(Post-WIP) How to evaluate inference engines

* People * David * Idea * For a given set of local LLM models that a set of users (or community) wants to use - how do you measure performance/UX on ways to serve it (based on ease of use, speed of processing and generation, level of accuracy, and features like multi-user-context

(Post-WIP) Comparing LLM inference engines (multi-user and multi-model)

* People * David * Idea * For a given set of local LLM models that a set of users (or community) wants to use - what is the best/easiest way to serve it (based on ease of use, speed of processing and generation, and features like multi-context and * Details * Engines like llama.

(Post-WIP) LangGraph Agentic spreadsheet matching/cleanup

* People * David * Idea * Use LangGraph workflows and nodes to collect and process QB export data in comparison to Po'owai DB export rows to generate annotated matching ids (focused on people, funding sources, and projects) * Details * LangGraph agentic workflows * Node-based Quickbooks Desktop Web Connector client * LangChain adapters for reasoning

Prototyping with v0

Prototype Description A web app / resource for ʻōlelo noʻeau that allows visitors to interact with the text in a dynamic way. Unique Problem, Curiosity, or Goal: These text are a unique resource into lāhui consciousness of the past. These sayings help to define an ʻōiwi mindset, and give insights to

AI Agent army

Joe + David assist for local AI Using AutoGen in order to test out using multiple agents in order to check the work of each other and hopefully make sure the data is cleaned up. Since it is microsoft project it hooks up the easiest by using OpenAI, but wanted to