myGPTs​

• Jamia: about Polkadot & JAM
• Spacesharks: about SpaceTech

這篇是我開始整理各種 "Claw" 工具時的第一份研究筆記。重點不是追熱點，而是先把堆疊分清楚：哪些是官方可驗證的事實，哪些是社群踩坑經驗，哪些目前只能當作待驗證線索。

截至 2026-05-24，我會把 Claw 生態先拆成三層來看：

• OpenClaw：個人 AI assistant / agent 的應用層，負責聊天、channels、tools、workspace、skills。
• NemoClaw：NVIDIA 做的 OpenClaw reference stack，把 OpenClaw 放進更可控的 OpenShell sandbox，補上 onboarding、policy、inference routing 與 lifecycle 管理。
• OpenShell：底層 sandbox / gateway / policy runtime，真正處理隔離、網路出口、檔案系統與推論路由。

接著 上一篇 Claw 堆疊筆記 之後，今天把 NVIDIA Agent Challenge 2026 的參賽計畫「Spacesharks Mission Desk」做了一次重要的重整。

題目沒換，stack 沒換，但我把這個 agent 要證明什麼 重寫了一次。

原本的版本是「把現有 Jamia / Spacesharks GPT 移植到 Nemotron stack」。今天的版本是：

一個低成本、多模型、能拿出證據、可以連跑 24 小時的衛星營運 copilot — 它的可信任性不是來自最大的模型，而是來自一個被刻意設計過的 trust stack。

這篇筆記紀錄這次重整的原因、四層信任架構，以及我為什麼把 scope 鎖到只有四個 ingestion source。

Taiwan is nearly a world-class leader in the upstream of the LEO satellite supply chain (RF PA, filters, high-frequency PCB), and has decent participation in the downstream (ground terminals, antennas) — but a structural void exists in the hottest new theme of 2025–2026: mid-stream C, Orbital Data Center (ODC) hardware integration.

This is not an accident. It is a structural opportunity window.

In March 2026, Jensen Huang said something on the GTC stage:

"Space computing, the ultimate frontier, has arrived."

This was not a metaphor. In November 2025, US startup Starcloud launched an NVIDIA H100 into orbit (Starcloud-1) and completed the first large language model training session in space. Two months later, on January 11, 2026, Axiom Space launched two orbital data center nodes (ODC Node 1 & 2), connected them to Kepler Communications' optical relay network, and began offering cloud compute services to external customers.

Earth orbit has officially become a new location class for data centers.

The traditional space industry has a simple answer to radiation: test everything, apply twice the design margin, and only use military-spec parts.

That approach made sense when launching a billion-dollar GEO satellite. It makes much less sense when you're a New Space startup trying to ship a CubeSat in nine months on a shoestring budget.

This post captures my current engineering thesis across AI systems, space infrastructure, blockchain, and RF validation.

This post is automatically generated for the week starting 2026-03-23.

Summary Prediction (Next Week)​

• Macro baseline: neutral
• US equities (S&P 500 / Nasdaq / Dow): neutral
• Crypto (BTC / ETH): neutral

Bugs​

HW​

• Design
  • symbol, package, pin
• PCB
  • CAM, X-section, DRC, trace mismatch
• PCBA
  • SMT, BOM, variant, alternative
• System
  • interference, interconnection

FW​

• OTP version of SOC, SOD
  • register, MCN#, variant
• Meta of platform
  • ADB command, code builder
  • regression screen, sanity check
  • review stage, feature progress

SW​

• test config.
  • dialog, profile, sequence, coverage
• prerequisites
  • lib dependency, config. path
• ATE
  • scripts, extension, package
• review
  • data parser, visualize, KPI

Environment​

• Comparison
• Reproduce
• ATE, extension, package
• Compatibility

General-purpose LLMs are powerful, but when it comes to deep analysis in specialized domains, they have two hard limitations: knowledge that lags behind the cutting edge, and factual dilution for niche fields. Take the Polkadot ecosystem as an example — key 2024–2026 developments like Agile Coretime, RegionX, and JAM are either unanswerable by general models, or return outdated information.

The solution is not a bigger model — it's giving the model a structured, trustworthy external knowledge base, then wrapping it with a persona and task prompt. That's the motivation behind my myGPTs.