Started diving into agentic infrastructure just two weeks ago with zero foundation—and honestly, it's been a blast. Built out my own local memory system using a small language model running locally, structured around a dual-channel design that actually works.

Channel 1 focuses on hybrid seeds with semantic search powered by embeddings. The approach lets you index and retrieve contextual information efficiently without relying on external APIs. The weighting system pulls different data signals based on relevance scores, which keeps the inference clean and responsive.

What surprised me most? How quickly you can prototype this stack with consumer-grade hardware. The local LLM handles the embedding generation in real-time, and the dual-channel setup routes queries intelligently between structured data and semantic matching. It's not groundbreaking infrastructure, but for personal AI agents that need memory persistence, this scales surprisingly well.

The learning curve was steeper than expected, but breaking it down—embeddings, vector search, local inference pipelines—each piece clicked once you stop overthinking it. If you're exploring agentic systems, starting local is definitely the move.