Everyone is arguing about which memory repo wins. Mem0. Claude Cidian. Memory Palace. Most people pick one, install it, and move on. That is the problem. Off the shelf memory systems are built for the masses. They do not know your business workflows, your recurring projects, or what you need Claude to retain across sessions. A lawyer needs case precedent recall. An ecommerce operator needs seasonal ad performance and consumer demand patterns. Same tool, completely different memory profile. I reverse engineered three open source memory repos and built my own stack. Here is the process. Step one is clone. Pull Mem0, Claude Cidian, and Memory Palace into a local folder. Step two is audit. Ask Claude Code to spin up sub agents that do a full deep dive on every repo and compare them against each other. Each agent takes about 5 to 10 minutes and runs in the background. The results land in your context window without flooding it. Step three is extract. Tell Claude what your day to day actually looks like, then ask it to pull only the design patterns and code that fit YOUR use case. Skip everything else. From there you layer your system on top of Claude's native memory instead of replacing it. Identity: name, role, anything that never changes. Lives at the top. Critical context: your business, your current projects, your market position. Sits right below identity. Working memory: the messy temporary thoughts for whatever you are building right now. Disposable once the task ships. Long term knowledge: outcomes worth revisiting even if they are not foundational to who you are. A litigation result, a product launch postmortem, a pricing change log. Episodic memory: why you saved something in the first place. The context behind the entry, not just the entry itself. Decay and promotions run in the background. Old irrelevant memories lose weight. Frequently called memories rise in importance. The stack cleans itself as your priorities shift. You do not need a nuclear bomb for a fist fight, right?

If you're building AI agents, I'd urge you to create a template for prompting. Two notable builds in the last month as proof: - I've built an AI agent that has handled over 9,000 emails - Another AI agent that's handling 25k customers. But here's the full setup: - A claude.md file that references a prompting guideline file, it tells Claude how to write prompts. - Once a prompt is approved, I write at the top "approved for production" which tells Claude that it should not make big changes. This makes sure that the prompt does not get destroyed by Claude. - Push the changes to my GitHub to keep track of all changes. This last part is where most people go wrong. When they see a mistake, they ask Claude to write an explicit rule to never do that again. The issue is that Claude will only look for that exact case, and if the next case doesn't match it, Claude will skip it. Instead, what I do is write mental models of the idea, what we're trying to do and why. When you do it this way, Claude has to use more reasoning to figure out which mental model makes sense. You're letting Claude think with some constraints. But this system has cut down my prompting time and also increased my reliability ten fold. And the thing is that I can use this wherever AI agents are used. Sales agent, customer service agent, any type of agent. Because the structure is the exact same every single time. Give me the agent and I'll make it reliable.