Build, flash, and provision an ESP32-S3/C6 CSI node for RuView — firmware variant choice, cross-platform checksum-verified esptool flashing, NVS/WiFi/channel/MAC-filter overrides, boot-log evidence.
インストール方法を見る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。
Bring an ESP32 sensing node online.
Prebuilt binaries: GitHub release v0.8.1-esp32 (hardware-validated on S3 QFN56 rev v0.2).
Use a release flash bundle (or release_bins/<variant>, or an ESP-IDF build/).
The harness flashes on Windows, macOS, and Linux through python -m esptool:
npx @ruvnet/ruview doctor --group python,serial,firmware # esptool, pyserial, ports, bundle checksums
npx @ruvnet/ruview flash-plan --port <PORT> --bundle <dir> --variant s3-8mb
npx @ruvnet/ruview flash --port <PORT> --bundle <dir> --variant s3-8mb --confirm
flash verifies SHA256SUMS, refuses a chip that does not match the variant
(esptool chip_id), writes 0x0 / 0x8000 / 0xf000 / 0x20000 (NVS preserved),
then captures a boot log. hardwareValidated: true requires CSI callbacks in
that log. Over MCP, ruview_node_flash needs the hardware-write grant and
confirm: true.
Building from source still uses ESP-IDF v5.4 (Docker or the Windows subprocess
flow; Git Bash/MSYS is unsupported, so strip MSYSTEM* env vars).
python firmware/esp32-csi-node/provision.py --port <PORT> \
--ssid "<SSID>" --password "<secret>" --target-ip <server-ip> --target-port 5005
# optional ADR-060 overrides:
python firmware/esp32-csi-node/provision.py --port <PORT> --channel 6 --filter-mac AA:BB:CC:DD:EE:FF
Never echo or commit the WiFi password.
ruview_node_monitor {port} — PASS criteria: serial shows CSI cb #... callbacks and
(on a bare board) CSI filter upgraded to MGMT+DATA. No callbacks → the node isn't
capturing; do not proceed to calibration.
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。