Hi, I am Bluz Lan

Quotes, orders, inventory, and revenue in one place

Covers the full retail flow from quoting and deal tracking to stock pulls, pre-delivery inspection, and revenue reporting. Self-hosted and used in live daily operations.

• Quotes for new builds, used PCs, laptops, and repairs, with live preview and PDF / JPG export.
• Inventory with stock movements, stock-take records, average cost, and low-stock alerts; stock is pulled automatically when an order enters assembly.
• Revenue dashboard by month and year with profit, customer-source, and region breakdowns; customers are tiered by lifetime spend.
• Admin / staff roles with Passkey (Touch ID / Face ID) sign-in; pushes to master deploy automatically through a self-hosted runner.

Personal knowledge base with local LLMs and AI agent access

A Notion-style app for notes, databases, to-dos, and a journal, self-hosted and reachable only from my own Tailscale devices. Semantic search and Q&A run on local models, so notes never leave the machine.

• Semantic search: Ollama bge-m3 embeddings stored in PostgreSQL pgvector find notes even when the wording differs; pg_trgm handles Chinese keyword search.
• Ask mneme (RAG): ask a question in plain language; the most relevant passages go to a local LLM, which answers with [1], [2] citations back to the notes.
• MCP server: lets Claude Code search and read notes and append to work logs and dev logs, limited to chosen categories and to read and append only.
• Block editor, nested pages, databases (table / board / calendar), version history, and two-way sync with Google Calendar.

Kubernetes operator for serving local LLMs

Describe a model in one LLMService custom resource; the operator fetches the model, runs the inference server, and exposes an OpenAI-compatible API inside the cluster.

• Two engines, Ollama and llama.cpp, behind the same Service port and OpenAI-compatible /v1 path.
• A StatefulSet gives each replica its own model cache volume, so models download once and survive restarts; changing the model rolls the pods.
• GPU scheduling: setting gpus requests nvidia.com/gpu, and llama.cpp switches to the CUDA image and offloads every layer to the GPU.
• Status phases and conditions explain why a service is not ready (model download, image pull, or no free GPU); supports the scale subresource and HPA, with CEL validation at the API.

Local LLM speed and capability benchmark

Benchmarks the same model across three inference engines (Ollama, llama.cpp, and MLX) with one consistent method, producing an interactive HTML report and a PDF while keeping every run for comparison.

• Speed: time to first token (TTFT), output and prompt-processing throughput, load time, and peak memory.
• Capability: MMLU (57 subjects) and GSM8K at temperature 0 with a fixed seed, keeping every answer for review.
• Built on the Python standard library only; datasets are verified by sha256 after download.
• ECharts report with a sortable, filterable summary table, speed-vs-accuracy scatter plot, and run history.

Local AI image generation web app

Generates images on your own machine from the browser with FLUX.1-dev (NF4 quantized) and WAI Illustrious SDXL; images never leave the device.

• FastAPI backend and React + TypeScript frontend with a job queue, live progress, and time-remaining estimates.
• Detects hardware automatically: Apple Silicon (MPS) and NVIDIA GPUs (CUDA) on macOS, Windows, and Linux.
• One-click install and in-app updates with automatic backup and rollback, plus amd64 / arm64 container images.
• Prompt builder with an editable tag library and content-safety checks before generation.