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pour-le-hommes/README.md


Languages-Frameworks-Tools


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pour-le-hommes's Streaks

🐍 My Contributions 🐍

snake eating my contributions




My Personal System Architecture

graph TD
    LifeUpSDK["LifeUp SDK"]
    TauriApp["Desktop App and Mobile App (full I/O)"]
    Website["Personal Website pourterra.com"]
    BE2["Second Serverless Backend (LifeUp)"]
    BE1["First Serverless Backend (LLMs + LifeUp DB)"]
    Supabase["Supabase"]
    AppData["AppData (Local Conversations & Triggers)"]
    GroqGemini["Groq, Gemini"]
    GitHubAction["GitHub Action (Daily Report)"]

    LifeUpSDK -- Fetch LifeUp Data --> TauriApp
    TauriApp -- Send Tasks (Only Desktop) --> LifeUpSDK
    TauriApp <-- For LLM Conversations --> BE1
    TauriApp <-- For LifeUp Analytics --> BE2
    TauriApp <-- For LLM and Analytics Caching --> AppData
    Website <-- For LLM Conversations --> BE1
    BE1 <-- For GenAI Generation --> GroqGemini
    BE1 <-- For GenAI Data --> Supabase
    BE2 <-- For Email Report Generation --> BE1
    BE2 <-- For LifeUp Data --> Supabase
    GitHubAction -- Send Email Report Request --> BE2
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Connection Semantics

  • Send: Subject A owns the data and actively transmits it to Subject B without solicitation. B does not request anything—A initiates the transfer. B only acknowledges receipt (success/failure), not transformation or response.
  • Fetch: Subject B owns the data, and Subject A initiates a request to receive it. B does not learn anything about A in the process—it simply fulfills the request. The interaction is one-way in function but initiated by the consumer.
  • For: A and B engage in mutual processing. The request leads to transformation or computation on both ends. Data, context, or state changes are involved in either or both systems. This is a purpose-driven collaboration.
  • LLM (Large Language Model): Narrow-scope, text-focused generative AI. Includes dialogue, summarization, and RAG operations like vector embedding or function calling—as long as they remain in the service of textual reasoning or output. Image, speech, or video tasks are excluded.
  • GenAI (Generative AI): Broad-scope, multi-modal generation. Encompasses all generative domains: text, audio, image, video. LLMs are a subset of GenAI, but not synonymous with it. Use when referring to the infrastructure or request path involving any generative capability.

Device Context

  • TauriApp (Desktop only): Full read/write access to LifeUp SDK
  • TauriApp (Mobile): Read-only to LifeUp SDK

Pinned Loading

  1. Siamese-Neural-Network Siamese-Neural-Network Public

    Initiated and led an in-depth exploration of Siamese Neural Networks for face recognition of my face with the help of research paper and YouTube. Leveraging the Siamese architecture, this project a…

    Jupyter Notebook

  2. Extract_Stratigraphic_Slices Extract_Stratigraphic_Slices Public

    Using data from Rohit Arora (Link at the bottom) This is my take in the data given about extracting channels from stratigraphic slices, the base code is using Z Score Transformation and MLPClassifier

  3. Seismic-Hazard-Function Seismic-Hazard-Function Public

    A simple creation of a seismic hazard function to determine to potentiality of earthquakes

    Jupyter Notebook

  4. Streamlit Streamlit Public

    My Personal Website with LLM and Government Statistics Available

    Python 1

  5. CTk CTk Public

    This project demonstrates video integration using CTk as the GUI framework and Gemini for multimodal testing with Text-to-Speech. The goal is to simulate a demo similar to the ChatGPT demo, showcas…

    Python

  6. myPM myPM Public

    This project demonstrates my RAG through function calling. Though this isn't available to use right away, this repository will retrieve information based on the the two functions available, retriev…

    Python