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3,528 documents
CineChoice RAG Implementation Plan
Plans a RAG movie recommendation system with vector embeddings, semantic search, and AI-generated responses.
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.
Quick Start Guide
Walks through installing dependencies, creating Supabase tables, and configuring API keys for a multi-agent AI assistant.
AI DevOps Copilot — ARCH.md
Defines the architecture for an AI-driven DevOps copilot that ingests signals, detects anomalies, correlates incidents, performs RCA, and automates remediation.
🚀 Vectro+ Visual Demo Guide
Walks through embedding compression, quantization, search, and benchmarking commands for the Vectro+ CLI tool.
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.
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.
HPC (High Performance Computing) bookmarks
Curates 200+ links to HPC resources: CFD codes, spectral methods, GPU solvers, multigrid libraries, and Fortran compiler projects.
End-of-term exam
Lists NLP and Python topics for an oral exam, with logistics for online Teams sessions.
Intelligent Document Query Platform — GitHub-ready Low-Level Design (LLD)
Provides a copy-ready low-level design for a serverless document query platform with vector search and LLM integration.
TODO — Dev Sentinel
Maps an 8-phase build plan for a CLI tool that detects developer frustration and generates learning notes from LLM transcripts.
Contextify - Implementasyon Planı
Defines a multi-phase plan for a local AI agent memory system with Go MCP server, PostgreSQL+pgvector, Ollama embeddings, and a web UI.
deep-cuts — Media Intelligence Pipeline
Defines a pipeline that ingests YouTube shows and podcasts, transcribes them, runs LLM analysis, stores results with semantic search, and generates infographic cards per episode.
The Simpsons Episode Database - Technical Plan
Plans a full-stack app that transcribes Simpsons episodes, creates searchable embeddings, and provides a web interface for semantic search and clip creation.
LokulMem — Product Requirements Document
Defines the full specification for a browser-native, zero-server memory library that gives LLMs persistent, inspectable user knowledge with decay and contradiction handling.
LO AI Sandbox Application
Documents a Spring Boot RAG application with pgvector, MCP travel services, and a Streamlit UI for document management and travel planning.
Building an AI Travel Assistant with Micronaut and LangChain4j
Builds a Micronaut travel advisor that embeds user queries, runs vector similarity search in Oracle AI Database, and returns semantically matched destinations via a chat endpoint.
Partie 2 : Pipeline RAG
Guides a student through forking, cloning, Dockerizing, and running a RAG chatbot with Streamlit, ChromaDB, and Google Gemini.
DinAI - RAG AI Chatbot Project Plan
Plans a four-phase RAG chatbot project, starting with a sequential web scraper for savollar.islom.uz and PostgreSQL/pgvector storage.
Sovereign Order Intelligence - Complete Documentation
Documents a full-stack supply chain AI platform with local LLM, vector store, web search, and agentic RAG workflow.
Contextinator v2.0 - Complete Usage Guide
Documents a Rust-powered CLI and Python library for fast filesystem operations plus an optional RAG pipeline for semantic code search with ChromaDB and OpenAI embeddings.
Implementation Plan: Ethnobotany Vectorized Database
Lays out an 11-week plan to build a semantic search engine for ethnobotany literature using RAG, vector embeddings, and multi-LLM chat.
WARP.md
Documents a Docker-based Django development environment for testing PostgreSQL extensions (PostGIS, TimescaleDB, pgVector) with Python 3.14 and uv.
GhostWriter Complete Setup Guide
Walks through setting up a full-stack app with a Go backend, React frontend, and iOS client, including Docker, database, and push notifications.