linkedin_post.md

🚀 LinkedIn Post Drafts for Aegis / Sentinel

Here are three variations for your LinkedIn post, depending on the angle you want to take.

Option 1: The "Technical Deep Dive" (Best for Engineering Networks)

Headline: Building a Proactive Financial AI Agent with LangGraph & Microservices 🛡️

I just wrapped up building Aegis, a "Premium Financial Terminal" powered by AI. It’s not just a chatbot; it’s a fully orchestrated agentic system.

The Tech Stack:

• Brain: LangGraph for stateful orchestration (Google Gemini).
• Architecture: Microservices pattern using the Model Context Protocol (MCP).
• Real-Time: A background monitor that watches my portfolio and pushes alerts.
• Frontend: Streamlit for that Bloomberg Terminal vibe.

How it works: The "Orchestrator" breaks down natural language directives (e.g., "Analyze TSLA and check my exposure"). It routes tasks through a FastAPI Gateway to specialized agents: a Web Researcher (Tavily), a Market Analyst (Alpha Vantage), and a Portfolio Manager (Local DB).

It’s been a great journey learning how to decouple AI tools from the core logic.

#AI #LangGraph #Python #Microservices #FinTech #LLM #OpenSource

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Option 2: The "Product Showcase" (Best for General Audience)

Headline: Meet Sentinel: My Personal AI Financial Analyst ⚡

Tired of switching between news sites, stock charts, and my brokerage app, I decided to build my own solution.

Introducing Sentinel (Project Aegis) – an AI agent that acts as a proactive financial analyst.

What it does: ✅ Deep Dives: I ask "Why is Apple down?", and it reads the news, checks the charts, and writes a report. ✅ 24/7 Monitoring: It watches my watchlist and alerts me to price spikes or breaking news. ✅ Portfolio Context: It knows what I own, so its advice is personalized.

The power of Agentic AI is that it doesn't just talk; it does. It plans, researches, and synthesizes information faster than I ever could.

#ArtificialIntelligence #FinTech #Productivity #Coding #Streamlit

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Option 3: The "Learning Journey" (Best for Engagement)

Headline: From "Chatbot" to "Agentic System" – My latest build 🧠

I spent the last few days building Aegis, and it completely changed how I think about AI applications.

I started with a simple script, but realized that for complex tasks like financial analysis, a single LLM call isn't enough. You need Agents.

Key Lessons Learned:

1. State Management is King: Using LangGraph to pass context between a "Researcher" and a "Data Analyst" is a game changer.
2. Decoupling Matters: I built a "Gateway" so I can swap out my News provider without breaking the whole app.
3. Latency vs. Accuracy: Orchestrating multiple AI calls takes time, but the depth of insight is worth the wait.

Check out the architecture in the comments! 👇

What are you building with Agents right now?

#BuildInPublic #AI #Learning #SoftwareEngineering #TechTrends