How We Built LeetLLM
Inside LeetLLM's Git-backed curriculum: reviewable article bundles, generated figures, user-owned state, and the checks between a source edit and a deployed page.

Blog
Practical notes on LLM systems, evaluation, agents, inference, and developer workflows.
Inside LeetLLM's Git-backed curriculum: reviewable article bundles, generated figures, user-owned state, and the checks between a source edit and a deployed page.


Compare AI coding subscription prices, annual commitments, seat minimums, usage windows, and overflow costs, then calculate a budget from reviewed work.

Build a pinned llama.cpp server, verify a Qwen3.6 GGUF download, and test a local text endpoint. Understand memory limits before adding context, MTP, or vision.

Five focused AI engineering projects, with concrete experiments, inspectable artifacts, and failure cases that make the work worth discussing.

Start with programming, grow one useful project, and choose deeper study from the work you want to do. A practical path without a hiring countdown.

What DeepSeek V4's release notes, parameter counts, benchmark tables, and live prices actually tell you about deploying Flash 0731 or Pro 0813.

Choose an OpenClaw plan by permitted workload, credentials, quotas, and paid fallback. Compare dated prices and calculate request costs without confusing them with task costs.

Run Gemma 4 locally with Ollama: pick an exact tag, leave memory for context, verify placement with ollama ps, and benchmark the native API.

Compare vLLM, SGLang, TensorRT-LLM, Ollama, and llama.cpp by deployment surface, checkpoint recipes, latency, throughput, and operating cost.

Fifty LLM engineering questions that connect model mechanics to serving, retrieval, agents, evaluation, and safety through evidence and failure diagnosis.

Compare Cursor, Codex, Copilot, and Claude Code through one authorization change. Choose by operating mode, review surface, cost meter, and team controls.

Compare AI engineering pay without mixing BLS wages, posted salary, and total compensation. Match role family, level, location, equity terms, and source date first.

Separate model rights from deployment control, evaluate the complete data path, and compare cost per accepted answer rather than API tokens against GPU rent.

Million-token windows can hold a bounded corpus, but capacity doesn't guarantee reliable retrieval. Measure effective context, watch length-based prices, and choose between full context and RAG.

Build a Python agent that follows configuration references, then test its tool permissions, history, recovery, and stop conditions before connecting a live model.

A policy assistant gives the wrong answer. Should you rewrite its prompt, retrieve better evidence, or train it? A controlled evidence swap reveals which experiment to run next.

Follow a support assistant from a wrong answer to a tested fix. See how applied AI, ML platforms, and research engineering divide the work.

Read SWE-bench as a repository-repair experiment: trace a patch through grading, compare variants and attempt rules, and check the limits of public scores. Updated for Multimodal v2.

Prepare for the interview you're actually taking: confirm the format, attempt representative problems, and use concrete coding, ML, and LLM debugging exercises to find gaps.