From vague intent to executable enterprise work. 從模糊需求，到可執行的企業工作。
Joe turns a heterogeneous NVIDIA edge-GPU cluster into an observable AI workforce that can develop software, generate demo videos, manage network operations, monitor WAN Load Balance, and deliver verified enterprise artifacts. Joe 將 DGX Spark、RTX 5090、Jetson Nano Super 與 PC 組成的異質 NVIDIA 算力叢集，轉換成可觀測、可派工、可驗證的 AI 工班，可自動開發程式、製作 demo 影片、管理網路、監控 WAN Load Balance，並產出可驗證的企業交付成果。
Joe's cluster is now fully decentralised. If the GB10 head node (1.125) goes down, every critical service — task queue, service discovery, AI inference, worker agents — continues operating automatically on the remaining nodes. Joe 叢集現已完全去中心化。即使 GB10 主節點（1.125）當機，所有關鍵服務——任務佇列、服務發現、AI 推理、工作代理——都會在其他節點上自動繼續運作。
Deployed 2026-05-31 — Joe now monitors and fixes itself 部署於 2026-05-31 — Joe 現在可自我監控並自動修復 2026-05-31 デプロイ済み — Joeは自律的に監視・修復します Desplegado el 2026-05-31 — Joe ahora se monitorea y repara solo Triển khai ngày 2026-05-31 — Joe giờ đây tự giám sát và tự sửa lỗi
AirCore is the AirLive backend server for AI-assisted network operations. It helps teams manage devices, inspect live status, troubleshoot faster, and move from detection to action with less manual work.
Built for AirLive users who want a central control layer for visibility, reliability, and operational speed.
A vague request is not the end of the workflow. For Joe, it is the starting point for task decomposition, routing, execution, verification, and delivery. 模糊需求不是工作流程的終點，而是 Joe 進行任務拆解、路由、執行、驗證與交付的起點。
Most enterprise requests start vague. A chatbot can answer, but Joe can decompose the request, route it to the right models, skills, and devices, execute the workflow, verify the output, and publish delivery artifacts. 多數企業需求一開始都是模糊的。聊天機器人只能回覆文字，但 Joe 可以拆解任務、分派模型與技能、執行工作流程、驗證結果，並產出可交付成果。
Hidden Real Need — 不是一段建議，而是 repo inspection、需求拆解、程式碼修改、測試、修錯、README 更新與部署資料。
What Joe Delivers:
Why Chatbots Are Not Enough: 一般 chatbot 只能告訴你怎麼做，Joe 可以進入 repo、改檔案、執行指令、修錯並產出可驗證的開發成果。
Hidden Real Need — 影片腳本、分鏡、系統截圖、架構圖、字幕、縮圖、MP4 輸出與可提交的 demo package。
What Joe Delivers:
Why Chatbots Are Not Enough: 一般 chatbot 只能寫影片腳本，Joe 可以截圖、產生素材、合成影片並輸出 MP4、字幕與縮圖。
Hidden Real Need — 專案定位、技術敘事、README、demo page、影片腳本、架構圖、submission answers、NVIDIA ecosystem usage 與可驗證成果。
What Joe Delivers:
Why Chatbots Are Not Enough: 一般 chatbot 只能幫你寫文字；Joe 可以同時更新網站、GitHub、簡報、影片素材與提交資料。
Hidden Real Need — 連線品質、服務狀態、DNS、gateway、routing path、延遲、封包遺失、log 蒐集與診斷報告。
What Joe Delivers:
Why Chatbots Are Not Enough: 一般 chatbot 只提供排查建議；Joe 會實際執行檢查、收集證據並輸出可交付診斷報告。
Hidden Real Need — 多 WAN 線路狀態、failover 行為、latency、packet loss、gateway、active route、頻寬使用與健康報告。
What Joe Delivers:
Why Chatbots Are Not Enough: 一般 chatbot 只能解釋 WAN Load Balance 原理；Joe 可以實際檢查多線路狀態、路由結果與 failover 狀況，並產出可閱讀的維運報告。
| Vague Intent | Chatbot Reply | Joe Execution |
|---|---|---|
| 幫我把網站功能做好 | 提供開發建議 | 讀 repo、改 code、測試、修錯、產出 diff |
| 幫我做 demo 影片 | 提供腳本 | 截圖、產生分鏡、字幕、合成 MP4 |
| 幫我參加 NVIDIA 黑客松 | 提供文字草稿 | 更新 GitHub、網站、架構圖、submission package |
| 幫我看網路問題 | 提供排查清單 | 執行檢查、蒐集 log、產生診斷報告 |
| 確認 WAN Load Balance | 解釋原理 | 檢查多 WAN、failover、route、latency、產出報告 |
Joe is the foreman. He hears the fuzzy request, picks the right skill, binds it to the right model on the right device, then verifies the output before it ships. Every step is logged, every artifact is downloadable, every mistake becomes a lesson note that feeds the next run. Joe 是工頭。他聽懂模糊需求，挑對技能、綁對模型、選對裝置，產出前先驗過。每一步都有日誌、每個產物可下載、每個錯誤都會變成「進化筆記」回饋下一次。
No external API calls in default mode. Data stays on the edge.預設不打外部 API，資料留在本地。
Every "done" claim ships with evidence — Playwright sweeps, file parses, curl 200s.每個「完成」都附證據 — Playwright 巡檢、檔案解析、curl 200。
Mistakes captured as lesson notes; promoted into canonical patterns after 3 repeats.錯誤化為 lesson note；同類重複 3 次後升級為標準範式。
Joe is not just a content generator. It is a local AI workforce for real enterprise operations. Joe 不只是內容產生器，而是一個面向真實企業維運的地端 AI 工班。
Joe assists with repo-level development — requirement interpretation, code generation, modification, debugging, test execution, error fixing, README updates, and deployment scripts.Joe 可協助 repo 級程式開發 — 需求理解、程式碼生成、修改、除錯、測試執行、錯誤修正、README 更新與部署腳本產生。
Joe generates demo videos — screenshots, title cards, architecture diagrams, subtitles, scripts, thumbnails — assembled into MP4 with Python and FFmpeg.Joe 可自動製作 demo 影片 — 系統截圖、標題卡、架構圖、字幕、腳本、縮圖，透過 Python + FFmpeg 合成 MP4。
Joe can be extended as a network ops agent — monitoring local services, checking connectivity, inspecting device status, generating diagnostic reports, and assisting with infrastructure troubleshooting.Joe 可作為自動化網路管理 Agent — 協助監控本地服務、檢查連線、檢視設備狀態、產生診斷報告，並支援基礎設施除錯。
Joe assists with WAN Load Balance monitoring — multi-WAN status, failover behavior, latency, routing paths, bandwidth usage, and operation reports.Joe 可協助 WAN Load Balance 監控與管理 — 多線路狀態檢查、failover 行為觀察、延遲監測、路由路徑確認、頻寬使用分析與管理報告產生。
Joe operates as a software development agent — reads project requirements, inspects repositories, modifies code, generates code diffs, executes tests, fixes errors, updates documentation, and prepares deployment artifacts. Joe 可作為自動化程式開發 Agent，能讀取需求、檢查 repo、修改程式碼、產生 code diff、執行測試、修正錯誤、更新文件並準備部署資料。
Requirement ↓ Repo Inspection ↓ Task Planning ↓ Code Generation / Modification ↓ Test Execution ↓ Error Fixing ↓ Documentation Update ↓ Deployment Artifact
Joe generates product demo videos — browser screenshots, architecture diagrams, scripts, subtitles, thumbnails — final MP4 assembled with Python and FFmpeg. Joe 可自動產生產品 demo 影片，含瀏覽器截圖、架構圖、腳本、字幕、縮圖，並以 Python + FFmpeg 合成 MP4。
Web Page / Dashboard ↓ Screenshot Capture ↓ Scene Planning ↓ Script + Subtitle Generation ↓ Title Cards + Architecture Diagrams ↓ FFmpeg Video Assembly ↓ MP4 + SRT + Thumbnail
Joe extends beyond document and code generation into infrastructure operations — monitoring local services, inspecting connectivity, collecting logs, generating diagnostic reports, and supporting WAN Load Balance monitoring across multiple network links. Joe 不只可處理文件與程式開發，也可延伸到基礎設施管理 — 監控本地服務、檢查連線、蒐集 log、產生診斷報告，並支援多線路 WAN Load Balance 監控。
| Capability | Description |
|---|---|
| Service Health Check | Check local services, ports, and response status |
| Connectivity Diagnostics | Inspect latency, packet loss, DNS, routing paths |
| Device Status Reporting | Summarize network device or node status |
| WAN Load Balance Monitoring | Monitor multiple WAN links and failover behavior |
| Routing Verification | Check active route, gateway, and path selection |
| Operation Report | Generate human-readable network operation report |
Three orthogonal axes. The Joe Router selects a triple per task, gated by CPU/VRAM headroom (Iron Law #29). 三個正交軸。Joe Router 為每個任務挑一組 (model, skill, device)，並以 CPU/VRAM 餘量為閘門（Iron Law #29）。
128 GB unified memory · arm64
Hosts Nemotron-3-Super, NemoClaw sandbox, primary orchestration brain.跑 Nemotron-3-Super、NemoClaw sandbox，主要編排大腦。
32 GB VRAM · Win11
Ollama on F: drive — Qwen2.5-coder, DeepSeek-R1, SDXL image gen.F: 槽跑 Ollama — Qwen2.5-coder、DeepSeek-R1、SDXL 出圖。
Jetson-Nano-Super-class
3B–8B always-on monitor, intent classifier, embedding endpoint.3B–8B 常駐監控、意圖分類、embedding 端點。
Jetson-Nano-Super-class
Redundancy + sharding peer for 1.63. Failover < 2s.1.63 的備援與分片夥伴，故障切換 < 2 秒。
Joe CLI, OpenClaw, Aider, portal, dashboard, git. Same physical PC on LAN+Wi-Fi.Joe CLI、OpenClaw、Aider、portal、儀表板、git。實體同一台 PC，LAN + Wi-Fi。
Intel Arc — image / video workloads fallback. Frees NVIDIA VRAM for LLM.Intel Arc — 影像 / 視訊工作備援，讓 NVIDIA VRAM 留給 LLM。
Not a Mixture-of-Experts inside one model — a Mixture-of-Experts at the system level. Joe treats every locally-served model as an expert and every superpower / aircore skill file as a routing key. 不是單一模型內部的 MoE — 是系統層級的 MoE。Joe 把每一個本地模型當作專家，每一份 superpowers / aircore skill 文件當作路由鍵。
NVIDIA-tuned reasoning. Joe's default brain.
Code, long-context, math specialists.
Small fast workers on Jetson-class nodes.
brainstorming · TDD · plan-writing · verification · systematic-debugging · …
device-adapter · iron-law-* · i18n-parity · xadmin-cloaking · …
3-repeat lessons promoted into Joe's canonical patterns.
Joe decomposes vague enterprise tasks into executable subtasks, routes each to the right model, skill, and compute node, then ships verified code, videos, reports, decks, HTML, and network diagnostics. Joe 可將模糊的企業任務拆解為可執行子任務，依任務性質派送到合適的模型、技能與算力節點，完成程式開發、影片製作、文件產出、網路檢查與診斷，並產出可驗證的交付資料。
Fuzzy intent accepted — bilingual, multi-domain.接受模糊意圖，雙語、跨領域皆可。
brainstorming + writing-plans skills emit step list.brainstorming + writing-plans 技能輸出步驟清單。
Reasoning model on DGX Spark binds intent → plan.DGX Spark 上的推理模型把意圖轉成計畫。
Each subtask gets a (model, skill, node) triple under VRAM/CPU gate.每子任務獲得 (model, skill, node) 三元組（受 VRAM/CPU 閘控）。
Coding · media · document · network skills run in parallel (Iron #10).程式 · 媒體 · 文件 · 網路技能平行執行（Iron #10）。
Logs · screenshots · tests · artifact checks (Iron #40.5).log · 截圖 · 測試 · 產物檢查（Iron #40.5）。
Code · videos · reports · PPTX · HTML · network diagnostics.程式 · 影片 · 報告 · PPTX · HTML · 網路診斷皆可。
Mistakes → lesson notes → promoted to canonical pattern after 3 repeats.錯誤 → lesson notes → 重複 3 次後升級為標準範式。
Mockups in preview v1 — to be replaced with live Playwright captures before final publish. 預覽 v1 為示意圖 — 正式發佈前會換成 Playwright 實機截圖。
Real files generated by Joe and served unauthenticated under /files/joe-hackathon/artifacts/.由 Joe 實際生成，無需登入即可下載。
input/output count, dollar-equivalent against GPT-4o.
name · quant · device · latency.
name · args · duration · exit status.
VRAM · power · queue depth.
stack · last 200 log lines · related lesson.
path · size · sha256 · public URL.
trigger · root cause · corrected pattern · promotion count.
every delivery commit + mail to operator.
This wave focused on low-risk, high-impact controls that keep daily automation running without blocking production tasks. 本次先上線低風險、高回報的守門機制，確保每日自動化持續運作且不阻塞正式任務。
| Dimension | Before | With Joe |
|---|---|---|
| Cost per delivery | $5–$50 cloud tokens | $0 · 100% local |
| Data residency | Sent to third-party cloud | Stays on edge cluster |
| Output format | Chat text only | PPTX · HTML · SVG · MD pack |
| Verification | "It looks right" | Playwright + parsers + curls |
| Observability | Black box | Per-token · per-tool · per-device |
| Self-improvement | Operator re-prompts | Lesson notes → canonical patterns |
| GPU utilization | Single big card | 6 heterogeneous NVIDIA nodes |
| Recovery | Manual rerun | Iron Law #40 verification gates |
