{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreibovhfo2avfh4hw5gd6uxzegbrnp4dtvg4ji2izr24o36x2oghwp4",
    "uri": "at://did:plc:lk3jfj3zq4k4wxnk474axylu/app.bsky.feed.post/3meiwdn3oxmw2"
  },
  "path": "/t/kruel-ai-kv2-0-kx-experimental-research-to-current-8-2-api-companion-co-pilot-system-with-full-modality-understanding-with-persistent-memory/674592?page=26#post_508",
  "publishedAt": "2026-02-10T11:33:22.000Z",
  "site": "https://community.openai.com",
  "textContent": "Good questions. For the memory viewer\n\nit’s all render, no clustering. Every datapoint gets pushed to the GPU and\nrendered at once. We’re not doing any level-of-detail tricks or aggregation per zoom level. The\nSpark’s GB10 handles it fine with WebGL acceleration we send flat typed arrays straight to the GPU and let it chew through it. Viewport culling happens naturally but there’s no explicit clustering logic. It’s brute force with good hardware.\n\nOn the response speed side we actually already have streaming working in our KX system. It uses Server-Sent Events so tokens stream to the client as they’re generated, and TTS runs in parallel batched per sentence. So you’re hearing audio within a couple seconds while the rest of the response is still being generated. We’re looking at bringing that same approach into K9 for the main output pipeline.\n\nYour idea about pre-generating multiple predictions is interesting but doesn’t really work with our architecture. We’re not doing simple text completion each response goes through a full orchestration pipeline. Intent detection, tool selection, memory retrieval, belief checking, reasoning evaluation, emotional scoring and many other layers all of that has to run\nbefore we even start generating the actual response. You can’t speculatively branch that because the output depends entirely on which way it gets called and what comes back from memory and the knowledge graph. Two slightly different inputs could trigger completely different tool chains and pull completely different context. So pre-generating multiple candidates would basically mean running the entire cognitive pipeline multiple times in parallel on guesses, which is way more expensive than just waiting for the actual input.\n\nThe filler audio idea is interesting though sending a natural acknowledgment while processing. The issue is similar:\n\nour system doesn’t know what it’s going to do until it’s done reasoning about it, so even the filler would feel disconnected. The chunked streaming approach is the better fit for us. Stream the response as it generates, kick off TTS per sentence, and the user starts hearing the answer almost immediately. That’s what KX does and that’s what we’re bringing across.\n\nGood ideas though. got me thinking some more on one of the other Ai’s",
  "title": "Kruel.ai KV2.0 - KX (experimental research) to current 8.2- Api companion co-pilot system with full modality , understanding with persistent memory"
}