Acing AI — AI education, tutorials, research and datasets for data scientists
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.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
Fine-Tuning 4-Bit Models: Adapting a Base That Only Ships Quantized
Fine-tuning 4-bit models is a different problem from QLoRA over a BF16 base: what precision to train the adapter in, what to merge into, and what you deploy.
Attention Mechanism Interview Questions: What Interviewers Actually Probe
A senior researcher's guide to attention mechanism interview questions: the escalation from self-attention to MLA, what each rung tests, and the traps that expose memorizers.
ArticleYour Skill Scanner Reads the File, Not the Execution
ArticleTen Proofs, Zero Sorries: What an AI Proof Certificate Actually Settles
ArticleYour LLM Serving Bottleneck Moved to the CPU
Browse by Type
Tutorials
Step-by-step guides from neural network basics to advanced LLM fine-tuning.
Research Papers
Peer-reviewed insights and white papers defining the frontier of artificial intelligence.
Datasets
High-fidelity training sets for natural language processing and computer vision.
Start Learning
Guided sequences through our best content — structured to build understanding from the ground up.
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.