OpenClaw Memory Problem SOLVED | Stop Wasting Time Explaining скачать в хорошем качестве

OpenClaw Memory Problem SOLVED | Stop Wasting Time Explaining

In this Box Money AI episode, we break down why OpenClaw agents “forget” (session resets + context window limits) and how to improve long-term memory. We cover semantic search with embeddings (OpenAI and the newer cheaper Mistral option), QMD as an alternative memory approach, and using persistent Skills for repeatable behavior. We also share a workflow to save daily chat summaries to text, Obsidian, and GitHub. Next video: Opus vs Minimax model cost/performance. Join our Discord:   / discord   Zeabur Server: https://zeabur.com/ (Save $5 use code: boxmining) Minimax 10% Off: https://platform.minimax.io/subscribe... Kimi AI: https://www.kimi.com/kimiplus/sale?ac... Read more AI News: https://www.boxmining.com/ 00:00 Welcome Back + Why OpenClaw Memory Matters 00:20 The Frustration: Context Window & “It Forgot Everything” 01:50 How AI “Wakes Up” Each Session (The Reset Analogy) 02:34 Demo Setup: Asking Banner About Memory & Embeddings 03:31 Embeddings 101: Semantic Search to Recall Past Chats 04:19 Enabling Memory + Cost Tradeoffs (OpenAI vs Mistral Embeddings) 05:34 Alternative Memory Method: QMD (Cheaper Long-Term Recall) 05:41 Skills as Persistent Knowledge (Matrix/Kung Fu Example) 06:53 3 Memory Options Recap + Viewer Requests 07:32 Personal Workflows: Daily Summaries to Obsidian/GitHub 09:01 Wrap-Up + Next Video Teaser: Opus vs Minimax Models 09:48 Final Thanks & Sign-Off