I'm trying to start working on a project to create a personalized AI language model that evolves based on user interactions and feedback. Inspired by Ted Chiang's "The Lifecycle of Software Objects," I envision AI companions that learn and grow with their users, acting as personal assistants, tutors, or friends.
My plan is to use an open-source LLM like Meta's Llama or Stanford's Alpaca, host it on a personal server or low-cost cloud service, and fine-tune it based on conversation history and user feedback.
I'd appreciate your thoughts on:
- Feasibility and potential challenges.
- Best practices for fine-tuning LLMs and effective feedback mechanisms.
- Ideas for user-friendly interfaces that encourage interaction.
- Ethical considerations, privacy concerns, and potential biases in the learning process.
- Relevant experiences or resources you'd recommend.
Quick context on "The Lifecycle of Software Objects," by Ted Chiang: The story explores the concept of digital beings called "digients" that evolve and learn through interactions with their human "parents" in a virtual environment.For experienced programmers (Python, JS, Java, Kotlin, etc.), what's the best way to pickup modern C++?
What are best resources you have come across for C++?
Would appreciate any leads on where can i find any reading material or experiences of folks having done such transitions & how they managed team incentives/KPI alignment to enhance overall productivity of the organisation...
tl;dr Need to move from (Messy team + Messy codebase) => (Clean team + codebase structure) for better productivity. Any guidelines or stories on this from experiences?