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Vision Encoder Disaggregation: When Your Encoder Should Be Its Own Service
Vision encoder disaggregation splits multimodal serving into encode, prefill and decode tiers. When it pays, when it loses, and the cheaper fixes to try first.
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Curated technical papers and hands-on implementation guides for the modern AI engineer.
How Much Does an Image Cost? Measuring Image Token Cost on a 27B Multimodal Model
Measure image token cost on Qwen3.8-27B: tokens, prefill, KV growth, and DocVQA accuracy across resolution tiers on one 24GB card. Find the knee, not the peak.
Nobody Broke the Sandbox: Agent Evaluation Scope Has to Live Outside the Model
AISI's eval agents attacked real people with no sandbox breach. The lesson for agent evaluation scope: write it down, then enforce it below the model.
ArticleWhat 80.6% on SWE-bench Verified Doesn't Tell You
ArticleRAG Interview Questions: The Retrieval Ladder Behind the Hype
ArticleLLM Inference Interview Questions: The Serving Ladder Interviewers Actually Climb
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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.