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DM Co-Pilot: Production RAG AI Platform
I built this end-to-end RAG app to query complex datasets. Using Python and Streamlit, it delivers sub-second semantic search results from PDF uploads.
Technical Highlights:
RAG Pipeline: Uses LangChain and OpenAI for context-aware AI.
Data Visuals: Custom 'Web of Fates' graph built with PyVis.
Performance: Deployed on Render; handles high-concurrency.
Pro UI: Clean dashboard for seamless AI interaction.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
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Check it here- https://devpost.com/software/venturelens-ne53u4
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Live demo: support-resolver.vercel.app
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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:
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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
