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Creatives on Contra have earned over $150M and we are just getting started
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I built a small data-cleaning workflow around one simple rule: bad rows should never silently disappear.
Synthetic demo results:
• 20 raw order records
• 16 clean rows
• 2 duplicates removed
• 2 invalid rows quarantined
• $3,690 paid revenue reconciled
The output includes a clean CSV, a rejected-row file, an audit log, and an Excel dashboard. Dates, names, emails, regions, products, and payment statuses are standardized.
The key design choice is quarantine + reasons, not just dropping bad rows and exporting. That lets a reviewer trace exactly what changed and decide whether rejected records should be repaired.
For recurring exports, what needs the most manual review in your workflow: duplicates, inconsistent labels, or invalid amounts and dates?
Everyone's building AR filters and calling it "computer vision magic." Almost nobody's asking what's actually happening underneath — that most of these effects are just clever masking, not detection.
Here's proof. I built an invisibility cloak that runs entirely in the browser, no green screen, no chroma key, no model training.
https://github.com/AnuragNagare/Ghost-frame
Strong example of why the distinction between masking and detection matters. A useful next benchmark would be robustness under lighting changes and moving backgrounds, with latency and failure cases shown alongside the effect. That would make the trade-offs very clear for browser-based workflows.
I built a local Python price-monitoring starter kit to replace repetitive manual checks.
The useful part is not just collecting prices — it is keeping the workflow auditable:
• append-only price history and change logs
• price-drop, target-price, and stock-change alerts
• CSV, JSON, and a readable HTML digest
• no telemetry, with SMTP disabled by default
I verified the synthetic demo across 12 fictional targets and 8 snapshots: 96 history rows, 12/12 successful checks in the latest run, and 13 generated alerts.
It is intentionally a transparent Python source kit, not a universal no-code scraper. JavaScript-rendered, login-protected, or unusual pages can require custom adaptation.
What is the hardest part of price monitoring in your workflow: changing layouts, noisy alerts, or JavaScript rendering?
