Continuous integration

Workflow runs for Continuous integration

• fix: relax golden-step hydration timing assert (100μs → 10ms) Debug builds + loaded CI runners can spike to 150μs for a 4096-element golden-step projection. The 100μs assert was too tight. 10ms is generous while still catching actual regressions (operation is ~10μs release). https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp

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  Continuous integration#123:Pull request#79openedbyAdaWorldAPI
  claude/setup-embedding-pipeline-Fa65C

  Apr 3, 20261m 50s
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• Merge pull request #78 from AdaWorldAPI/claude/setup-embedding-pipeli…

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• refactor: bgz17_bridge.rs fully rewired to crate::simd::I32x16 Zero raw _mm512_/_mm256_/_mm_ intrinsics remaining. All 5 kernels rewired (92 intrinsics → 0): L1 distance: from_i16_slice → sub → abs → reduce_sum L1 weighted: same + from_array(WEIGHT_VEC) → mul Sign agreement: from_i16_slice → xor → cmpge_zero_mask XOR bind: from_i16_slice → xor → to_i16_array Inject noise: from_i16_slice → add → simd_min/max → to_i16_array AVX2 2-pass patterns collapsed: polyfill I32x16 absorbs the split internally (array-backed [i32; 16] on AVX2, native __m512i on AVX-512). LazyLock runtime dispatch preserved. #[target_feature] preserved. Scalar fallbacks untouched. 19/19 bgz17_bridge tests pass. 1514/1515 full suite pass (1 pre-existing timing flake in vml.rs). https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp

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  Continuous integration#121:Pull request#78openedbyAdaWorldAPI
  claude/setup-embedding-pipeline-Fa65C

  Apr 3, 20261m 43s
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• Merge pull request #77 from AdaWorldAPI/claude/setup-embedding-pipeli…

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• feat: I32x16 Base17 ops — from_i16_slice, abs, to_i16_array, cmpge_zero_mask Added to all 3 tiers (AVX-512 / AVX2 / scalar): from_i16_slice(&[i16]) → I32x16 — load 16×i16, sign-extend to 16×i32 abs() → I32x16 — absolute value per lane to_i16_array() → [i16; 16] — narrow 16×i32 back to 16×i16 cmpge_zero_mask() → u16 — bit mask where lane >= 0 These are the primitives bgz17_bridge.rs needs to replace its 92 raw intrinsics with crate::simd::I32x16 calls. Fixed duplicate abs() in AVX-512 I32x16. 19 bgz17_bridge tests pass. https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp

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  Continuous integration#119:Pull request#77openedbyAdaWorldAPI
  claude/setup-embedding-pipeline-Fa65C

  Apr 3, 20261m 48s
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• Merge pull request #76 from AdaWorldAPI/claude/setup-embedding-pipeli…

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• feat: U8x64 byte-level ops for palette codec, nibble, byte scan (Pumpkin/SD) Added to all three tiers (AVX-512 / AVX2 / scalar): cmpeq_mask(other) → u64 — byte-wise equality, returns bitmask shr_epi16(imm) → Self — shift right 16-bit lanes (nibble extract) saturating_sub(other) — max(a-b, 0) per byte (delta subtraction) unpack_lo_epi8(other) — interleave low bytes (nibble interleave) unpack_hi_epi8(other) — interleave high bytes These operations are used by: palette_codec.rs — Minecraft-style variable-width bit packing nibble.rs — 4-bit light level packing (Pumpkin) byte_scan.rs — NBT format byte scanning (future) stable_diffusion/ — VAE latent palette encoding via GGUF All three are currently using raw _mm256_/_mm512_ intrinsics. Next step: rewire them to use crate::simd::U8x64 instead. https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp

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  Continuous integration#117:Pull request#76openedbyAdaWorldAPI
  claude/setup-embedding-pipeline-Fa65C

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• Merge pull request #75 from AdaWorldAPI/claude/setup-embedding-pipeli…

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• refactor: heel_f64x8 uses crate::simd::F64x8 polyfill, add SIMD cosine Replace raw std::arch intrinsics with crate::simd::F64x8 polyfill. Automatic dispatch: AVX-512 (native __m512d) → AVX2 (2×__m256d) → scalar. Consumer writes crate::simd::F64x8 — polyfill handles tier selection. Added SIMD cosine kernels using F64x8 FMA: cosine_f64_simd() — single-pass dot + norm_a + norm_b via F64x8 cosine_f32_to_f64_simd() — f32 input, f64 precision cosine dot_f64_simd() — F64x8 FMA dot product on f64 slices sum_sq_f64_simd() — F64x8 sum of squares 12 tests passing (6 HEEL + 6 cosine). https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp

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  Continuous integration#115:Pull request#75openedbyAdaWorldAPI
  claude/setup-embedding-pipeline-Fa65C

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• Merge pull request #74 from AdaWorldAPI/claude/index-llama-shards-A2Qzr

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  Apr 1, 20261m 57s
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• fix: HttpRangeReader stall detection, CDN re-resolve, HF API resolve Root cause: 21 GB tensor reads stall silently when HuggingFace CDN drops the TCP connection. curl sits forever with no data. Fixes: - --speed-limit 100000 --speed-time 30: abort if < 100 KB/s for 30s - Re-resolve URL on 403 (CDN token expiry after ~1 hour) - Segment-aligned fetches (no overlapping reads on sequential access) - 6 retries (was 4) with capped exponential backoff (max 32s) - from_hf() constructor: resolves via huggingface_hub Python API first, falls back to curl HEAD, then HF REST API. Stores repo/filename for automatic re-resolution on token expiry. - resolve_hf_url() tries 3 methods: Python HF API → curl HEAD → REST API https://claude.ai/code/session_01HmdXNPit7QsTCfhJFef3Ee

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  claude/index-llama-shards-A2Qzr

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• Merge pull request #73 from AdaWorldAPI/claude/qwen-claude-reverse-en…

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• feat: hhtl_cascade_search score_fn callback — LEAF level pluggable score_fn(row, col) -> f32 replaces the hardcoded 1.0 placeholder. lance-graph passes LanceDB VectorSearch as the LEAF backend. ndarray provides the cascade logic. lance-graph provides the data. 23 p64 tests passing. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK

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• Merge pull request #72 from AdaWorldAPI/claude/qwen-claude-reverse-en…

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• 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

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  Continuous integration#109:Pull request#72openedbyAdaWorldAPI
  claude/qwen-claude-reverse-eng-vHuHv

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• Merge pull request #71 from AdaWorldAPI/claude/qwen-claude-reverse-en…

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• bench: 4096-head SPO throughput — 611M lookups/sec, 18K tokens/sec

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  claude/qwen-claude-reverse-eng-vHuHv

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• Merge pull request #70 from AdaWorldAPI/claude/qwen-claude-reverse-en…

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• feat: axum OpenAI-compatible REST server (--features serve)

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  claude/qwen-claude-reverse-eng-vHuHv

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• Merge pull request #69 from AdaWorldAPI/claude/transcode-deepnsm-rust…

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• data: Llama 4 Scout BF16-direct shards (full downloads, 78.4 MB total)

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• Merge pull request #68 from AdaWorldAPI/claude/qwen-claude-reverse-en…

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• Claude/qwen claude reverse eng v hu hv

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• Merge pull request #67 from AdaWorldAPI/claude/qwen-claude-reverse-en…

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• Claude/qwen claude reverse eng v hu hv

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  Continuous integration#99:Pull request#67openedbyAdaWorldAPI
  claude/qwen-claude-reverse-eng-vHuHv

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