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LocalLLM Technical Architecture Documentation
Documents the architecture of an Android app that runs LLMs locally using llama.cpp, with RAG support.
⚒️ StudyForge: Implementation Plan
Outlines a phased implementation plan for an AI-powered study app that ingests PDFs, generates quizzes and flashcards, and runs locally via Ollama.
🔥 FIRE — Freedom Intelligent Routing Engine
Routes customer support tickets through LLM classification, geocoding, and a cascade of business rules to assign each to the best manager.
An Introduction to Transformers
Presents a slide deck introducing transformer architecture, attention mechanisms, and a PyTorch implementation from scratch.
scope
Defines a Python tool that downloads YouTube transcripts and comments, then runs a 3-stage AI pipeline to produce topic summaries and atomic insights with hybrid search.
Building a RAG Pipeline from Scratch
Teaches how to build a modular RAG pipeline from scratch with chunking, embeddings, vector storage, retrieval, and generation.
Startup Ecosystem Platform — Implementation Plan
Breaks a full-stack startup platform into 10 ordered phases with checklists for backend, AI, and frontend implementation.
Document DB (MCP Server) Requirements Specification
Specifies a local vector server for Japanese documents using OpenAI embeddings and sqlite-vec, exposed as MCP tools for insert/find/delete.
SMART-CAM (Smart AI Camera) — Full Documentation
Documents an edge AI surveillance pipeline that detects people, extracts ReID embeddings, and tracks identities locally on a Raspberry Pi with optional Hailo NPU acceleration.
Socrates Development Plan
Lays out a four-phase build plan for an AI tutor with text, voice, diagrams, and vision features.
Enso Atlas: Technical Specification
Describes a modular, on-premise pathology AI platform with swappable foundation models, classification heads, and config-driven cancer-type projects.
arXiv Knowledge Base — Specification
Defines a complete arXiv paper ingestion pipeline with hierarchical chunking, entity extraction, vector search, REST API, and Claude MCP integration.
AI Code Migration Assistant — Architecture (Deployment‑Ready)
Defines a deployment-ready architecture for an AI-assisted code migration platform with 8 worker types, event bus, and PostgreSQL data model.
Contract RAG MVP — Plan
Defines a complete local-only contract analysis MVP with document upload, background processing, and RAG extraction via Ollama.
AI Chatbot Maker with TiDB Vector Search - Hackathon Plan
Outlines a hackathon plan for building an AI chatbot platform that ingests documentation and GitHub repos, uses TiDB vector search, and generates embeddable script tags.
Schema-Driven Document Extraction Service (Gently Take-Home)
Defines a full-stack document extraction service where document types are runtime data, not code, enabling zero-code addition of new schemas.
1. System Architecture
Blueprints a RAG-based SaaS for generating quizzes and summaries from uploaded documents using a decoupled Next.js, FastAPI, and Supabase stack.
Multilingual Agentic RAG (venv, Qdrant, NiceGUI, Gemini-001)
Defines a multilingual agentic RAG pipeline using Gemini embeddings, Tesseract OCR, Qdrant, and NiceGUI with CPU-first defaults.
Legal Brief Matcher: Technical Documentation
Documents a legal brief matching system that uses embeddings, citation analysis, and multi-factor confidence scoring to align arguments between opposing briefs.
CineChoice RAG Implementation Plan
Plans a RAG movie recommendation system with vector embeddings, semantic search, and AI-generated responses.
Vesper — Implementation Plan
Breaks a journal app build into 5 sequential phases with checkboxes, exit criteria, and file references for each task.
AI Scientist Lab Notebook — Architecture (V1)
Defines the full architecture for an AI scientist lab notebook with RAG, ingestion, and plotting pipelines.
Delivery Shield - Setup Guide
Guides setup of a monorepo refund system with mock and production modes, including MongoDB, Gemini AI, and CDP integration.
Quick Start Guide
Walks through installing dependencies, creating Supabase tables, and configuring API keys for a multi-agent AI assistant.