# $DOS Social Sentiment & Intelligence — 2026-08-13 09:30 UTC

> **Asset:** $DOS  
> **Momentum Status:** Trending Up  
> **Timestamp:** 2026-08-13 09:30 UTC (2026-08-13T09:30:00Z)  
> **Canonical URL:** https://cryptitalk.com/2026-08-13-09-30/crypto/DOS  
> **Overview Brief:** https://cryptitalk.com/2026-08-13-09-30/crypto.md  

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## 10-Minute Social Metrics
- **Posts Analyzed:** 1
- **Total Impressions:** 351
- **Likes:** 7
- **Retweets:** 1
- **Comments:** 4

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## Momentum & Sentiment Analysis
Total Engagement - Comments: 4, Retweets: 1, Likes: 7, Impressions: 351


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## Cited Community Posts & Evidence


### Post #1 by @real_j3nny
> **Author:** [@real_j3nny](https://x.com/real_j3nny)  
> **Metrics:** 67 likes · 1 retweets · 76 comments · 206 views  


> **Source Link:** [https://x.com/real_j3nny/status/2087486946343833612](https://x.com/real_j3nny/status/2087486946343833612)  


> **Visual Context:** A scatter plot titled "Score vs model size — smaller, specialised, better" displays benchmark scores (0-100) against model size on a logarithmic scale, showing Velvet Flash 0.1 (4B parameters) leading at 50, followed by Qwen3.5 at 45, Llama at 32, Gemma at 31, and Mistral at 20, illustrating how a smaller task-specific model outperforms much larger general-purpose models on crypto skills evaluation.  

> 
> "A 4B model trained specifically for crypto operations scored 50 on 
@Velvet_Capital
 crypto skills benchmark, ahead of Qwen3.5-27B at 45, DeepSeek-V4 at 40, and Llama-3.3-70B at 32.

What I find even more interesting is the jump from 23 in the base model to 50 after task-specific"




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## Contributing Accounts

- `@erjie22` (https://x.com/erjie22)