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2.9 KiB
2.9 KiB
jcode TUI log schema (for grounding the user model in real usage)
Logs live in ~/.jcode/logs/jcode-YYYY-MM-DD.log. Lines look like:
[2026-06-28 00:15:41.814] [INFO] <message>
<message> is sometimes a structured event:
EVENT event=<TYPE> key=value key=value ...
Structured EVENT types observed (3-day sample, by volume)
| event= | vol | meaning / useful fields |
|---|---|---|
| AGENT_PROVIDER_STREAM_LIFECYCLE | 12303 | model streaming; not a user action |
| SESSION_PERSISTENCE | 11935 | session saved; append_ms, chars |
| TOOL_LIFECYCLE | 9907 | a tool ran. resolved_tool_name=, `phase=start |
| model_routes_summary | 4235 | routing; not a user action |
| SERVER_REQUEST_LIFECYCLE | 2222 | a client request hit the server (proxy for a user message / command) |
| SESSION_LIFECYCLE | 1750 | phase=, client_connection_id=, allow_takeover=, client_has_local_history= |
| SWARM_LIFECYCLE | 963 | swarm member status; phase=member_status_updated, new_status= |
User-action verbs (grep counts, 3-day sample)
These are the closest proxies to "what the user does", and the actions the iOS app must also support, so they should weight the mobile user graph:
| verb | count | iOS equivalent action |
|---|---|---|
| compact | 8399 | context compaction notice (passive) |
| diff_mode | 1951 | (TUI-only; n/a on mobile) |
| interrupt | 1845 | cancel / stop button |
| soft_interrupt | 1164 | queue-a-message-mid-run |
| cancel | 262 | cancel |
| scroll_up/down/page | ~ | transcript scrolling |
| resume | 251 (today) | resume_session / switch session |
| side_panel | 39 | (n/a on mobile) |
How to mine it (for log_mining.py)
- Read the last N daily logs (default 7) under
~/.jcode/logs/. - Count: user messages (
SERVER_REQUEST_LIFECYCLEstart, orAssistant:turns as a proxy for turns), interrupts, soft_interrupts, cancels, resumes/session switches, model switches, scrolls, tool runs (TOOL_LIFECYCLE phase=start). - Emit a normalized frequency profile dict, e.g.:
{"send_message": 0.55, "scroll": 0.20, "soft_interrupt": 0.08, "interrupt": 0.06, "switch_session": 0.05, "change_model": 0.02, ...}These become the relative edgeweights in the mobile ActionGraph. - Be robust: logs are huge (100k+ lines/day) and noisy; stream line-by-line, tolerate missing files, and DEGRADE GRACEFULLY to literature-default weights if no logs are found (so the engine still runs in CI / on a fresh machine).
Caveats (be honest in evidence)
- TUI usage is a proxy for mobile usage, not identical (no diff_mode/side_panel on mobile; mobile likely has relatively MORE scroll + read, fewer power-tools). log_mining.py should expose the raw TUI counts AND the mobile-mapped weights so the mapping assumptions are auditable.
- These logs are this user's personal data; keep mining read-only and local.