Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Build a CSI→pose model without overstating it. The project has a retracted 92.9%/100% history — the discipline below exists so it never recurs.
A pose model that always predicts the dataset's mean pose already scores ~50% PCK. Quote PCK only as a delta over that baseline, on a held-out split with no subject or temporal leakage. Example honest result (ADR-181):
Held-out PCK@20 59.5% vs a 50% mean-pose baseline = +9.4 pp real signal — MEASURED.
npx @ruvnet/ruview train-plan --mode pose-smoke # command, cwd, outputs; runs nothing
npx @ruvnet/ruview train --mode pose-smoke --confirm # SYNTHETIC pipeline smoke (libtorch 2.11 for tch 0.24)
npx @ruvnet/ruview train --mode pose --data-dir <in-repo MM-Fi dir> --confirm
npx @ruvnet/ruview train-gate --file eval-report.json # mean-pose baseline + leakage gate
The gate returns the only acceptable claim sentence. Quote nothing it fails.
(model − baseline) in pp, with the split definition (chronological /
blocked-gap / grouped-bucket; no leakage).ruview_claim_check the writeup — it flags any untagged or 100%/perfect claim.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。
Web browser automation with AI-optimized snapshots for claude-flow agents
日本語の概要は準備中です。原文の説明を表示しています。