We fingerprinted 178 AI models' writing styles and similarity clusters
Details
- External ID
- 47690415
- Source
- HN
- Company
- —
- Product
- We fingerprinted 178 AI models' writing styles and similarity clusters
- Website domain
- rival.tips
- Launched
- April 8, 2026
- Cohort
- —
- Upvotes
- 78
- Upvotes percentile
- 0.877892030848329
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
We have a dataset of 3,095 standardized AI responses across 43 prompts. From each response, we extract a 32-dimension stylometric fingerprint (lexical richness, sentence structure, punctuation habits, formatting patterns, discourse markers).Some findings:- 9 clone clusters (>90% cosine similarity on z-normalized feature vectors) - Mistral Large 2 and Large 3 2512 score 84.8% on a composite metric combining 5 independent signals - Gemini 2.5 Flash Lite writes 78% like Claude 3 Opus. Costs 185x less - Meta has the strongest provider "house style" (37.5x distinctiveness ratio) - "Satirical fake news" is the prompt that causes the most writing convergence across all models - "Count letters" causes the most divergenceThe composite clone score combines: prompt-controlled head-to-head similarity, per-feature Pearson correlation across challenges, response length correlation, cross-prompt consistency, and aggregate cosine similarity.Tech: stylometric extraction in Node.js, z-score normalization, cosine similarity for aggregate, Pearson correlation for per-feature tracking. Analysis script is ~1400 lines.
Enrichment
- Theme
- AI text humanizers and detectors
- Vertical
- Security
- Function
- Observability & eval
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Hobby / open-source project
- Normalized one-liner
- ai model writing style fingerprinting analysis
- Manually corrected
- False
Could you build this?
Partial Extracting stylometric features from text via standard NLP libraries is straightforward, but assembling, cleaning, and normalizing a benchmark dataset across 178 models requires substantial automated evaluation pipelines and API compute.
What it would actually take: The implementation requires automated prompt evaluation harnesses querying dozens of model endpoints via standard APIs (OpenAI, Anthropic, open-source model providers via vLLM), followed by a feature extraction pipeline computing 32 linguistic and statistical metrics (lexical diversity via TTR/Yule's K, POS distribution via spaCy, sentence entropy, and formatting tokens). Dimensionality reduction and clustering (UMAP, HDBSCAN, or hierarchical clustering) must then be applied and validated across thousands of samples.
Discussion
20 comments analyzed.
Competitors mentioned: models.dev, arena.ai, HuggingFace
Concerns raised: Methodology lacks linguistic theory and uses arbitrary metrics/thresholds, Claims (75% similarity = 'writes the same') unsubstantiated without prompts/responses shown, Accessibility issues: muted colors on dark background, poor contrast ratios, Suspicious claim that Opus and Gemini Flash share 99% style similarity, No explanation of how 32 dimensions were selected or if prompts were optimized
Competitors
Other products that read as similar to this one — 227 launches clear the similarity bar, closest 8 shown.
Attention rank: #26 of 228 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 159 days after the earliest competitor.
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- slopXiv · ph · 2026-09-17 · 1 upvotes · similarity 0.45
- 17MB model beats human experts at pronunciation scoring · hn · 2026-02-20 · 13 upvotes · similarity 0.43
- econ-ai-detector · github · 2026-09-09 · 8 upvotes · similarity 0.43
- Academic-Writing-Humanizer · github · 2026-09-26 · 7 upvotes · similarity 0.42
- learning_ai_pentesting · github · 2026-09-26 · 24 upvotes · similarity 0.42
- Hupmapper · ph · 2026-09-15 · 4 upvotes · similarity 0.41
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a observability & eval tool for Media & entertainment yet.