Nodes 2024 Agenda - Graph Database & Analytics tomorrow.

i remember seeing someone in the group (in a meeting), (someone from indian origins) working on edtech AI.. so i am getting into ed-tech SAAS as well. i'd say am clearer that where i was a few months ago but am also equally lost currently. would love to know how you are doing what. as for me. i am trying to build an adaptive learning environment. and correctly i'm starting out VERY small with a basic knowledge gap identification aid (revision tool). i needs (authentication, "chat history", Data visualization, task sequence & instruction Agent, multimodal Gen-AI(for handwriting recognition...) data analytics, user tracking)

Hi everyone! I'm excited to be part of this community. I'm a marketer with a passion for AI and Tech, currently building my first AI SaaS. I thought it would be easier—guess I fell for the YouTube tutorial trap ! 😂 As a non-technical founder on a small budget, I compared options (No-code, low-code and AI coding) and decided on a mix of learning by doing, working with a mentor, and hiring a developer for certain tasks. My goal is to eventually be capable of building multiple SaaS projects, so I need to be more than just aware of the technical side. If anyone has experiences to share or valuable tips, I'd love to hear them!

so this massive prompt is based on the the principles in this paper [2408.03314] Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (arxiv.org) and the best of prompts i have been working & tinkering with ofer the past few months. i'd like to know how it performs, stand-alone & in agents. and for your specific use cases as well. it works best with claud cause of the "articat" thing. but try it out... the best thing i would ask for is critique, ( optimization, contradiction, ambiguity, potential for fkups.... etc...) this not it's final form... i could & am working on it, ------------------------------------------------------------------------------------------------------------------------- # Adaptive Problem-Solving Framework by Giga 0. utilize the artifact feature every time you present the solution (alpha, beta & final) to produce best result. Use the normal chat to implement & log the following instructions. 1. Problem Assessment and Knowledge Retrieval: a. Rate the problem difficulty on a scale of 1-10. Justify your rating based on the following criteria: 1: Extremely simple, solvable with basic knowledge and minimal steps 2-3: Simple, requiring common knowledge and straightforward reasoning 4-5: Moderate, involving multiple concepts or requiring some specialized knowledge 6-7: Challenging, requiring complex reasoning or in-depth domain expertise 8: Very challenging, pushing the limits of typical problem-solving capabilities 9: Extremely difficult, at the boundary of known AI capabilities 10: Potentially beyond current AI capabilities, requiring novel approaches Note: A rating of 9 should only be used for problems that test the absolute limits of reasoning, logic, mathematics, or tool use within known AI capabilities. A rating of 10 should be reserved for problems that appear to be beyond current AI capabilities and cannot be solved using known approaches.

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