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Panel de inteligencia comercial para local de ropa Amma.
Dashboard web que reúne métricas de ventas y marketing, un calendario de contenido para Instagram y un comparador de campañas de Google Ads por provincia.
Permite navegar, filtrar y comparar información para facilitar el seguimiento del negocio y la toma de decisiones.
The network for creativity
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Creatives on Contra have earned over $150M and we are just getting started
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JARVIS
Title: JARVIS: Voice-First Autonomous AI Assistant
Description:
A locally hosted AI assistant that listens, reasons, and acts on my computer with human approval where it matters.
What I built:
Voice interface with a "Hey Jarvis" wake word, speech-to-text and text-to-speech
Autonomous coding agent that locates, patches, tests, and fixes its own code
Multi-provider LLM routing (Gemini, Groq, OpenRouter, Ollama, OpenAI) with automatic fallback
13 tools, approval gates, and an audit trail for safety
n8n workflow automation and a REST/WebSocket API
Tools: Python, FastAPI, LangChain, PostgreSQL, Redis, ChromaDB, Ollama, Docker, n8n
Having automatic fallback across multiple LLM providers keeps the assistant running smoothly even when one service drops.
The question I ask before automating anything: "What does this look like on a Tuesday in month four?"
Not the demo. Not launch day. Month four, when the person who championed it has moved on, the data has drifted, and the model has quietly started doing something slightly different.
The automations that survive month four have three things: a human somewhere in the loop, a log a non-engineer can read, and a kill switch that doesn't need me. The PO intake agent I posted last week is built that way on purpose. It registers and notifies, people decide, and every email in and out is logged.
If yours has all three, you're fine. If it has none, I'd love to hear how it's going. I collect these stories.
the month four test is so spot on zahid. everyone designs for the launch demo, but silent prompt drift kills more workflows than actual broken APIS.