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My own server platform in Go: a modular core where services plug in as modules over gRPC/protobuf. Built-in i18n — logs follow the system locale (Russian on screenshot) or a language flag. Structured logging with microsecond timings, themed with Material Design colors ported to the terminal (theme.json). YAML/env configuration
The network for creativity
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A multi-tenant AI orchestration platform that lets organizations deploy and manage AI agents at scale. It supports persona-based agents with defined roles and behaviors, webhook-driven triggers for automated actions, and single-prompt management of complex multi-step workflows. Built to run reliably on Kubernetes and Docker across multiple organizations.
Whats your thoughts on this?
Very impressive architecture. Multi-tenancy, persona-based agents, and Kubernetes scalability make this a strong foundation for enterprise AI automation. 🚀
This project demonstrates a Retrieval-Augmented Generation (RAG) architecture using open-source tools. It solves the "knowledge cutoff" problem of LLMs by giving them access to live tools.
How it works:
Intent Classification: The user's input is first processed by a local Llama 3 instance, which acts as a semantic router. It analyzes the prompt to determine if external data is required.
Tool Execution: If a search intent is detected, the Python backend executes a query against the SerpApi (Google Shopping) endpoint. Data Structuring: The raw JSON response from the API is parsed, cleaned, and structured into standardized product objects.
UI Rendering: The structured data is dynamically rendered into interactive product cards using Streamlit's component system, allowing users to view images and click direct purchase links without leaving the chat interface.
Retrieval- Augmented Generation (RAG) web app that synthesizes real time data from PUBMed.com and ClinicalTrials.gov, a custom React frontend and Node.js backend with MongoDB backend multi turn memory to maintain conversational context and prevent AI hallucinations
