feat: index Reader LM 1.5B + BGE-M3 for OSINT pipeline jinaai/reader-lm-1.5b (safetensors, 1 shard, 3.1 GB): HTML→Markdown local model. No Jina API needed. bgz7 → palette → O(1) HTML structure recognition. CompendiumLabs/bge-m3-gguf (GGUF F16, ~1.2 GB): Multilingual embedding model. Replaces DeepNSM for non-English. bgz7 → palette → O(1) semantic similarity. Together: Reader LM reads the web, BGE-M3 embeds it, AriGraph stores it as SPO triplets, AutocompleteCache routes it at 17K tok/sec. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK#72
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…-graph ndarray is the foundation (SIMD, GEMM, HPC, Palette, Base17). lance-graph is the spine (planner, strategy, AutocompleteCache). The REST server should live where the planner lives. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Added: 34 styles, CausalEdge64, AVX-512 config, SPO benchmark, architecture rule (ndarray=hardware, lance-graph=thinking, p64=highway). https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
jinaai/reader-lm-1.5b (safetensors, 1 shard, 3.1 GB): HTML→Markdown local model. No Jina API needed. bgz7 → palette → O(1) HTML structure recognition. CompendiumLabs/bge-m3-gguf (GGUF F16, ~1.2 GB): Multilingual embedding model. Replaces DeepNSM for non-English. bgz7 → palette → O(1) semantic similarity. Together: Reader LM reads the web, BGE-M3 embeds it, AriGraph stores it as SPO triplets, AutocompleteCache routes it at 17K tok/sec. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
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AdaWorldAPI
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