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6 daysdocs: sweep for the worker-only reality (drop mcp supervisor/spawn.sh/--db)blasty1-45/+21
* RPC.md: the TUI is launched via `./ida-tui <bin> --rpc <sock>` (was `idatui.tui --db`); point the "regression lock" note at rpcclient/drive + the pilot instead of the deleted rpc_smoke.py. * PAGING_FINDINGS.md: reframe the intro as the ida-pro-mcp tool functions the worker now calls in-process (shapes/caps unchanged); replace the supervisor-era "idle self-exit" + "max_workers cap" sections with the single-owned-worker lifecycle (no cap, no idle-exit, crash -> reconnect). * TEXTUAL_NOTES.md / TUI_DRIVING_BLUEPRINT.md: drop the ida-pro-mcp framing and the deleted rpc_smoke.py references (-> test_scenarios.py / rpcclient). Also updated the idatui + idatui-rpc skills (in ~/.pi, outside the repo) to the worker model: no supervisor/spawn.sh/--db, worker python + $IDATUI_WORKER_PYTHON, worker.py/worker_client.py/errors.py architecture, and the load-starvation gotcha.
2026-07-09stop piling up idalib workers; raise max-workers headroomblasty1-3/+19
Root cause of 'Maximum idalib worker count reached': the app bumped idle_ttl to ~never on every open, so sessions never freed and hit the default cap of 4. - app: drop the immortal bump_idle_ttl(1e9); rely on the KeepAlive heartbeat (120s) to keep the running session warm, and open with a moderate idle_ttl (1800s) so the worker self-exits and frees its slot after the TUI closes. - spawn.sh: set IDA_MCP_MAX_WORKERS (default 8) for headroom. - doc: supervisor prunes unreachable workers before counting, so killing an idle worker frees a slot on the next open. (Verified separately: big/truncated decompile results propagate renames fine via the earlier force_recompile fix.)
2026-07-09keep idle workers alive: bump_idle_ttl + KeepAlive heartbeatblasty1-8/+29
Root cause: idalib workers run an idle watchdog (worker_lifecycle.py) that self-exits after idle_ttl_sec (default 600s) of no requests -- deliberate resource hygiene for a shared/ephemeral supervisor, wrong for an interactive TUI. CLI-opened startup binary always gets 600s (no flag). Fix (both verified against a 12s-TTL session): - IDAClient.bump_idle_ttl(1e9): re-open (idempotent) re-applies TTL, no upper cap -> effectively immortal. Primary fix, call once at startup. - IDAClient.keepalive()/KeepAlive: background server_health heartbeat resets the watchdog; covers adopted sessions too. Safety net. tests/test_keepalive.py: 4/4 (heartbeat + bump both keep a short-TTL worker alive past 2x TTL). docs updated with why + fix.
2026-07-09domain/paging layer: FunctionIndex, block-cached DisasmModel, decompile, resolveblasty1-0/+10
- Program: model registry + small prefetch pool; cache invalidation hooks - FunctionIndex: lazy pagination (advance by len, clamp page=500), filter globs, viewport windows; full 10,092-func enumerate matches survey in ~3.1s - DisasmModel: block-cached windowed disasm (256/block), forward+back prefetch, cached instruction totals; deep window revisit 196ms -> 0ms (cache proven) - Decompilation: truncation-aware, hard-failure surfaced as data (monster funcs) - resolve(): int/hex/symbol via lookup_funcs (string-array query shape) - tests/test_domain.py: 17/17 on both ls (~290 funcs) and libcrypto (10k+52k-insn) - smoke_client no longer assumes 'main' exists (works on libraries) - doc: idle_ttl worker self-exit finding ("not reachable" needs re-open)
2026-07-09stress paging on 10k-func binary; document scale constraintsblasty1-0/+101
Measured against libcrypto.so.3 (10,092 funcs, biggest 52,120 insns): - list_* count cap ~700, disasm max_instructions cap ~500, then SILENT collapse to 10 (not clamped) -> must clamp client-side (use 500). - pagination must advance by len(data); next_offset=offset+count skips data. - disasm offset paging is O(offset): 6ms@0 -> 180ms@50k, no resumable cursor -> domain layer must cache windows + prefetch + over-fetch. - include_total scans whole func (~217ms on monster) -> fetch once, cache. - decompile hard-fails on huge funcs as a soft error (code=null) -> handle. - idb_open needs a writable path for the .i64. tests/stress_paging.py: durable paging benchmark harness docs/PAGING_FINDINGS.md: constraints that drive the paging layer