Mine called openwalrus is local-llm first written in rust:
builtin metasearch engine, graph based memory system, editing configs with commands (never need to edit the config files manually)...
we indeed need to focus on sort of real "use cases" first, since I just realized when I'm talking with others about it, the conversions are always meaningless, ends with no response, or sth like cool
Has anyone implemented a system of Pi for a team? Basically consolidate all shared knowledge and skills, and work on things that the team together is working on through this?
Basically a pi with SSO frontend, and data separation.
If no one has - I have a good mind to go after this over a weekend.
And others pull regularly from the pool? how are knowledge and skills continuously updated? I was thinking these necessarily need to be server side (like the main project under discussion) for it to be non-clunky for many users, but potentially git could work?
Like, let's take a company example - gitlab. If an agent had the whole gitlab handbook, then it'll be very useful to just ask the agent what and how to do in a situation. The modern pi agents can help build such a handbook with data fed in all across the company.
Can you do so with SQLite? Doesn’t seem possible. Agent is capable of writing code so is capable of interacting with file. Cannot remove write from agent because needs to put message.
Realistically, once you are using agent team you cannot have human in the loop so you must accept stochastic control of process not deterministic. It’s like earthquake or wind engineering for building. You cannot guarantee that building is immune to all - but you operate within area where benefit greater than risk.
Even if you use user access control on message etc. agent can miscommunicate and mislead other agent. Burn tokens for no outcome. We have to yoke the beast and move it forward but sometimes it pulls cart sideways.
Maybe this is a dumb question, but none of these "*Claw" setups are actually local, right? They are all calling out to OpenAI/Anthropic APIs and the models are running in some hyperscale cloud?
builtin metasearch engine, graph based memory system, editing configs with commands (never need to edit the config files manually)...
we indeed need to focus on sort of real "use cases" first, since I just realized when I'm talking with others about it, the conversions are always meaningless, ends with no response, or sth like cool
https://github.com/skorokithakis/stavrobot
I guess everyone is doing one of these, each with different considerations.
Sandboxing fixes only one security issue.
Basically a pi with SSO frontend, and data separation.
If no one has - I have a good mind to go after this over a weekend.
Like, let's take a company example - gitlab. If an agent had the whole gitlab handbook, then it'll be very useful to just ask the agent what and how to do in a situation. The modern pi agents can help build such a handbook with data fed in all across the company.
“””
Data Integrity
The SQLite database at /workspace/.piclaw/store/messages.db must never be deleted. Only repair/migrate it when needed; preserve data.
“””
Realistically, once you are using agent team you cannot have human in the loop so you must accept stochastic control of process not deterministic. It’s like earthquake or wind engineering for building. You cannot guarantee that building is immune to all - but you operate within area where benefit greater than risk.
Even if you use user access control on message etc. agent can miscommunicate and mislead other agent. Burn tokens for no outcome. We have to yoke the beast and move it forward but sometimes it pulls cart sideways.
The "mac mini" you install it on is a prop?
Eh screw the whole thing.