Recent submissions

Comment · 4 days ago
Fair point, a gitignored file has no version history. git-dupe gives you that, and worktree support.

Substrate, for example, keeps the history of edits to every fact, and the source of it.

Comment · 4 days ago
Comment · 5 days ago
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Comment · 5 days ago
We don't move session / skill files between tools. Instead the next tool gets the decisions from the last session, as well as the sources they were based on. The plugins are open source (MIT), the server isn't, and there's no self-hosted version yet. Is it enough to have the decisions and open questions, or do you need the full transcript?
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Comment · 9 days ago
The problem then is how to avoid a situation where 1 wrong fact is present in the common memory and then propagated to all agents. So you need to be able to see what's in the memory, correct it once and then make sure this change is propagated to all agents. This is 1 of the ideas we used when we were building up memory in Substrate. How do you store the shared memory?
Comment · 9 days ago
A markdown file works well as long as you don't use a second agent / tool that needs access to this same file. Otherwise, you will find that the copies of the file drift apart.

It's better to store the memory outside of all tools and then connect all of them to it via MCP. Then you have a single store where all of the tools can read from and write to.

This is one of the ideas we used when building up memory in Substrate. How many tools are using this file as memory?

Comment · 9 days ago
You can add a reference to the source session for each note in the vault. Then you could check if this is true in the actual session instead of trusting what the note says.

This is one of the ideas we used when we were building memory for Substrate.

Does your agent write to the vault automatically?

Comment · 9 days ago
If the skill wa already loaded into context tranplanting it into a new harness will move that skills context as well (not the scripts though).

What kind of memory has degraded your performance? Memory is quite tricky to tune and simple a direct implementations of an LLM Wiki concept can indeed have a lot of problems, as context accumulates.

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