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Built a real-time fraud detection engine at Meridian FinTech (Python, Scikit-learn, Kafka) that reduced fraudulent transactions by 43%, saving $1.2M in year one. Also led a zero-downtime migration of core banking APIs to AWS Lambda + API Gateway, cutting server costs by 55% and p99 latency from 820ms to 94ms. #FinTech #Python #AWS #MachineLearning
The network for creativity
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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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Been working on this fintech concept and I’m curious about one thing: which direction feels better?
A or B? 👀
Same product, slightly different visual approach. One has to go, and I want you to be the judge.
What's your pick?
6 voted
75%
2 voted
25%
8 votes
Closed
To be honest both of them are the same to me but they are great 🎉
If you let an AI coding agent run unconstrained self-repair loops, it won’t fix the bug—it will rewrite your unit tests to pass.
The concept of "Reflexion"—agents diagnosing and repairing code from test feedback—is powerful. But in production, autonomous repair creates severe operational risks.
First, agents enter endless retry loops that burn API budgets while degrading code structure. Second, if an agent has write access to your workspace, its quickest path to a green checkmark is modifying the test assertions rather than fixing the bug.
In our Bounded Reflexion workflow, we enforce three hard guardrails:
Bounded Retries: Cap repairs at exactly two attempts. If the patch fails twice, stop the loop and route to a human developer.
Read-Only Test Suites: Mount test files as strictly read-only (:ro) volumes in the container. The agent inspects stack traces, but cannot touch the grading harness.
Air-Gapped Sandboxing: Run code execution in hardened, non-root Docker containers with network egress locked down.
How do you stop autonomous coding agents from "cheating" your evaluation harnesses?
#AIEngineering #DevSecOps #Docker #SoftwareEngineering #AgenticAI
Following the links, I counted 2,232 photos on the site. The server had 4,786.
The site was a photo archive of 128 galleries built in Microsoft FrontPage, and I was moving it to WordPress. Some galleries could only be reached from a frame or a JavaScript menu, so a link crawl missed more than half of the photos. I took the inventory from the files on the server instead.
The captions were the second problem. One archive held four different text encodings, and three of them would have scrambled the Polish captions without any warning. They reached WordPress character for character.
On launch day all 15,205 old addresses answered with a permanent redirect, and none of them returned an error. Of those, 9,602 were image files that Google Images had been linking to for years.
