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What 80.6% on SWE-bench Verified Doesn't Tell You
DeepSeek V4-Pro leads with 80.6% on SWE-bench Verified, the coding benchmark OpenAI stopped reporting. Why an agentic score belongs to a bundle, not a model.
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Curated technical papers and hands-on implementation guides for the modern AI engineer.
RAG Interview Questions: The Retrieval Ladder Behind the Hype
A senior practitioner's guide to RAG interview questions: the escalation from naive retrieval to chunking, reranking, evaluation, and GraphRAG, with the trap at every rung.
LLM Inference Interview Questions: The Serving Ladder Interviewers Actually Climb
A senior engineer's guide to LLM inference interview questions: the escalation from prefill vs decode to the KV cache, continuous batching, quantization, and speculative decoding, with the trap at every rung.
ArticleFine-Tuning 4-Bit Models: Adapting a Base That Only Ships Quantized
ArticleAttention Mechanism Interview Questions: What Interviewers Actually Probe
ArticleYour Skill Scanner Reads the File, Not the Execution
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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.