IX2-0551
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Klaud Cold AI Usage - Line hint:
780
Context Before
Shortly after the release of InferenceX v1, we realized how much developer throughput was being left on the table by not utilizing AI more in our InferenceX development. So, we rolled our sleeves up and decided to embrace Claude Code and begin absorbing intelligence, one token at a time to the point that we are currently spending at a 3 million dollars’ worth of Claude intelligence, apply here to join the mission. ↗ We started our enlightenment journey when we realized the GitHub Copilot agent was free – at first we couldn’t believe this feature came at no cost! We soon realized that Copilot is terrible and it became apparent why GitHub was giving it away for free. You probably would have had to _pay us _to keep using it.
We had been using Claude Code locally ever since it was released. But recently, we have integrated Claude Code into InferenceX development, using it for the usual tasks such as reviewing PRs, but we also have given it the ability to perform sweeps on clusters. With the workflows we setup, Claude can manually initiate runs, view the results, and iterate. This has enabled us to deploy quick fixes easily on the go via the GitHub app.
Evidence
With our Claude Code integration, we simply open an issue and ask Claude to search through all commits in the image changelog to find necessary changes to be added to the recipe
Context After
GitHub Actions
In the spirit of open source, all runs occur on GitHub Actions, so benchmark results are verifiable, transparent, and reproducible. However, GitHub outages have been a constant obstacle to our goals recently. We have seen more unicorns lately than any other animal ↗! But maybe it’s time for us to touch some grass.
② Atomic Claim
透過 Claude Code 整合，團隊只需建立 issue，要求 Claude 搜尋 image changelog 的所有 commits，以找出 recipe 必須加入的變更。
- Epistemic Mode:
ASSERTED - Mapping Status:
COMPLETE
③ Semantic Frame
{
"additional_nodes": [],
"frame_type": "RELATION",
"object": {
"id": "04_knowledge_base/Anthropic Claude",
"label": "Claude"
},
"predicate": "INTEGRATES",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"subject": {
"id": "04_knowledge_base/Claude Code",
"label": "Claude Code"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| subject | Claude Code | 04_knowledge_base/Claude Code |
| object | Claude | 04_knowledge_base/Anthropic Claude |
⑤ Human Review
請在 Properties 逐項確認：
- 原文 → Atomic Claim 是否忠實
- Atomic Claim → Semantic Frame 是否忠實
- Canonical Entity mapping 是否正確
- Epistemic mode 是否保留原文語氣
- 最後選擇
review_action
Review state
Markdown 內文不是正式 approval。只有 Apply bridge 寫入的 Decision Ledger event 才是正式決策。