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
Product catalog validator — self-initiated demo
Built with AI-assisted development using 13 synthetic products. This is an independent demonstration, not paid client work.
The Python validator accepts 4 products unchanged and rejects 9 with field-level JSON paths and error codes. Checks include duplicate SKUs, required fields, decimal-string prices, supported currencies, integer stock and HTTPS URL syntax.
11 automated tests passed. The tool does not invent replacements or silently repair records. Source code, sample input, validation report and reproduction commands are included in the demonstration package.
Local syntax validation only; no live store integration or network requests.
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
Trending
retrofuturism
Share your favorite designs from the past exploring the distant future. Or your own take on the aesthetic 🚀
halftone
Dots. Texture. Grain. Let's see your designs using the halftone technique.
Midjourney
Creatives are showing off what they're making with Midjourney.
colorgrade
Video editors sound off: How do you enhance the mood of your video in color grading?
Related posts
Designing Bledga with a clear, intuitive interface—from profile and settings to support and upgrades—so every interaction feels simple, purposeful, and easy to navigate.
Looking really clean and usable!
AI agents are most useful when they reduce uncertainty—not when they silently replace judgment.
The Next.js team just used a research agent to review a backlog of 2,244 GitHub issues. In roughly three weeks, 1,462 issues were closed and only 3 were reopened.
What made the workflow credible:
• every investigation ran in an isolated sandbox
• the agent checked code, versions, PRs, commits, releases, and docs
• it actively searched for evidence against its first conclusion
• risky actions stayed read-only until humans reviewed the evidence
• the clearest cases required a second-agent challenge before automation could close them
That is the pattern I trust for production AI automation: evidence first, structured outputs, bounded permissions, and reversible actions.
Where could an evidence-driven agent remove the most operational drag in your workflow?
Where it removed the most drag for me was real-estate contract deadlines - a chain of dates where one addendum moves a date and nothing throws an error. The agent is not there to judge, it is there to show that something moved.
The second-agent challenge is the bullet I would...
Inventory reconciliation — self-initiated demo
An AI-assisted Python tool comparing expected stock with counted stock, using only synthetic data. This is demonstration work, not a client engagement.
The executed example retains all 16 input rows for audit, reviews 9 SKUs and finds 3 exact quantity differences. Duplicate SKUs and invalid quantities remain blocked for review; missing counts are not treated as zero.
7 tests passed. Repeated runs produced identical report files. Includes Python source, CSV inputs, JSON/CSV results and instructions. Uses Decimal arithmetic; assumes a consistent unit per SKU. No ERP connection or production deployment.
