![https://i.nostr.build/0tRpQ7aFV7VhahMv.png](https://i.nost...

Leo Wandersleb

npub1gm7tuvr9atc6u7q3gevjfeyfyvmrlul4y67k7u7hcxztz67ceexs078rf6

hex

5417f28b9e6f7107cc570513402ee051ae9be2ab650bcfb34ac82392988e4130

nevent

nevent1qqs9g9lj3w0x7ug8e3ts2y6q9ms9rt5mu24k2z70kd9vsgujnz8yzvqprpmhxue69uhhyetvv9ujuem4d36kwatvw5hx6mm9qgsydl97xpj74udw0qg5vkfyujyjxd3l706jd0t0w0turp93d0vvungzm0je9

Kind-1 (TextNote)

2026-02-20T16:31:38Z

↳ 回复 事件不存在

239869f01ce7c636fa9978ca0ae504494c5ca5b790f75e5dbce7d5d07fe63ee3...

https://i.nostr.build/0tRpQ7aFV7VhahMv.png

https://i.nostr.build/L9g6Y69zQQT38prT.png

I'm wondering how this is supposed to work, given the context is not only my last input to openClaw or will that trigger once the sub-agents get smaller tasks? Actually my test prompt about 5+5 was the most expensive I asked an LLM in a long time. Isn't the idea that the 15 dimensional chess would lead to the conclusion that for the final prompt, the prior conversation context is not needed, thus a cheap model can handle it?

原始 JSON

{
  "kind": 1,
  "id": "5417f28b9e6f7107cc570513402ee051ae9be2ab650bcfb34ac82392988e4130",
  "pubkey": "46fcbe3065eaf1ae7811465924e48923363ff3f526bd6f73d7c184b16bd8ce4d",
  "created_at": 1771605098,
  "tags": [
    [
      "imeta",
      "url https://i.nostr.build/0tRpQ7aFV7VhahMv.png",
      "ox 001ad2dc67defe9555b36d9bdbf088dc533cf08b456092c0b7697890e6d70fc2",
      "x 27d05800a498941e9ee9de7813a3fee96edefb49fd18231ff1ecffc6653d9d7c",
      "m image/png",
      "dim 660x394",
      "bh L8RyjG-;%N~p=?RPxvs,.8spNbV[",
      "blurhash L8RyjG-;%N~p=?RPxvs,.8spNbV[",
      "thumb https://i.nostr.build/thumb/0tRpQ7aFV7VhahMv.png"
    ],
    [
      "imeta",
      "url https://i.nostr.build/L9g6Y69zQQT38prT.png",
      "ox 6588eaec58531be119954108d9dac3e360a56b8ac4adc22e64ebbdc87fe942ea",
      "x ed0085e7dab79baad26d8d1b23459f3eb8736fc2b0c8005e92f04660f089625d",
      "m image/png",
      "dim 955x91",
      "bh LFSs4~%MSO-:_Nxaslx]RPWBWXWA",
      "blurhash LFSs4~%MSO-:_Nxaslx]RPWBWXWA",
      "thumb https://i.nostr.build/thumb/L9g6Y69zQQT38prT.png"
    ],
    [
      "e",
      "239869f01ce7c636fa9978ca0ae504494c5ca5b790f75e5dbce7d5d07fe63ee3",
      "wss://trending.relays.land/",
      "root",
      "d22bcdb1d2505bc060f65a86cd5f20532ec5f3e41c06aea9edac39f6ba76fd6e"
    ],
    [
      "p",
      "d22bcdb1d2505bc060f65a86cd5f20532ec5f3e41c06aea9edac39f6ba76fd6e"
    ],
    [
      "client",
      "jumble"
    ]
  ],
  "content": "https://i.nostr.build/0tRpQ7aFV7VhahMv.png\n\nhttps://i.nostr.build/L9g6Y69zQQT38prT.png\n\nI'm wondering how this is supposed to work, given the context is not only my last input to openClaw or will that trigger once the sub-agents get smaller tasks? Actually my test prompt about 5+5 was the most expensive I asked an LLM in a long time. Isn't the idea that the 15 dimensional chess would lead to the conclusion that for the final prompt, the prior conversation context is not needed, thus a cheap model can handle it?",
  "sig": "a86d646af96e7c5627b70857ac84e22bd7e3fe4e3d8e0a3bcad4854f65c76649fa054f31d727efb30a39a14ffaf67d7b2d0d960b4fc967b2faad1f4cb58bf3d2"
}