# $GLM Catalyst Posts & Social Intelligence Recap — July 13, 2026

> **Asset:** $GLM  
> **Date:** 2026-07-13 (July 13, 2026)  
> **Canonical URL:** https://cryptitalk.com/tokens/glm/2026-07-13  
> **Token Profile:** https://cryptitalk.com/tokens/glm.md  
> **Market Rankings:** https://cryptitalk.com/rankings.md  

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## Curated Viral Posts & Community Evidence


### Post #1 by @ysu_nlp
> **Author:** Yu Su ([@ysu_nlp](https://x.com/ysu_nlp))  
> **Metrics:** 151 likes · 20 retweets · 9 comments · 25.0K views  

> **Posted:** 2026-07-13T01:37:59+00:00  


> **Source Link:** [https://x.com/ysu_nlp/status/2076481232117067894](https://x.com/ysu_nlp/status/2076481232117067894)  


> 
> "There's a major gap in this otherwise compelling vision. 

Lots of interesting signals recently: token budget caps, intelligence sovereignty, increasing competition (Meta/Grok/GLM) that drives the commoditization of intelligence.  

All seem to point toward a more benevolent"


### Post #2 by @TechMDAI
> **Author:** TechMD ([@TechMDAI](https://x.com/TechMDAI))  
> **Metrics:** 35 likes · 1 retweets · 4 comments · 2.7K views  

> **Posted:** 2026-07-13T14:42:27+00:00  


> **Source Link:** [https://x.com/TechMDAI/status/2076678649252622392](https://x.com/TechMDAI/status/2076678649252622392)  


> **Video Content:** The video shows a screen recording of a terminal window displaying an SSH session labeled "python3--vLLM-Moot" where a user has pasted a prompt asking a 753B-parameter GLM-5.2 model (running on 2 NVIDIA DGX Sparks) to explain why Mixture-of-Experts models can be compressed to 2 bits per expert. Across the frames, the response streams in progressively, generating three numbered paragraphs covering fundamental (sparse activation), statistical (low-entropy weight distributions), and practical/implementation (2-bit quantization preservation) reasons for the compression. The post caption "This guy is pushing the limit with 2 sparks. GLM at home on 2 sparks" references the dual DGX Spark hardware setup visible in the terminal header.  

> 
> "This guy is pushing the limit with 2 sparks. GLM at home on 2 sparks"


### Post #3 by @0xCRASHOUT
> **Author:** crashout ([@0xCRASHOUT](https://x.com/0xCRASHOUT))  
> **Metrics:** 0 likes · 0 retweets · 0 comments · 248 views  

> **Posted:** 2026-07-13T01:04:27+00:00  


> **Source Link:** [https://x.com/0xCRASHOUT/status/2076472791545852317](https://x.com/0xCRASHOUT/status/2076472791545852317)  


> 
> "wat u gon use? glm 5.2??"


### Post #4 by @spettrotoken
> **Author:** Spettro ([@spettrotoken](https://x.com/spettrotoken))  
> **Metrics:** 13 likes · 2 retweets · 0 comments · 243 views  

> **Posted:** 2026-07-13T21:15:28+00:00  


> **Source Link:** [https://x.com/spettrotoken/status/2076777553218384156](https://x.com/spettrotoken/status/2076777553218384156)  


> 
> "GLM 5.2 is the current most underrated model ever

- Cheaper than many frontier models
- Opensource
- Can work on long tasks without loosing context

And, guess what? 

It's in the Spettro subscription since day 1"


### Post #5 by @jackzampolin
> **Author:** Jack Zampolin ([@jackzampolin](https://x.com/jackzampolin))  
> **Metrics:** 2 likes · 0 retweets · 1 comments · 157 views  

> **Posted:** 2026-07-13T17:57:30+00:00  


> **Source Link:** [https://x.com/jackzampolin/status/2076727735364526589](https://x.com/jackzampolin/status/2076727735364526589)  


> 
> "Been testing a new GLM 5.2 quant quite a bit over the last few days and gave it a writeup on the blog. tl;dr is that its def the best way to use 4x 6000s right now"


### Post #6 by @0xrwu
> **Author:** Richard Wu ([@0xrwu](https://x.com/0xrwu))  
> **Metrics:** 1 likes · 0 retweets · 1 comments · 146 views  

> **Posted:** 2026-07-13T04:12:01+00:00  


> **Source Link:** [https://x.com/0xrwu/status/2076519995983479154](https://x.com/0xrwu/status/2076519995983479154)  


> 
> "I’m excited for GLM 6/Deepseek V5/Kimi 3 distilled on 5.6 Sol!"




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*Generated by Context Pilot Social Intelligence Engine.*