21-year-old developer in China, working at the intersection of AIoT, edge AI, and embedded systems — and lately, at the boundary between AI and quantitative research.
| Area | Stack |
|---|---|
| Hardware | BGA rework, motherboard repair, edge device integration |
| IoT / Embedded | ESP32 / ESP32-S3 / ESP32-CAM, MQTT, multi-sensor fusion |
| AI / Edge | Ollama, llama.cpp, YOLOv8, Whisper, local LLM deployment |
| Agent Infra | DeepSeek Harness plugins, MCP servers, multi-model orchestration |
| Full-Stack | WSL2, Docker, FastAPI, Python 3.12 |
| Repo | What it is | Outcome |
|---|---|---|
| ashare-quant-research | Full engineering log: A-share screening + Qlib factor backtest + mutation-tested validation | 9 strategies validated, 0 survived — a negative-result study |
| Repo | What it is | Key number |
|---|---|---|
| dsh-multi-model-orchestration | Route tasks to Kimi / Qwen / GLM / DeepSeek by strength, with config self-check | 30 KB, pure local |
| dsh-laya-mcp | Typed-decision model as an MCP server for DeepSeek Harness | ~55 ms/item, zero tokens |
| Repo | What it is | Key number |
|---|---|---|
| Bonsai 27B on 8GB VRAM | Ternary 27B at 16K–64K context on RTX 4060 | 18.8–20.5 tok/s |
| Qwen3.5 4B on 8GB VRAM | Two 4B models at 64K context + "is uncensored really uncensored?" test | 4.3 GB, 47–60 tok/s |
| Repo | What it is | Key number |
|---|---|---|
| OmniForge | Full-modal private annotation (text/image/audio/video) + local AI pre-labeling | 20–30 person teams |
| AIGC Local Image Pipeline | Batch generation + API client on Stable Diffusion Forge | Fully local |
| SD Forge on 8GB VRAM | Deployment log for Forge on constrained hardware | CUDA 12.1 + Torch 2.12 |
| Repo | What it is |
|---|---|
| ESP32 Edge AI Security | Two-node (S3 + CAM) sensor gateway + local 4B multimodal judgement → TTS + WeChat |
| ESP32 PWM Fan Controller | DC motor control with transistor switch, Web UI, and OLED status |
- OmniForge — AI-assisted data annotation for startup clients
- DeepSeek Harness plugins — making multi-model orchestration actually reliable
- Squeezing big models into small VRAM — 27B on 8 GB is a solved problem now
- Honest negative results — the quant repo documents 9 dead strategies, because "this doesn't work" is worth publishing too
Document what actually happened, not what was supposed to happen.
Every repo here includes the traps, the silent failures, and the measured numbers — because the next person's afternoon is worth more than my vanity.
- GitHub: @Alexander390370
- Email: [email protected]
- [email protected]
"Bringing AI from the cloud to the edge, one device at a time."