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Qwen3.8-Max Open Weights: The Checkpoint You Download Is Not the Model You Tested
Qwen3.8-Max open weights ship text-only under a revenue-share license as a 2.4T MoE. Why the downloadable checkpoint is not the hosted API model, plus the VRAM math.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
Agent Plugin Security: What a Plugin Can Reach in an npm Agent Runtime
Agent plugin security is a permission-model problem, not a scanner problem. Here is the install-decision rule for npm agent runtimes like DeepSeek Harness.
The Harness Was the Closed Part: What an Open Agent Runtime Actually Buys You
DeepSeek Harness open-sourced the agent loop under MIT. What an open agent runtime actually makes inspectable, where it breaks, and when it beats an API.
ArticleVerified Code Generation: When the Model Has to Prove It
Research PaperQuantization of Ethics: Mathematical Constraints for AI Fairness
ArticleDiffusion Models Beyond Images: Audio, Video, and 3D in 2026
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Post-Training Modern LLMs
Pretraining produces a model that predicts text. Post-training is what turns it into something you can ship. This path walks the levers in the order you would actually reach for them: supervised fine-tuning and adapters, preference optimization without a reward model, the reinforcement-learning map from RLHF to verifiable rewards, RL against a verifier that cannot be talked out of its answer, and finally the inference-time compute that picks up where training leaves off. Every step names the ceiling it runs into.
Evaluating LLMs Honestly
A leaderboard number is a hypothesis, not a result. This path builds the habit of asking what a benchmark measured before quoting what it reported, starting with contamination and judge bias, moving through a case where the advertised figure and the measured one diverge, then to agents where a single passing run tells you almost nothing, and ending where the eval harness itself turns into attack surface.