thehardwarestore.ai

Tandoor is a self-hosted, multi-tenant recipe management web application. The system is a monolithic Django application (cookbook app) exposing a Django REST Framework API, paired with a Vue 3 + Vuetify single-page frontend that consumes an auto-generated TypeScript OpenAPI client. Core production domains, verified in code, are: structured recipes (steps, ingredients, scaling), a tree-based hierarchical food database with nutritional properties and USDA FDC IDs, meal planning with iCal export, shopping lists with supermarket categories and offline sync queues, inventory/pantry booking, cookbooks, share links, and view/cook logging. Tenancy is enforced through Space/Household/UserSpace models with django-scopes middleware and granular guest/user/admin permission classes rather than through database row-level security. The largest subsystem is recipe import/export: an abstract Integration base class with 20+ adapters for other recipe managers (Paprika, Mealie, Cooklang, Gourmet, ChefTap, etc.), URL scraping via recipe-scrapers/microdata/pytubefix, cloud storage sync providers (Dropbox, Nextcloud, WebDAV, local), a bulk open-data importer for USDA-derived reference foods/units/conversions, and an embedded PDF.js viewer for external recipe files. A connector/event layer dispatches async updates to external services such as Home Assistant, a Telegram bot webhook writes shopping list entries, and recent AI features (litellm-based, per-space credit tracked) handle image/PDF recipe import, step sorting, and nutrition extraction. Background work is done via threads rather than a task queue. Craftsmanship is above average: clean abstract patterns (Integration, Provider, Connector ABCs), chainable queryset managers, mixin-based permission/merge/tree logic, a strong permission-matrix test suite, and genuinely reusable helper algorithms (BFS unit conversion, ingredient parsing, Cooklang parsing). However, the codebase carries notable debt: 240+ migrations with heavy schema churn, a ~3000-line monolithic api.py view file, committed auto-generated OpenAPI client code that can drift, vendored PDF.js binaries, some tests that suppress tenant scoping via scopes_disabled, and copy-paste duplication inside the open-data importer. Overall it is a well-maintained, actively developed application with solid architecture for its scale.

This repository is a cookbook of runnable examples for the OpenClaw SDK. It contains two layers: small one-concept recipes (e.g., run-an-agent, stream-events, cancel-a-run) and standalone starter apps (quickstart Node app, coding-agent CLI, agent-workbench React app, run-board React dashboard). The code demonstrates SDK workflows such as starting runs, streaming normalized events, cancelling work, reusing sessions, checking model status, and testing with an in-memory transport. The architecture is modular: recipes are self-contained TypeScript files with shared configuration helpers, and SDK examples are independent packages that can be copied out and adapted. The repository includes comprehensive tests, CI checks, and a workspace SDK shim for local validation without a live Gateway. It is actively maintained with modern dependencies (TypeScript 7, Node 22, pnpm 10.34.5) and follows strict formatting and linting rules.

This repository is a personal cookbook and project showcase centered on the MiniCPM family of large language models (MiniCPM, MiniCPM-V, MiniCPM3). It contains over a dozen distinct application projects, including multi-modal OCR + visual grounding (OCR_VG), cross-modal image-text search, ReAct-style agent planning (agent_auto_plan), MBTI personality role-play, low-memory RAG (4G_memory_rag), AWQ quantization, embedding extraction, SFT/DPO fine-tuning pipelines, and Windows agent demos. Each project includes source code, shell scripts, and accompanying Chinese/English tutorials in markdown format. The codebase is primarily a collection of independent, loosely coupled modules rather than a unified application. It demonstrates practical usage of MiniCPM models for fine-tuning, deployment with vLLM/Ollama/llama.cpp, and integration with LangChain. The quality is functional but inconsistent: many modules contain hardcoded paths, duplicated logic, and lack tests, yet several components (ReAct data generation, image slicing utilities, LangChain wrapper) exhibit sufficient isolation and craftsmanship to be reusable across projects. The repo is actively maintained with a recent commit, but dependency on unmerged forks and version-specific model files poses a medium technical drift risk.