Emerging Mental Models
These frequencies describe the retained, query-conditioned X sample; they are not ecosystem market share. A post may count toward several models.
| Mental model | Posts in retained sample | Reading |
|---|---|---|
| Typed classifier | 32 (51%) | Jev replaces prose with labels, scores, and probabilities. |
| Agent router | 7 (11%) | Jev chooses a model, tool, specialist, or next action. |
| Verification gate | 0 (0%) | Jev decides whether another system's output may proceed. |
| Probabilistic rules engine | 6 (10%) | Fuzzy predicates feed deterministic workflows. |
| LLM complement | 8 (13%) | A fast control plane surrounds generative work. |
| LLM replacement | 6 (10%) | For bounded decisions, a text-generating model may be unnecessary. |
Disagreements
- New model category vs specialized classifier: TypeSafe frames System One Models as a new category; skeptics may reasonably ask for comparisons with compact classifiers and rules.
- No hallucinations vs schema-valid errors: constrained output removes invented prose and parsing failures, but not incorrect choices.
- Replacement vs complement: the evidence supports replacement for bounded decision calls and complementarity wherever text, code, or reasoning traces must be generated.
Raw category counts are available in data/processed/analysis.json (21 observed categories).