SaaS Metrics
PyTorch and ONNX Runtime tell you what came out. They can't tell you what actually ran to get there — which ops executed, in what order, on what inputs.A model gets packaged into a sealed .cnox container. SHA-256 is verified before a single op executes. Inference walks a fixed plan over a minimal opset. Every run can emit a per-op audit log: op type, output tensor hash, output sample — cryptographically linked to the exact container and input that produced it. If something goes wrong in production, you have a trail.Scalar backend today — reference implementation and permanent fallback when hardware acceleration isn't available. Audit and verification is identical across all backends. SIMD next, GPU after that.Input below is synthetic (all-ones) — pipeline is identical with real inputs.github.com/Coelanox/CLF
Audit example:
{
"schema": 2,
"run": {
"run_id": "59144ede-5a27-4dff-bc25-94abade5b215",
"started_at_unix_ms": 1776535116721,
"container_path": "/home/shark/cnox/models/output/bert_base_uncased.cnox",
"container_sha256_hex": "184c291595536e3ef69b9a6a324ad5ee4d0cef21cc95188e4cfdedb7f1f82740",
"backend": "scalar"
},
"input": {
"len": 98304,
"sha256_hex": "54ac99d2a36ac55b4619119ee26c36ec2868552933d27d519e0f9fd128b7319f",
"sample_head": [
1.0,
1.0,
1.0,
1.0
]
},
"ops": [
{
"op_index": 0,
"op_type": "Add",
"out_len": 98304,
"out_sample_head": [
0.12242669,
-4.970478,
2.8673656,
5.450008
],
"out_sha256_hex": "19f8aa0a618e5513aed4603a7aae2a333c3287368050e76d4aca0f83fb220e78"
},
{
"op_index": 1,
"op_type": "Add",
"out_len": 98304,
"out_sample_head": [
0.9650015,
0.23414998,
1.539839,
0.30231553
],
"out_sha256_hex": "7ae2f025c8acf67b8232e694dd43caf3b479eb078366787e4fdc16d651450ad4"
},
{
"op_index": 2,
"op_type": "MatMul",
"out_len": 98304,
"out_sample_head": [
1.0307425,
0.19207191,
1.5278282,
0.3000223
],
"out_sha256_hex": "44c28e64441987b8f0516d77f45ad892750b3e5b3916770d3baa5f2289e41bdd"
},
{
"op_index": 3,
"op_type": "Gelu",
"out_len": 393216,
"out_sample_head": [
0.68828076,
-0.0033473556,
1.591219,
-0.16837223
],
"audit_elided": "hash_skipped: len 393216 > max 262144"
}
Show HN: Coelanox – auditable inference runtime in Rust (BERT runs today)
A solution for transparency and debugging in AI model inference, addressing the lack of visibility in existing runtimes like PyTorch and ONNX. It provides a 'trail' for production issues.
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AI Executive Synthesis
A solution for transparency and debugging in AI model inference, addressing the lack of visibility in existing runtimes like PyTorch and ONNX. It provides a 'trail' for production issues.
Coelanox addresses a critical gap in AI model operationalization: the lack of auditable inference. Existing runtimes offer output but obscure the execution path. Coelanox provides cryptographic verification of model containers and detailed, per-operation audit logs, including output tensor hashes. This transparency is crucial for debugging, compliance, and ensuring model integrity in production environments. The ability to trace 'what actually ran' directly tackles a significant developer pain point in diagnosing AI system failures. For B2B SaaS, this offers a compelling value proposition for industries requiring high assurance, such as finance, healthcare, or defense. The market trend is towards greater explainability and accountability in AI systems, making auditable runtimes like Coelanox essential infrastructure for enterprise AI adoption.
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What is Coelanox – auditable inference runtime in Rust (BERT runs today)?
Coelanox – auditable inference runtime in Rust (BERT runs today) is analyzed by our AI as: A solution for transparency and debugging in AI model inference, addressing the lack of visibility in existing runtimes like PyTorch and ONNX. It provides a 'trail' for production issues.. It focuses on Coelanox addresses a critical gap in AI model operationalization: the lack of auditable inference. Existing runtimes offer output but obscure the e...
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The initial public indexing or launch date for Coelanox – auditable inference runtime in Rust (BERT runs today) within our tracked developer communities was recorded on April 19, 2026.
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Based on metadata extraction, Coelanox – auditable inference runtime in Rust (BERT runs today) is categorized under topics such as: auditable inference runtime, Rust, BERT, PyTorch.
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The original author or development team describes the product as follows: "PyTorch and ONNX Runtime tell you what came out. They can't tell you what actually ran to get there — which ops executed, in what order, on what inputs.A model gets packaged into a sealed .cnox con..."
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