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Karpathy Autoresearch: A Prompt Replaces the Paper

Karpathy Autoresearch: A Prompt Replaces the Paper

Andrej Karpathy released Karpathy Autoresearch — a framework where AI agents autonomously run LLM training experiments. 110+ runs in 12 hours on 8×H100.

Claude Sonnet 4.6: Near-Opus Power at One-Fifth the Cost

Claude Sonnet 4.6: Near-Opus Power at One-Fifth the Cost

Claude Sonnet 4.6: Near-Opus Power at One-Fifth the Cost

Kimi K2.5: How a $0.60/M Token Open-Source Model is Forcing Big AI to Rethink Pricing

Kimi K2.5: How a $0.60/M Token Open-Source Model is Forcing Big AI to Rethink Pricing

Moonshot AI released Kimi K2.5, a trillion-parameter open-source model at $0.60/M tokens that matches frontier models. Here is how smart model routing can cut your AI costs by 82% while improving performance.

From Mode Collapse to Context Engineering: How We Build Reliable AI Systems (2026)

From Mode Collapse to Context Engineering: How We Build Reliable AI Systems (2026)

Two fundamental challenges define LLM development in 2026: Mode Collapse reduces output diversity through alignment training, while Context Rot degrades model performance as context windows grow. This article analyzes both phenomena and presents practical solutions like Verbalized Sampling and systematic Context Engineering.

Context Engineering: How to Build Reliable LLM Systems by Designing the Context

Context Engineering: How to Build Reliable LLM Systems by Designing the Context

Context engineering is the discipline of curating, structuring, and defending everything that reaches the LLM at inference time. This comprehensive guide covers 2026 best practices for building reliable AI systems.

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