To build personal superintelligence, our model’s capabilities should scale predictably and efficiently. Below, we share how we study and track Muse Spark’s scaling properties along three axes: pretraining, reinforcement learning, and test-time reasoning. 🧵👇 L - x.com

2026-04-08 · Source: https://x.com/aiatmeta via Google News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

The provided content indicates that JavaScript is currently disabled in the user's browser, preventing the proper display and functionality of the x.com website. The platform suggests checking the Help Center for supported browsers and offers a "Try again" option. Additionally, it warns that certain privacy-related browser extensions might interfere with x.com's operation, recommending their temporary deactivation to resolve potential issues. The message implies a technical problem on the client side, rather than a server-side error, requiring user intervention to restore full site access.

Key takeaway

For users encountering issues accessing x.com, your immediate action should be to confirm JavaScript is enabled in your browser settings. If the problem persists, consider temporarily disabling any privacy-related browser extensions, as these are explicitly identified as potential causes. This troubleshooting approach will help determine if client-side configurations are preventing the site from loading correctly.

Key insights

Website functionality relies on JavaScript; browser settings or extensions can disrupt this critical dependency.

Principles

• Client-side scripts are essential for modern web apps.
• Browser extensions can impact site functionality.

In practice

• Verify JavaScript is enabled in your browser.
• Temporarily disable privacy extensions for troubleshooting.

Topics

• Personal Superintelligence
• Muse Spark
• Model Scaling Properties
• Pretraining
• Reinforcement Learning

Best for: AI Scientist, Machine Learning Engineer, AI Architect

Related on AIssential

• With Muse Spark, we are on a predictable and efficient scaling trajectory. We look forward to sharing increasingly capable models on the path to personal superintelligence soon. - x.com — x.com requires JavaScript, and browser settings like Firefox's Strict Mode can disrupt its functionality.
• Towards Engineering Scaling Laws with Pretraining Data Composition — Pretraining data composition can engineer neural scaling laws in particle physics, favoring data-rich regimes over larger models.
• On the slow death of Scaling (birth of Adaption Labs) | Sara Hooker | HF ML Club India EP2 — Monolithic AI scaling is ending; the future is adaptive, efficient, post-training intelligence.
• MIT study explains why scaling language models works so reliably — Superposition, where concepts overlap in limited dimensional space, mechanistically explains language model scaling laws.
• From bigger models to better intelligence: what NeurIPS 2025 tells us about progress — AI progress is shifting from raw scale to efficiency, real-world evaluation, and interactive world modeling.
• Meta is back in the LLM game after a year-long break — Meta's Muse Spark release signals a renewed, resource-intensive push into top-tier LLMs, despite past failures and ongoing post-training challenges.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by https://x.com/aiatmeta via Google News.