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Devs are using Memory Bank as a project architecture and planning tool before they write any code. "I prefer starting in Cline, using Memory Bank to build out the context files as a roadmap, and letting Cline take the wheel." How to use Memory Bank for project planning 🧵

96,628 次观看 • 1 年前 •via X (Twitter)

10 条评论

Cline 的头像
Cline1 年前

1/ Traditional project planning forces you to switch contexts between planning tools and coding. Memory Bank eliminates this by embedding your roadmap directly in your codebase -- where both you and Cline can access it.

Cline 的头像
Cline1 年前

2/ Start by defining your high-level goals in Then let Cline populate the technical architecture in By the time you reach you have a complete blueprint before writing a single line of code.

Cline 的头像
Cline1 年前

3/ The real magic happens when Cline references its own roadmap while coding. It's not just following orders - it's understanding the WHY behind each component, leading to more coherent implementations that align with your original vision.

Cline 的头像
Cline1 年前

4/ Cline users report this approach creates an "autopilot coding" experience. One user compared it to "driving on the autobahn: you burn through fuel faster, but you get where you're going much quicker."

Cline 的头像
Cline1 年前

5/ Unlike static planning docs, Memory Bank evolves as your project does. As new patterns emerge during development, Cline updates the roadmap automatically. Your documentation is always in sync with your code.

Cline 的头像
Cline1 年前

6/ The productivity boost comes from eliminating the mental overhead of context switching. Cline knows the plan, executes against it, and handles the documentation updates - creating a seamless loop from planning to implementation.

Cline 的头像
Cline1 年前

Interested in using Memory Bank? Here are the docs:

GitProtect.io 的头像
GitProtect.io2 年前

Automated GitHub, GitLab, Bitbucket and Jira backups, security compliance, data migration and every-scenario-ready Disaster Recovery for 360 cyber resilience. Schedule a custom demo or try 14 days for free.

Simo-Pekka Kiihamäki 的头像
Simo-Pekka Kiihamäki1 年前

How do you use Cline with large codebases? I find it hard to have it really scan through all files. It seems to be superficial and complete the task early

No me importa nada ⭐⭐⭐ 的头像
No me importa nada ⭐⭐⭐1 年前

implement darg and drop for the folder context

相关视频

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,673 次观看 • 1 年前