Hypothesis-driven validation with falsification tests, triangulated demand signals, and transparent scoring. Every claim labeled Fact vs Hypothesis.
Every artifact is tagged as: supports / weakly supports / contradicts / irrelevant for each hypothesis.
"ML teams hit serialization bottlenecks often enough that they file issues and switch tools."
"Those bottlenecks are specifically about tensors/graphs (not generic JSON), so ML-native types matter."
"A drop-in serializer with no schema registry & good DX is adoptable in <2 weeks."
Each card separates facts from claims, includes quantification, decision signals, and verification plans. Not just complaints — behavioral proof.
NumPy → Protobuf serialization is 1000x slower than alternatives, blocking production deployment.
SJSON would provide 100x+ improvement over current Protobuf approach.
No standard way to convert graphs between DGL and PyG frameworks.
SJSON with Node/Edge/GraphShard types solves this completely. Universal format for both frameworks.
Never rely on a single signal. Each segment validated across 3 evidence types: Public Pain, Behavioral Proof, Economic Proof.
Triangulation Status: All 3 evidence types present. Conclusion stable if any one removed.
Where SJSON is NOT the right solution. Logging contradictions makes research credible.
Arrow/Parquet optimized for columnar analytics. SJSON is row-oriented streaming format — wrong paradigm for aggregations.
If you need compile-time type safety and schema enforcement, Protobuf's code generation is a feature, not a bug.
If entire stack uses gRPC + Protobuf, switching to SJSON means rewriting transport layer.
For regular JSON APIs (user data, configs), standard JSON or MessagePack is simpler. ML types are overhead.
| Competitor | Why They Win | Where They Fail | SJSON Wedge |
|---|---|---|---|
| Apache Arrow | Zero-copy IPC, columnar, huge ecosystem | Complex setup, no streaming | ML-native streaming, simpler API |
| Protobuf + gRPC | Industry standard, type safety | No ML types, schema overhead | Schema-free, native tensors |
| MessagePack | Simple, fast, good libraries | No tensor support, just bytes | Same speed + ML semantics |
| Pickle | Python-native, supports everything | Security nightmare, Python-only | Safe, cross-language |
Transparent scoring rubric: Pain Intensity × Frequency × Budget / Friction. No more "cool problems" with low adoptability.
| Rank | Company | Pain | Frequency | Budget | Friction | Score |
|---|---|---|---|---|---|---|
| 1 | BentoML | 5 | 5 | 4 | 2 | 50.0 |
| 2 | TensorFlow Data | 5 | 5 | 5 | 4 | 31.3 |
| 3 | PyTorch Geometric | 5 | 4 | 3 | 2 | 30.0 |
| 4 | DGL (Amazon) | 5 | 4 | 4 | 3 | 26.7 |
| 5 | Confluent Kafka | 4 | 4 | 5 | 4 | 20.0 |
Formula: Priority = (Pain × Frequency × Budget) / Friction
Scoring Guide: 5=Critical/Daily/Enterprise | 4=High/Weekly/Pro | 3=Medium/Monthly/Free | 2=Low/Rare | 1=Minimal
Weekly loop that makes every hour of research increase future research speed.
Key insight: Step 3 (Cardify) is mandatory. That's where rigor happens.
Before publishing, every section must pass these checks. If all pass → rigorous research.
Current Score: 6/8 checks passed — Research is rigorous but needs PoC validation.