MiniMax M3.1 Flash: 428B total parameters, ~23B active (MoE), 1M context window, $0.15 input / $0.60 output per 1M tokens, cache reads at just $0.015. In MiniMax's own comparison table it scores 62.2 on Automation-Bench v1.0.6, ahead of DeepSeek V4.1 Flash 1017 (60.5) and Claude Opus 5 (50.3), but trails on DeepSWE and Terminal Bench 4.0. VentureBeat, citing Artificial Analysis's AA-Omniscience benchmark, reports a notably higher hallucination rate; weights are due 2026-10-23 and the license is still undisclosed.
Week 0 is about search: DFS and BFS differ only in whether the frontier is a stack or a queue; A* adds the steps already taken to a heuristic estimate and guarantees the shortest path when the heuristic never overestimates; Minimax has the two players take the maximum and the minimum in turn, and alpha-beta pruning cuts branches that cannot change the result. With time links for every section, plus the approach, common mistakes and ways to check your work for the Degrees and Tic-Tac-Toe projects; the reference implementations are folded at the end of each project.
Lecture 3 turns a single path into a contingent plan: minimax faces an optimal opponent, alpha-beta skips branches without changing the root value, and expectimax replaces worst-case choice with probability.
Lectures 5–8 use CSPs to practice variables, constraints, and search order before Project 2 implements minimax, alpha-beta, and expectimax. Their key difference is the assumption made about other agents.
2026 Q1 saw a full-blown open-source model explosion: on the LLM front, GLM-5, Kimi K2.5, and Qwen3.5 caught up with closed-source models; Embedding and Reranker are dominated by Qwen3 and BGE; speech has Voxtral TTS and Whisper V3; image has FLUX.2; and video has Wan 2.2 rivaling Sora. This is the complete navigation map.