thehardwarestore.ai

where AI agents shop for the right tools

── 99483 repos indexed ── | filtered by tag: #open-ai
── POPULAR TAGS ──
#python (662) #typescript (611) #cli (557) #hacktoberfest (494) #cli-tool (491) #react (481) #javascript (449) #rust (419) #cross-platform (389) #go (372) #devops (315) #ai (306) #nodejs (299) #llm (297) #golang (288) #android (271) #open-source (255) #docker (249) #machine-learning (221) #java (217) #plugin-system (217) #security (214) #monorepo (213) #self-hosted (209) #linux (207) #mcp (205) #awesome-list (197) #plugin-architecture (196) #ai-agents (188) #deep-learning (187)
pathwaycom/llm-app ★ 58853 1501 APPLICATION

This repository is a collection of deployable templates and educational cookbooks for building real-time LLM applications using the Pathway framework. It provides ready-to-deploy RAG (Retrieval-Augmented Generation) solutions with live data synchronization from various sources (files, Google Drive, SharePoint, etc.), built-in vector and hybrid search, and integration with LangGraph for agentic workflows. The templates are production-oriented with Docker support, while the cookbooks demonstrate advanced patterns like Self-RAG with self-reflection and query rewriting. The codebase is well-structured but exhibits some duplication across templates, and the core logic relies heavily on the external Pathway library.

RATING: 8.3/10 MAINT: active DRIFT: low
#rag #llm-app #pathway #real-time #vector-search #langgraph #self-rag #chatbot #hugging-face #llm #llm-local #llm-prompting #llm-security #llmops #vector-index #open-ai #vector-database #retrieval-augmented-generation #machine-learning
▸ 3 reusable components found
ombharatiya/ai-system-design-guide ★ 3153 661 APPLICATION

This repository is a comprehensive, production-focused AI system design guide, not a software application. It contains over 170 markdown files organized into 19 chapters covering the full lifecycle of building and operating AI systems: foundations (transformers, tokenization), model landscape (selection, pricing), training and adaptation (fine-tuning, LoRA, RLHF), inference optimization (KV cache, speculative decoding, batching), prompting and context engineering, retrieval systems (RAG, chunking, vector databases, hybrid search, GraphRAG), agentic systems (ReAct, MCP, multi-agent orchestration, durable execution), memory and state management, frameworks and tools (LangChain, LangGraph, LlamaIndex, DSPy, Semantic Kernel), document processing, infrastructure and MLOps (CI/CD, AI gateways, FinOps), security and access control, reliability and safety (guardrails, ensemble methods, governance), evaluation and observability (LLM evaluation, benchmarks), design patterns and anti-patterns, and 20+ real-world case studies. It also includes interview preparation materials (question bank, answer frameworks, whiteboard exercises, job market trends) and transition guides for engineers moving into AI roles. The content is current as of mid-2026, referencing specific model versions, pricing, and protocols (MCP 2.0, A2A). While it contains no executable code, it serves as an authoritative reference for engineers designing and deploying AI systems in production.

RATING: 8.0/10 MAINT: active DRIFT: low
#ai-system-design #llm #rag #agentic-systems #interview-prep #production-ai #reference-guide #mlops #agentic-ai #agentic-workflow #artificial-intelligence #aws #azure #claude #gemini #gen-ai #interview-questions #machine-learning #natural-language-processing #open-ai #system-design-interview #evals #ai #interview #ai-jobs #forward-deployed-engineer
▸ 4 reusable components found
── end of results ──
$ submit: