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Post by elie on X

elie@eliebakouch

Fable and Mythos 5.1 are the EXACT same weights, they look at the internal activations then escalate to a bigger classifier and ultimately fallback to Opus 4.8 if the request is categorized as dangerous Fable is not distilled version of a larger Mythos model

594 likes27 repliesPosted Sep 1, 2026

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

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Why it worked

Opens with a short, self-contained line that is easy to take in at a glance.
Ends on a short line that is easy to quote and repeat.
The opening line stands alone, so it reads completely before the post is expanded.
Short enough to read in full without expanding, which removes a step before the point lands.

The mechanics

Hook: Plain observation
Structure: Hook → Landing
Style: Conversational paragraphs at a steady pace.
Tone: plain, matter-of-fact
Written for: People building with AI

Analysis by ViralLoop — not written by the original author.

Topics

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For those confused why the US state and Silicon Valley are having a meltdown on twitter today. China released multiple AI models that are 50x more efficient than the best American AI models and made them open source, ruining the AI market

Universal HIGH INCOME via checks issued by the Federal government is the best way to deal with unemployment caused by AI. AI/robotics will produce goods & services far in excess of the increase in the money supply, so there will not be inflation.

I am very excited about AI, but to go off-script for a minute: I built an app with Codex last week. It was very fun. Then I started asking it for ideas for new features and at least a couple of them were better than I was thinking of. I felt a little useless and it was sad.

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More from elie

> Using Codex with GPT‑Astra, the team brought three open-weight models that were not part of Jalapeño’s original production plan to high performance within two months from openai blog, two months is quite a lot here no? i'm a bit confuse by this

new anthropic benchmark for "automated ai research". they sourced problems they had on their infra and training stack, give the model the exact same state of the codebase and see if it can solve it openai also has a similar eval since the gpt 5.2 system card

new deepseek v4 pro is now open weight on hugging face (mit license) "v4" is a bit misleading, previous model was only a preview and this one has way more training behind it, feels more like a v4.5 also very excited for the deepseek harness release t.co

grok 4.6 has a very impressive test time curve on cursorbench, in a league of its own they did more mid/pre-training on the grok 4.5 checkpoint and newer SFT stages with grok 4.5 traces and model base filtering. once again shows how important a good SFT ckpt is

mistral, the inference provider company of Europe. smart (and hard to make!) move imo, their models are largely behind open model so this lets them get big clients on the infra part in without forcing their own models on them t.co

wow so seems like openai now has chain of thought monitoring at scale? wondering what technical challenges they faced here, would be something really cool to share more openly as this will contribute to making every model safer

elie on AI · ViralLoop