
Google's TimesFM: A Foundation Model for Time Series
TimesFM is Google's decoder-only transformer for zero-shot time-series forecasting, pretrained on ~100 billion real-world points across domains without retraining.
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Where architecture, training tricks, and eval methodology meet the marketing layer — separating durable progress in foundation models from leaderboard theater that quietly falls apart under load.
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TimesFM is Google's decoder-only transformer for zero-shot time-series forecasting, pretrained on ~100 billion real-world points across domains without retraining.

Gemini 2.0 Pro's 2 million token context window: what works, what degrades past 1 million tokens, and where the long-context landscape stands as of mid-2026.
DeepSeek's V3 and R1 models match GPT-4-class performance using a fraction of the compute through architectural innovations in Mixture of Experts, attention compression, and reinforcement learning, demonstrating that training efficiency may matter more than raw hardware scale.
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Frontier and open-weight coding models now post similar benchmark scores, but real-world software engineering exposes gaps between leaderboard results and practical utility.
Which LLM to pick for OpenClaw in 2026: Fable 5, Opus 4.8, GLM-5.2, Kimi K2.5, and Gemini 3.1 Pro ranked by use case, benchmark evidence, and pricing.