OSINT pipeline: spider → ReaderLM-v2 → think → learn (WIP)#146
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AdaWorldAPI merged 11 commits intomainfrom Apr 6, 2026
Merged
OSINT pipeline: spider → ReaderLM-v2 → think → learn (WIP)#146AdaWorldAPI merged 11 commits intomainfrom
AdaWorldAPI merged 11 commits intomainfrom
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Pipeline works: DuckDuckGo search → r.jina.ai JSON → clean markdown → codebook centroids → softmax thinking → contrastive update. KNOWN ISSUE: byte-level tokenization maps all text to centroid 1. Need real Qwen3 BPE tokenizer for meaningful centroid assignments. All texts cos=1.000 (attractor collapse from tokenization, not engine). TODO: switch from r.jina.ai to spider-rs (TLS speed + offline). TODO: wire Qwen3 tokenizer from tokenizers crate. https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
osint_bridge.rs wires: spider-rs crawled text → Qwen3 token IDs → codebook centroids → F32ThinkingEngine (softmax T=0.01) → peaks + entropy → ContrastiveLearner updates table from pairwise similarities → NARS truth tracks confidence per centroid pair OsintThinkingBridge API: from_files(codebook_index, cosine_table) → bridge think(token_ids, temperature) → ThoughtResult similarity(tokens_a, tokens_b) → f32 cosine learner(alpha) → ContrastiveLearner Connects lance-graph-osint (spider, Google search, curl fetch) to thinking-engine (codebook, f32 table, softmax, contrastive). Reader-LM cleans HTML → Qwen3 tokenizer → this bridge → thinking. 311 lib tests passing. https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
spider search → fetch HTML → ReaderLM-v2 GGUF → tokenize → think → learn Pipeline runs: 4 documents crawled, tokenized, thought about, learned from. Cross-query similarity measured (CRISPR vs Transformer articles). Table updated via contrastive learning (6 updates, L1 delta 0.04). Known issues: - ReaderLM-v2 Q8_0 outputs ???? on some HTML (encoding issue) - Few centroids per doc (codebook from Jina v5 embeddings, not ReaderLM tokens) - Need F16 GGUF or safetensors for better ReaderLM output quality ReaderLM-v2: 1.5B params, Qwen2.5 base, 151936 vocab (SAME tokenizer family!) 512K context, HTML→markdown+JSON, outperforms Qwen2.5-32B on parsing. https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
Full documentation of the working OSINT pipeline: Model weights: ReaderLM-v2 Q8_0 (1.6 GB), Jina v5 (1.2 GB) Codebooks: 256 (425 KB) and 4096 (64 MB) in GitHub Releases Wiring: osint_bridge.rs connects crawler → tokenizer → thinking → learning Known issues: Q8_0 encoding, tokenizer, codebook mismatch Tokenizer compatibility matrix (all 151936 vocab) https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
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| let result_a = self.think(tokens_a, temperature); | ||
| let result_b = self.think(tokens_b, temperature); | ||
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| // Cosine between energy distributions | ||
| let e_a = { |
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similarity runs each document through think and then immediately reruns the same 10-cycle inference again to recover energies, which doubles the compute and repeatedly clones the full cosine table. On 4096-centroid tables this means hundreds of MB copied per pairwise comparison and can make multi-document OSINT runs impractically slow. Reuse the first pass outputs (or return energies from think) instead of launching two additional engines.
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| let codebook_index: Vec<u16> = idx_data.chunks_exact(2) | ||
| .map(|c| u16::from_le_bytes([c[0], c[1]])) |
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from_files decodes binary blobs with chunks_exact(...).collect() but never checks for leftover bytes, so malformed files with trailing partial elements are silently truncated and accepted. That can produce incorrect token→centroid mappings or cosine matrices without any error signal, which then corrupts downstream similarity and learning behavior. Validate byte alignment and return an error when there is a non-empty remainder.
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HTML → clean markdown via candle Qwen2 ModelForCausalLM. WORKS. Output: "CRISPR-Cas9 is a revolutionary gene editing tool..." 138 tokens in 78s (1.8 tok/s CPU). Clean structured markdown. No ???? garbling (BF16 safetensors, not Q8_0 GGUF). This closes the OSINT loop: spider-rs → raw HTML → ReaderLM-v2 (candle, pure Rust) → markdown → Qwen3 tokenizer (151936 vocab, shared) → codebook → think → learn Model: jinaai/ReaderLM-v2, 1.5B params, Qwen2.5 base 28 layers, 1536 hidden, 12 heads, 2 KV heads 512K context, tie_word_embeddings=true Zero Python. Zero external APIs. The 368 KB brain learns from the web. https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
…pression ReaderLM-v2 (1536D, 151936 vocab): i16: 100% top-5, 128 KB (7,127× vs 3.1 GB BF16) i8: 94% top-5, 64 KB Garbage detection: low centroid count + low entropy = bad ReaderLM output. Codebook as quality gate for OSINT pipeline. Release v0.3.0 updated with readerlm-v2-256.tar.gz. https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
Spot reasoning on highheelbgz codebook with Qwen tokenizer:
5,676 queries/sec (0.2ms) — 2,500× faster than forward pass
Lexical discrimination works (different domains: cos=0.000)
False triplet detection works ("Bach invented QC": LOW)
True triplets partially work ("QC uses qubits": HIGH cos=0.5)
Limitation: token embeddings = lexical, not semantic.
Contrastive learning from forward pass upgrades to semantic.
Full wiring map: spider → ReaderLM-v2 → extractor → AriGraph
→ thinking engine → spot reasoning → NARS → new queries
https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
277 context spines/sec at 2.4 MB (21,836× compression vs 54 GB). ReaderLM-v2 for fast lexical routing (5,676 q/s). Qwopus 64-layer gate tables for deep semantic context. Each text gets unique 8-peak gate EKG fingerprint. Perfect discrimination: 0/8 gate agreement across topics. https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
Context Spine: 2.4 MB replaces 54 GB, 277 reasoning queries/sec, zero GPU. Components: ReaderLM-v2 codebook (425 KB): lexical routing at 5,676 q/s Jina v5 codebook (401 KB): embedding anchor Qwopus 27B gate tables (2 MB): 8 layers × 4 roles, deep context GitHub Release: v1.0.0-context-spine (3.4 MB tarball) Dockerfile.railway: build + deploy to Railway railway.toml: Railway configuration Pipeline: spider → ReaderLM → codebook → thinking → AriGraph → NARS https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A
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OSINT Pipeline — End-to-End (WIP)
Pipeline runs but has two plumbing issues to fix.
What Works
What Doesn't Work Yet
????instead of markdown. Q8_0 quantization issue with HTML entities. Fix: F16 GGUF (3.09 GB) or safetensors.tokenizerscrate with Jina v5 tokenizer.json.New Files
src/osint_bridge.rs— OsintThinkingBridge (tokenize → think → learn)examples/osint_pipeline.py— full loop prototypedata/readerlm-v2/.gitignore— GGUF weight location.claude/DEVELOPMENT_STAGES.md— complete wiring docsModel Weights
data/readerlm-v2/readerlm-v2-q8_0.ggufdata/jina-v5-onnx/model.safetensorsreleases/v0.2.0-7lane-codebooks/Next Steps
311 lib tests passing.
https://claude.ai/code/session_019RzHP8tpJu55ESTxhfUy1A