Artifacts
Research artifacts published on hbar.science, labeled by epistemic zone, rigor tier, and AI involvement.
Artifacts are pointers and structured metadata — not hosted files. Submit an artifact →
What counts as an artifact?
An artifact is any inspectable object produced in the course of scientific inquiry that enables understanding, critique, or replication.
Artifacts are inputs to knowledge, not just outputs. They make reasoning, evidence, and uncertainty visible rather than compressed into opaque claims.
Hosting scope. hbar.science does not host large, executable, or continuously evolving research assets. Instead, it curates metadata, structure, and durable links to externally hosted artifacts (e.g., code repositories, datasets, notebooks), preserving inspectability while avoiding duplication or version drift.
Artifact types
All artifacts on hbar.science are classified under one of the following canonical types:
- Algorithm — a formally specified procedure, update rule, or method; the intellectual artifact independent of any particular implementation
- Code — executable or inspectable implementations, scripts, or pipelines
- Dataset — raw or generated data used for analysis or evaluation
- Analysis — derived results such as simulations, benchmarks, parameter sweeps, or evaluation outputs
- Protocol — formally specified procedures defining how experiments, simulations, or evaluations are conducted
- Model / Framework — conceptual or mathematical structures with explicit assumptions and scope
- Tool / Application — an interactive interface (web app, CLI, dashboard) that enables use, exploration, or presentation of a scientific object; typically has a deployed URL and source link
- Collection / Bundle — a structured index pointing to multiple related artifacts (e.g., thesis + app + pipelines), used to present a coherent project while keeping sub-artifacts independently inspectable
These categories are intentionally minimal and stable.
Specific implementations (e.g., notebooks, benchmarks, proof sketches) are treated as instances of these types, not as separate categories.
Note: "Paper" and "Essay" are formats, not canonical artifact types. A paper is typically a model or analysis artifact with an associated publication link. An essay is a synthesis artifact in the Synthesis zone.
See also: Method
The Future Evolution of hbar.science
Plausible directions for platform evolution: living artifacts, AI transparency, replication workflows, and lightweight stewardship.
GPU Concurrency Benchmark — vLLM Serving Box (RTX PRO 6000 Blackwell)
Measured the concurrent-request ceiling of a single RTX PRO 6000 Blackwell Server Edition GPU serving Llama 3.3 70B Q4_K_M GGUF under vLLM 0.21.0, to replace the ~25-user-per-box estimate in the sovereign-compute architecture with a measured number. Result: as-deployed ceiling is ~5 in-flight requests, falsifying the architecture-doc hypothesis 5×; the gap is attributable to software configuration (GGUF format, fp16 KV cache, FlashAttention v2, native PyTorch sampler) rather than hardware. Derived per-user GPU cost ~€16/month validates the €29 subscription placeholder within ~€2.
HOPSO: Harmonic Oscillator–Based Particle Swarm Optimization (Reference Implementation)
Reference Python implementation of the Harmonic Oscillator–Based Particle Swarm Optimization (HOPSO) algorithm, corresponding to the published PLOS ONE article.
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Compliance: 4/5 (80%)
Missing: Environment hash documented
HOPSO — VQE Optimizer Implementation
Reference implementation of the HOPSO classical optimizer adapted for use in variational quantum eigensolver (VQE) workflows.
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Compliance: 1/2 (50%)
Missing: Deterministic seed documented
Institutionalized Science: Incentives, Access, and the Limits of the Journal Model
An analysis of structural constraints in contemporary scientific publishing and institutional research.
VQE Regularization Evaluation Pipeline
Computational pipeline for evaluating regularization effects in variational quantum eigensolvers, including parameter sweeps and optimization landscape analysis.
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Compliance: 1/3 (33%)
Missing: Deterministic seed documented
Missing: AI audit missing/incomplete