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Add Calibrate AI Control UI and fleet LLM backend wiring.
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Operators toggle Logic gates vs AI Control on Settings, refresh local Ollama models, and save ai_endpoint settings via Calibrate PUT; server scheduler and agent snapshot/command paths support stateless 60s fleet decisions.
This commit is contained in:
AetherForge
2026-06-07 02:14:28 -07:00
parent 34afa28f81
commit 0002e5fd93
33 changed files with 2791 additions and 12 deletions
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4
server/web/src/api/client.test.ts
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@@ -237,6 +237,7 @@ describe('api client', () => {
.mockResolvedValueOnce(jsonResponse([]))
.mockResolvedValueOnce(jsonResponse([]))
.mockResolvedValueOnce(jsonResponse([]))
.mockResolvedValueOnce(jsonResponse({ models: ['llama3.2'] }))
.mockResolvedValueOnce(jsonResponse({ xmr_per_day: 0.01, usd_per_day: 1, network_hashrate: 1 }))
.mockResolvedValueOnce(jsonResponse({ success: true }))
.mockResolvedValueOnce(jsonResponse({ agent_id: 'a1', content: 'log' }))
@@ -253,6 +254,9 @@ describe('api client', () => {
await api.getAIActivity();
expect(lastFetch().url).toBe('/api/v1/ai/activity');
await api.getAIModels('http://127.0.0.1:11434/v1');
expect(lastFetch().url).toBe('/api/v1/ai/models?endpoint=http%3A%2F%2F127.0.0.1%3A11434%2Fv1');
await api.getEarningsEstimate(1234.5);
expect(lastFetch().url).toBe('/api/v1/earnings/estimate?hashrate=1234.5');