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Why it worked

The mechanics

Hook
Style
Tone
Written for

Analysis by ViralLoop — not written by the original author.

Emad on AI · ViralLoop

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Post by Emad on X

imo GDPVal is probably the most important benchmark, it measures the performance of models on real world tasks Big leap in performance here to top it at a great price, congrats to @SpaceXAI team & looking forward to the even bigger releases to come! t.co

446 likes35 repliesPosted Aug 12, 2026

View the original on X Written by Emad. Captured Sep 4, 2026.

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42

Viral score

Based on absolute engagement — the platform doesn’t expose follower counts here, so large accounts score higher.

From likes, comments.

Why it worked

Follows a clear Hook → Context → Landing shape, so the reader always knows where they are.
Ends on a short line that is easy to quote and repeat.
Short enough to read in full without expanding, which removes a step before the point lands.

The mechanics

Hook: Plain observation
Structure: Hook → Context → Landing
Style: Conversational paragraphs at a steady pace.
Tone: energetic, punchy
Written for: People building with AI

Analysis by ViralLoop — not written by the original author.

Topics

AI Hiring Revenue

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More from Emad

Without Hugging Face the launch and scaling of stable diffusion & the models that followed would only have been a fraction of what they were. Great and super engaged team who have constantly flown the flag for open source 🤗

I see a lot of my TL freaking out about this Here is the reality: Frontier models will one shot just about anything at 10,000 tokens per second in a few years Do you really think we can monitor that? Would need speed limits on the model that it would code around anyway

I would like to publicly commend @sama and @OpenAI for this taking it at face value. It is very clear that strange and perhaps dangerous things are happening and our systems are not ready for this. Models below frontier are competent enough to change lives so lets optimse

No human input, channeling the latent space of the new models. GPT 5.6 Sol is probably the first model I've used that doesn't make mistakes on maths. As we move to Astra & beyond I don't know how humans can keep up on math. Any pure research program is a harness loop away.

As shocking as the Kimi K3 release. Massive performance gain was just with post-training Model is 3x smaller than GLM 5.2 (10x smaller than K3) & works on a MacBook / Spark This is Q1 flagship (Opus 4.6/GPT 5.4) level for < $0.28/m tokens (100x cheaper)

If distillation were all that any of the western open source labs would have have raised billions could distill K3 (soon) or GLM 5.2 and have a sota model for say 32gb or 80gb of vram Who thinks they will