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Thank you for sharing this. Sorry for a possible noob question. How are embedding generated? Does it use a hosted embedding model? (I was trying to understand how is semantic search implemented)
(seems like there's some vague future plans for models like all-MiniLM-L6-v2, all-mpnet-base-v2)
https://github.com/jerpint/context-llemur
Although I developed it explicitly without search, and catered it to the latest agents which are all really good at searching and reading files. Instead you and LLMs cater your context to be easily searchable (folders and files). It’s meant for dev workflows (i.e a projects context, a user context)
I made a video showing how easy it is to pull in context to whatever IDE/desktop app/CLI tool you use
>MCP server (requires Boost) I see stuff like this, and I really have to wonder if people just write software with bloat for the sake of using a particular library.
How is savings of 40% on a typical codebase possible with block-level deduplication? What kind of blocks are you talking about? Blocks as in the filesystem?
Most “memory” layers I’ve seen for AI are either overly complex or end up ballooning storage costs over time, so a content-addressed approach makes a lot of sense.
Also curious — have you benchmarked retrieval speed compared to more traditional vector DB setups? That could be a big selling point for devs running local research workflow
Thank you for sharing this. Sorry for a possible noob question. How are embedding generated? Does it use a hosted embedding model? (I was trying to understand how is semantic search implemented)
It, uh... generates mock embeddings? https://github.com/trvon/yams/blob/c89798d6d2de89caacdbe50d2... (seems like there's some vague future plans for models like all-MiniLM-L6-v2, all-mpnet-base-v2)
not trying to be a hater but how is 100mb/s high performance in 2025? that's as performant as a 20 years old hdd