
META'S LLAMA IS EATING THE INTERNET
Meta's Llama went from 1.4T to 15T training tokens in 18 months. The internet is finite. The hunger isn't. You're the buffet.

Meta's Llama went from 1.4T to 15T training tokens in 18 months. The internet is finite. The hunger isn't. You're the buffet.

Alibaba's Qwen just crossed one billion downloads, body-slamming Meta's Llama off the open-source AI throne. The crown has officially moved east.
A $6M Chinese AI model just matched GPT-4o and Claude 3.5 Sonnet. NVIDIA lost $593B in a day. The AI moat was always a myth.

Meta pivoted from metaverse leglessness to AGI god-building in 18 months. SemiAnalysis says they've got the GPUs. But do they have a prayer? A hype-reality check on Zuck's most expensive fever dream yet.

Meta CTO Andrew Bosworth lays out Meta's AI strategy to Alex Kantrowitz. Translation: we missed the first wave, but we're spending billions to catch up. Real analysis of the Llama play, Ray-Ban glasses, and open-source gambit.

llama.cpp lets you run GPT-4-class models on hardware you own. No subscriptions, no data harvesting, no Sam Altman. Here's why the local LLM movement is the real AI revolution.

DeepSeek R1 cost $5.6M to train and matched OpenAI's o1. NVIDIA lost $600B in a day. The open-source AI revolution is here and Wall Street's late.

Meta admits its free-model strategy is dead. Llama was supposed to democratize AI—instead it became raw material for everyone else's products while Meta burned $80B. The open weights era is ending.

DeepSeek trained a GPT-4-class model for $5.6M on nerfed chips and open-sourced the whole thing. The AI hype machine just met its reality check.

Meta's first-ever LlamaCon on April 29 isn't just a dev conference—it's a power play in the AI platform wars. Expect Llama 4 teases, ecosystem plays, and open-source drama.