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Inventory tracker
Never run out of stock again 📦
Built this inventory tracker with live formulas — auto-calculated stock value, reorder alerts that highlight in red when stock drops below threshold, and category breakdown charts for a clear view of where your money sits.
Need something like this for your business? I build custom Excel trackers that do the thinking for you.
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
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Excel-based financial analysis and data reporting project focusing on structured data organization, budgeting, and performance metrics.
I built MacroPulse, an AI-powered financial intelligence dashboard that combines macroeconomic data, stock markets, crypto prices, and financial news into a single workspace.
The problem was simple: investors were constantly switching between dashboards, news sites, and market trackers just to piece together what was happening.
So I built a full-stack platform with Next.js, integrating FRED, Polygon, CoinGecko, and MarketAux, then added Claude AI to generate concise market commentary that explains why markets are moving—not just what changed.
📈 Early testing showed:
• ~65% less time spent gathering market data
• 40% faster market research workflows
• Over 1,000 market updates processed daily from multiple data sources
• AI summaries generated in seconds instead of users reading through dozens of articles
The biggest takeaway for me wasn't the AI it was how much value comes from putting the right context next to the data.
I'm curious, if you had a dashboard like this, what's the one feature you'd use every single day? Or what financial data source do you wish more platforms included?
Nice build. I like the focus on bringing everything into one place instead of adding AI for the sake of it. The context alongside the data is what makes it valuable. Great work. 👍
An interactive D3.js dashboard analyzing revenue, churn, and customer segments for a telecom subscription business (7,043 real customers). Calculates "Revenue at Risk" turning a churn rate into a churn cost and finds that contract type is the strongest churn predictor (42.7% for month-to-month vs. 2.8% for two-year contracts), while payment method is a surprisingly strong signal on its own: electronic check users churn at 45.3%, nearly 3x automatic payment methods.
