Core Viewpoint - Meta Platforms has experienced a 22% decline from its peak, raising questions about whether this presents a buying opportunity or reflects deeper issues in the company's strategy [3]. Group 1: Financial Performance - Meta's stock has dropped from approximately $796 to about $619, following a significant 400% increase since 2022 [3]. - The company holds over $60 billion in cash and continues to see double-digit revenue growth [4]. Group 2: Investment Strategy - The recent sell-off is viewed as a reality check rather than a sign of imminent collapse, with concerns focused on the aggressive capital expenditures in AI [3][6]. - Current valuation at around 26 times forward earnings is considered fair or even cheap for long-term investors [5]. Group 3: Competitive Landscape - There are fears of an "AI overspend" reminiscent of the previous metaverse investments, with competition from Microsoft and OpenAI posing additional challenges [6]. - Tightening EU regulations could further pressure margins before AI revenue becomes significant [6]. Group 4: Market Sentiment - The 22% decline is interpreted as a re-rating of risk rather than a panic situation, suggesting a potential strategic entry point for patient investors [7]. - The current price level is seen as a test of conviction for long-term investors, while traders may view it as an opportunity for volatility [8].

RT Avi Chawla (@_avichawla)A simple technique trains neural nets 4-6x faster!- OpenAI used it in GPT models.- Meta used it in LLaMA models.- Google used it in Gemini models.Here's a breakdown (with code): ...

Model Training Optimization - A simple technique can accelerate neural network training by 4-6x [1] - OpenAI, Meta, and Google have utilized this technique in GPT, LLaMA, and Gemini models respectively [1] Key Players - OpenAI employed the technique in GPT models [1] - Meta implemented the technique in LLaMA models [1] - Google incorporated the technique in Gemini models [1]

A simple technique trains neural nets 4-6x faster!- OpenAI used it in GPT models.- Meta used it in LLaMA models.- Google used it in Gemini models.Here's a breakdown (with code): ...