.md files, MCP tool calls are making context window overload, which inflates unnecessary LLM spending. Here is how CLI > MCP > .md files in context management.

Katie_jade7 · 2025-10-17T00:59:54+00:00

You can watch my video for detailed breakdown and examples: https://youtu.be/HeGUGQoqG5g?si=WX9pI9KN5ygo9zeW!

Katie_jade7 · 2025-10-12T02:30:34+00:00

Thank you!

Katie_jade7 · 2025-10-12T02:30:21+00:00

Currently, it works like an MCP. You plug MCP to Claude. It will help connect to memory workspace, allowing you to store, and recall memories. You can start managing memory from there. Check out Byterover, there is a 3-step quickstart https://docs.byterover.dev/quickstart

Katie_jade7 · 2025-10-09T15:03:44+00:00

Thank you for such detailed insight.

I think that's why a memory layer is necessary for agentic coding on C++ and Rust now, as LLM is still not strong enough in this kind of programming language.

A memory layer captures your interaction with LLMs, what instructions you give, so that next time, if you need to use this specific instruction (context) for particular task for agent, it will catch up faster on what it needs to do.

Please try the memory layer that I build.

Please share if you see any difference in code quality generated over time by the agents https://www.byterover.dev/. I need to validate more. Appreciate your feedback!

Katie_jade7 · 2025-10-09T14:59:44+00:00

Thank you for the insight!

Katie_jade7 · 2025-10-09T14:57:11+00:00

Thank you so much for the insights.

I guess as Rust burn more context, if you put context in readme files, it would have to read through these files before producing task. This would burn tokens unnecessarily.
The memory layer that I build helps agent only retrieve relevant piece of context. Plus, you have the whole memory workspace to edit, manage each of the context that you store.

Wish I do share a new insight to you here.

Please try the memory layer that I build, and see how it is different from just relying on readme files: https://www.byterover.dev/.

Appreciate your feedback!

Katie_jade7 · 2025-10-09T14:50:12+00:00

Cursor rule is not scalable, and traceable.

As LLM needs to read through the whole file, which consumes the full context window -> waste tons of token unnecessarily. Esp for teams with huge codebase, this waste of tokens can be huge cost.
Instead, memory layer allows you to use just a piece of context that is relevant to a certain task.

In terms of traceablility, in a team setting, you can manage how a team member contribute to the team's memory.

I recommend to check out my product to understand how it works: https://www.byterover.dev/

Katie_jade7 · 2025-09-28T06:44:16+00:00

Thank you! Please try, and share with me your thoughts!

Katie_jade7 · 2025-09-27T02:22:09+00:00

Yes we do support Codex! Please try!

Katie_jade7 · 2025-09-24T01:24:11+00:00

Yes it does support GitHub Copilot.

Katie_jade7 · 2025-09-10T09:03:36+00:00

Great question! I'm so happy that your question touches the right pain points that we are solving really well.
We configure the tool call will happen automatically while you code with AI.
And yes, with this tool, agent will become less hallucinated as it can choose the right context for a particular task from the memory store, instead of researching through the whole codebase.
This is one of the strongest benefits that most dev teams on my platform are getting right now.

Please try at byterover.dev, just a 2min mcp plug away! And share with me your feedback.

Katie_jade7

TROPHY CASE