waggle-dance
RVS_GTDriver
waggle-dance | RVS_GTDriver | |
---|---|---|
5 | 1 | |
150 | 5 | |
- | - | |
9.9 | 10.0 | |
7 months ago | almost 4 years ago | |
TypeScript | Swift | |
MIT License | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
waggle-dance
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Show HN: Demystifying Advanced Rag Pipelines
This seems very similar to LangSmith’s trace monitoring, which I have been leaning on heavily for observability. You also mention LlamaIndex— how do you see your project fitting into the ecosystem?
This is a great README, but I don’t think I would able to use this because it is serial.
In my experimental agent system, waggledance.ai, I have been working on a pre-agent step of [picking and synthesizing the right context and tools](https://github.com/agi-merge/waggle-dance/blob/main/packages...) for a given subtask of a larger goal, and it seems to be boosting results. It looks like now I have to try sub-question answering in the mix as well.
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Autotab – Boring AI Agents for real world tasks
This is amazing. I will try to have it automate my system of agents web app (turtles all the way down) (shameless plug: https://github.com/agi-merge/waggle-dance)
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Ask HN: Show me your half baked project
- source code: https://github.com/agi-merge/waggle-dance
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Language Agent Tree Search Unifies Reasoning Acting and Planning in LMs
Any advice for trying to implement this in my project over at https://github.com/agi-merge/waggle-dance
Currently I am creating different agent types for planned subtasks using langchain, so perhaps implementing a custom AgentExecutor? Or would I need to lift it up higher in the logic stack? I am not sure that I understand how the graph search and thought-action-reflection selection process is deciding when and how to reflect if a branch fails, and how it backpropogates the failure to other nodes?
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Why AutoGPT engineers ditched vector databases
I have been working on a system of agents over at https://github.com/agi-merge/waggle-dance - I already split problems up into subtasks for agents to work on independently. I give agents access to vector databases, using a simple global key for now, but soon a context/parent/child key. Access to the vector DBs is proxied via tools (agents have to “call” saveMemory or retrieveMemory). I also check for looping/repetition FREQUENTLY using in-memory vector databases of the langchain agent callback events.
My opinion on this: eh, who cares? AutoGPT and similar are non-standard use cases for Vector DBs right now, and Vector DBs are useful for RAG.
RVS_GTDriver
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Ask HN: Show me your half baked project
Well, these ones aren't "half-baked," but they are no longer being maintained (archived):
[0] https://github.com/RiftValleySoftware/RVS_IPAddress
[1] https://github.com/RiftValleySoftware/RVS_ParseXMLDuration
[2] https://github.com/RiftValleySoftware/RVS_ONVIF
This project is unfinished (I just walked away from it, as it wasn't really giving me what I wanted):
[3] https://github.com/RiftValleySoftware/RVS_GTDriver
This one is "half-baked," I believe. I never really took it particularly far:
[4] https://github.com/RiftValleySoftware/RVS_MediaServer
What are some alternatives?
playwright-chrome-recorder - playwright-chrome-recorder
FXcursion - Guitar processor prototype