashboard's data layer, with token-based authentication protecting every route
Full session handling: login, protected pages, automatic redirect for unauthenticated users
A fully responsive layout collapsible drawer sidebar on mobile, reflowing grid on tablet/desktop
