e14cfb13da295ccaa2cd21af4a9e7c9c3c4553a3
4
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 ed
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1a739ecef5 |
conductor(spec+plan): phase2_4_5_call_site_completion_20260621 + code_path_audit pre-flight adjustments + Phase 3 analysis
PHASE 2/4/5 FOLLOW-UP TRACK (Tier 1 decided SHINK to 6a + 6b + 6d): - Phase 6a: Fix HookServer.broadcast() callers (app_controller.py + events.py + gui_2.py) Adds tests/test_websocket_broadcast_regression.py with no-TypeError assertion - Phase 6b: Complete _send_grok/_send_minimax/_send_llama OpenAICompatibleRequest migration - Phase 6d: Update those 3 senders' NormalizedResponse to use UsageStats Total: ~16 atomic commits, ~3 hours Tier 2 work. Unblocks code_path_audit_20260607. CODE_PATH_AUDIT_20260607 PRE-FLIGHT ADJUSTMENTS (per handoffs): - Add 2 new actions: provider_history_append + websocket_broadcast - Add 5 micro-benchmarks: NormalizedResponse.__init__, WebSocketMessage.__init__, UsageStats.__init__, ProviderHistory.lock, ToolSpec.__init__ - Add no-TypeError-errors-on-any-thread assertion (backs test_websocket_broadcast_regression.py) - Add 89 fat-struct sites from ANY_TYPE_AUDIT_20260621.md as instrumented targets - BLOCKER: phase2_4_5_call_site_completion_20260621 (broadcast() TypeError) PHASE 3 HYPOTHETICAL ANALYSIS (separate doc): docs/reports/PHASE3_HYPOTHETICAL_PROMOTION.md - dataclass definitions (already on tier2 branch), per-provider codepath catalog (112 sites), qualitative cost estimation (~+1-2ms per session, ~+8-15us per _send_anthropic turn). Input for the audit; the audit quantifies the cost. REGISTRATION: conductor/tracks.md updated: new row 27 (follow-up), new row 28 (parent any_type_componentization), row 17 (code_path_audit) updated with pre-flight adjustments note. Files: - conductor/tracks/phase2_4_5_call_site_completion_20260621/spec.md (NEW; 633 lines) - conductor/tracks/phase2_4_5_call_site_completion_20260621/plan.md (NEW; 7 phases, 23 tasks) - conductor/tracks/phase2_4_5_call_site_completion_20260621/metadata.json (NEW; 8.8KB) - conductor/tracks/phase2_4_5_call_site_completion_20260621/state.toml (NEW; 11.8KB) - docs/reports/PHASE3_HYPOTHETICAL_PROMOTION.md (NEW; 380 lines; qualitative cost analysis) - conductor/tracks/code_path_audit_20260607/spec.md (MODIFIED; +93 lines Pre-Flight Adjustments) - conductor/tracks.md (MODIFIED; +35 lines: 3 new entries + 1 stale row fix) |
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 conductor-tier2
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a9333bbb59 |
conductor(track-update): code_path_audit_20260607 - post-4-tracks timing + 5-source framing
The user specified that the code_path_audit_20260607 track should run
AFTER the 4 foundational tracks complete (qwen_llama_grok,
data_oriented_error_handling, data_structure_strengthening,
mcp_architecture_refactor). This commit formalizes that timing
and grounds the audit's analytical framing in the 5 sources loaded
into context on 2026-06-08.
3 surgical additions to the spec/plan, no task changes:
1. Post-4-tracks timing (new section in spec.md §"Timing", plus
a "Timing" callout in plan.md's opening):
- The 4 tracks will significantly reshape src/ai_client.py,
src/mcp_client.py, src/app_controller.py, and
src/type_aliases.py
- Running the audit on pre-refactor code would produce a
report that's stale on day 1
- The post-4-tracks timing ensures the audit grounds
optimization decisions for the *resulting* architecture
- Pre-flight check: verify all 4 tracks are [x] completed
in conductor/tracks.md before starting this track
2. Analytical framing (new section in spec.md §"Analytical Framing
(5-source lens)"):
- Maps each of the 5 sources (Fleury taxonomy + Fleury
combinatoric + Muratori Big OOPs + Reece Assuming + user's
chunk ideation) to specific audit-time heuristics
- 4 concrete heuristics: effective-codepath count,
entity-hierarchy fingerprint, assumed-too-much detector,
chunkification candidates
- The heuristics shape REPORT INTERPRETATION, not the
static cost model (which stays data-grounded in
EXPENSIVE_THRESHOLD + per-class weights)
3. See Also cross-references in spec.md (6 new entries):
- nagent_review Pitfalls #2 and #4 (provider history
globals + stateful singleton)
- wo84LFzx5nI Big OOPs transcript (full text, 4310
segments, 200KB; loaded 2026-06-08)
- i-h95QIGchY Assuming transcript (full text, 3719
segments, 162KB; loaded 2026-06-08)
- ed_chunk_data_structures_20260523.md (5-image archive
of user's chunk ideation, 19KB; saved 2026-06-08)
- computational_shapes_ssdl_digest_20260608.md (the SSDL
digest that synthesizes the 4-source computational-shapes
thinking; the audit's tree/mermaid outputs ARE
computational-shape visualizations)
4. tracks.md entry updated to include the spec/plan links and
a brief status note that the audit is post-4-tracks.
5. plan.md has a "Timing" callout at the top stating the 4
tracks must ship before the plan executes.
No code modified. The audit's tasks (Phases 1-6) are unchanged
in structure; the new sections only add analytical context
and timing constraints.
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ed
|
ad13007352 |
chore(audit): switch output format from JSON to custom postfix DSL
Per user direction ('make a custom DSL ideal for recording the
call-graph or other metrics', 'I want a post-fix heiarchy', 'JSON
is ill-performant'): replaced JSON serializer with a custom
postfix (RPN) DSL tailored to the audit's record shapes.
THE CUSTOM DSL
- Postfix (operands before operator); no brackets, braces,
commas, or colons.
- Length-prefixed lists: N items followed by 'list' word.
- Tagged records: each 'word' is a constructor with a known
arity (action=3, fn=3, call=1, mut=3, exp-op=5, pair=2, int=1).
- Whitespace-tokenized; bare atoms unquoted; double quotes
only when whitespace/special chars present.
- nil for null; backslash for line comments; true/false for bool.
- Trivial parser (~30 lines): _tokenize_dsl splits on
whitespace and respects quotes + comments; parse_dsl
walks tokens and evaluates tagged words against a known
arity table (DSL_WORD_ARITY).
- Round-trips: to_dsl(profile) -> parse_dsl(to_dsl(profile))
yields the same in-memory structure.
DELIVERABLES (updated spec + plan)
- src/code_path_audit.py: to_dsl, dump_dsl, parse_dsl,
_tokenize_dsl, to_tree (prefix-tree text renderer),
to_markdown, to_mermaid.
- Output: .dsl files (machine) + .tree (human prefix view) +
.md (summary tables) + .mmd (Mermaid diagrams).
- No new pip dependencies; pure stdlib.
WHAT STAYED
- The 7 cost classes (file_io, network, ast_parse, json_io,
pickle, deep_copy, loop_amplified) and 5 mutation kinds
are unchanged. The json_io cost class is for JSON file
I/O the audit detects, not the output format.
- 36 tests total (15 + 8 + 10 + 3 across the 4 implementation
phases).
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ed
|
f069a8b27b |
chore(audit): spec code path audit track
Design for a data-oriented static-analysis tool (src/code_path_audit.py) that audits the 3 major actions (AI message lifecycle, discussion save/load, GUI startup) for expensive operations, redundant calls, and pipelining candidates. Output: JSON data files + markdown summaries + Mermaid per-action call graphs in docs/reports/code_path_audit/. 61 src/ files, 27,447 total lines. Call graph is non-trivial; per-action traversal is what makes analysis tractable. Cost model: 7 cost classes (file_io, network, ast_parse, json_io, pickle, deep_copy, loop_amplified) with heuristic weights; EXPENSIVE_THRESHOLD = 40,000 module constant. 5 state mutation kinds (attr_write, container_mutate, file_write, ipc_emit, global_write). The 3 action entry points are per-action defined (see Per-Action Design table). MMA worker spawn is OUT of scope per user (cold until 1:1 discussion UX is dogfooded). Two follow-up tracks recorded but NOT in this track: - pipeline_runtime_profiling_20260607: calibrate the heuristic cost model with real measurements; catch C-extension cost, decorator dispatch, JIT effects that static analysis can't resolve. - pipeline_pruning_20260607: implement the high-priority optimization candidates surfaced by this track's report. 6 atomic commits planned: data structures; trace_action + ActionProfile + cost model; output (JSON/MD/Mermaid); MCP + CLI; run audit + commit report; tracks.md update. |