PR1 follow-up (the actual IM_ASSERT root cause fix).
The IM_ASSERT in 'MainDockSpace' was triggered by the
render_approve_script_modal function (gui_2.py:4895) calling
imgui.checkbox with a None value for app.ui_approve_modal_preview.
The chain of bugs:
1. AppController.__getattr__ returned None for ANY ui_ attribute
(line 1237-1238). This was intended as a safety net for ui_*
flags defined in __init__ but it was too généreux: it returned
None for ui_ attrs that were NEVER set.
2. The pattern in render_approve_script_modal:
if not hasattr(app, 'ui_approve_modal_preview'):
app.ui_approve_modal_preview = False
_, app.ui_approve_modal_preview = imgui.checkbox(..., app.ui_approve_modal_preview)
relied on hasattr() returning False for unset attrs to trigger
the initialization. But the App.__setattr__ checks
hasattr(self.controller, name) to decide where to route
assignments. The controller's __getattr__ returned None for
ui_approve_modal_preview, so hasattr() returned True. The
App.__setattr__ routed the assignment to the controller.
The controller's __getattr__ then returned None on read,
silently dropping the False value.
3. The next line called imgui.checkbox with None, which raised
a TypeError. The TypeError propagated out of
render_approve_script_modal without closing the modal,
leaving the ImGui scope stack unbalanced. The unbalanced
scope triggered IM_ASSERT(Missing End()) on the next frame.
Fix: AppController.__getattr__ now only returns None for an
EXPLICIT allowlist of ui_ attrs that are defined in __init__.
For any other missing attribute (including the case
'hasattr() should return False'), it raises AttributeError.
The App.__getattr__ was also fixed (per the test) to check
hasattr(controller, name) before delegating. This is defense in
depth in case other __getattr__ patterns are added.
Test verification (TDD red → green):
- 1/1 test_app_getattr_hasattr_bug PASSES (verifies hasattr
returns False for unset attrs via App.__getattr__)
- 1/1 test_app_controller_getattr_ui_bug PASSES (verifies hasattr
returns False for unset ui_ attrs on controller)
Live verification:
- 4 sims + test_live_workflow + 2 markdown tests: 7/7 PASS in 83.15s
- Previously failed at 200s+ with 'cannot schedule new futures after
shutdown' / 121s with 'GUI is degraded before test starts'
- Now passes cleanly. The IM_ASSERT no longer fires.
13/13 related unit tests pass (app_controller_* + app_run_* +
app_getattr_*). No regressions in 51/51 io_pool/warmup/sigint/etc.
unit tests.
The SSDL digest (docs/reports/computational_shapes_ssdl_digest_20260608.md,
504 lines, 30KB) is the theoretical foundation for the chunkification
pattern. Per the digest's Technique 5 "Assume-away (Xar)" in §2.2
and the "Xar-style chunked arrays" recommendation in §5.2, the
chunkification track is a *direct application* of the SSDL's
"assume as much as possible" lens (§4).
This commit adds the SSDL digest to the See Also of the v1+v2
C11-Python interop assessment (front-matter Cross-references line).
The same cross-reference is also being added to:
- conductor/tracks/chunkification_optimization_20260608_PLACEHOLDER/spec.md
(in a new §6.1 "SSDL alignment" subsection)
- conductor/tracks/manual_ux_validation_20260608_PLACEHOLDER/spec.md
(in §5 Architectural Reference + §6 See Also + a new §2.6
"SSDL cross-reference" section that distinguishes GUI ASCII
vocabulary from SSDL vocabulary)
No code modified. Cross-reference only.
Also: small update to conductor/tracks.md to add the 2 new
tracks (manual_ux_validation_20260608_PLACEHOLDER as Active;
chunkification_optimization_20260608_PLACEHOLDER as Backlog/Contingency).
The user pushed back on the v1 recommendation (commit 68354841) twice
in this turn. Both corrections reshape the answer.
Correction 1 (already incorporated): duffle.h + pikuma ps1 are a
C11 STYLE REFERENCE, not an interop pattern. (Captured in v1 §0.)
Correction 2 (NEW, this commit): The C11 path is only worth it under
a hard constraint that no existing Python package can solve. The
shape is request-blob -> C11 pipeline -> response-blob, NOT a
stateful C extension with a Python-facing API. Targets cited:
parsing markdown files/sources into aggregate markdown, context
snapshot processing, "possibly other things."
This commit adds Part 3 (sections 3.1-3.12) to the existing doc.
Part 1 (style) and Part 2 (general interop) stay as background.
Section 4 is re-flagged as "SUPERSEDED - see Part 3".
Part 3 covers:
- The two moves the user's second correction made (threshold-shift
on when, shape-change on what)
- Grounded analysis of the 2 cited targets against actual code:
* src/aggregate.py:380-454 (current markdown hot path is
pure-Python string concat; pyproject.toml has zero
third-party markdown deps)
* src/history.py:1-141 (snapshot processing is bounded
~500KB at 100-snapshot capacity; pickle is the obvious
cheap fix, not C11)
- The request/response wire format design space (text vs binary
vs hybrid envelope-text+payload-binary)
- The pipeline API shape (single C entry point, subprocess-launch
model)
- Revised answer to the "chunkification" question (chunk-array
becomes an internal C implementation detail, not a Python
type)
- Decision tree: profile first, try existing Python packages,
only reach for C11 when hard constraint surfaces
- The 4 questions to revisit when constraint surfaces
- Revised insight: v2 (subprocess + wire format) is strictly
more tractable than v1 (stateful C extension)
- Track implications: chunkification_optimization becomes a
1-page contingency, not a full track; manual_ux_validation
unaffected and confirmed
- v2 verdict matrix (11 rows) replacing v1's 7
Cross-references the actual code paths I read this turn:
- src/aggregate.py:380-454 (build_markdown_from_items)
- src/summarize.py:1-219 (the 3 _summarise_* functions)
- src/history.py:1-141 (UISnapshot, HistoryManager)
- pyproject.toml:6-27 (no markdown deps)
The user is right to push back. The v1 framing was over-engineered.
"Build a stateful C extension" assumed a future need; the actual
answer is "wait for a real bottleneck, then build a simple
subprocess pipeline." The 843-line doc now captures both the
v1 over-engineering AND the v2 contingency plan, so future
sessions can see the iteration and learn from it.
The user asked a sharp, skeptical question: can a chunk-based C11
data structure actually interop with Python's runtime in a way
that's useful for Manual Slop? They explicitly corrected my
first-draft framing (the duffle.h + pikuma ps1 files are a C11
*style reference*, not an interop pattern). The assessment
investigates honestly and reports tractable-vs-not.
docs/reports/c11_python_interop_assessment_20260608.md (564 lines, 38KB):
Part 1: C11 style reference summary
- 11 style observations from reading duffle.h + main.c + pikuma
ps1 duffle/ + hello_gte.c end-to-end
- Byte-width typedef convention (U1/U2/U4/U8, S1/S2/S4/S8, B1-B8, F4/F8)
- The macro meta-DSL (Struct_/Enum_/Array_/Slice_/Opt_/Ret_)
- The I_/IA_/N_ inline discipline
- The r/v pointer rule (restrict OR volatile, never both, never const)
- Slice + Slice_T as the data-structure primitive
- FArena as the allocation primitive (single-buffer, NOT chunked)
- defer/defer_rewind/scope as the cleanup primitive
- KTL (linear key-value table) as the "assume small N" pattern
- What a chunk-array in duffle.h style would look like
Part 2: Interop design space (the actual question)
- 5 candidate interop layers: ctypes, cffi, pybind11, custom
CPython C extension, NumPy wrap
- Honest assessment matrix: build cost, per-op overhead, style
fit, lego-set pattern support
- Verdict: custom CPython C extension is most tractable; pybind11
is style-mismatched; ctypes/cffi work for non-hot-path
- What "MVP chunked C11 package" requires (~500-1000 LOC total)
- 5 questions to ask the user before this becomes a track
- Crucial insight: the user's "unorthodox" interop is most likely
duffle.h-style C11 + thin PyTypeObject glue at the bottom of
the same .h file. Tractable, style-fit high.
Cross-references the 5 sources:
- docs/transcripts/i-h95QIGchY (Reece's Xar reference impl)
- docs/ideation/ed_chunk_data_structures_20260523.md
- docs/reports/session_synthesis_20260608.md (the original proposal)
- src/app_controller.py:716 (the comms.log target)
- The user's local forth_bootslop + pikuma ps1 repos (read in full)
This is a follow-on to the synthesis's 2 proposed tracks
(manual_ux_validation_20260608_PLACEHOLDER + chunkification_optimization_20260608_PLACEHOLDER).
The user's question resolved the "skeptical of #2" concern by
scoping the tractable path: CPython C extension in duffle.h style.
The "lego-set of user-defined Python->C11 chunk ops" is NOT
tractable without a Python->C11 AST emitter, which is a
different (much larger) track.
The user explicitly requested the biggest in-depth report I can
muster at 478,992 tokens (94% of context window). The next
session will start with a fresh context; these two documents are
the minimum-sufficient anchor.
docs/reports/session_synthesis_20260608.md (579 lines, 40KB):
- 12 sections covering every artifact this session produced
- The 5 sources loaded: 2 YouTube transcripts + 2 Fleury
articles + user's chunk-ideation archive
- The 10 commits in the session's commit chain (with the
user's test-fragility work adjacent but not mine)
- The 4 audit-time heuristics derived from the 5-source lens
- The "what the user should know" section for next session
docs/reports/proposed_new_tracks_20260608.md (190 lines, 12KB):
- 2 new tracks proposed (manual_ux_validation_20260608_PLACEHOLDER,
chunkification_optimization_20260608_PLACEHOLDER) with
spec-ready detail
- 8 non-recommendations (so the user knows what I'm NOT
suggesting)
- A "what I'd recommend" section with one-tracks-when
sequencing
No code modified. Both are session-final artifacts, not tracks.
They live in docs/reports/ alongside the other session outputs
(SSDL digest, ASCII-sketch workflow, chunk ideation archive).
Cross-references the 5 sources (all committed to docs/transcripts/
and docs/ideation/ in earlier user commits):
- docs/transcripts/wo84LFzx5nI_big_oops_casemuratori.txt
- docs/transcripts/i-h95QIGchY_assuming_as_much_as_possible_andrewreece.txt
- docs/ideation/ed_chunk_data_structures_20260523.md
- docs/reports/computational_shapes_ssdl_digest_20260608.md
- docs/reports/ascii_sketch_ux_workflow_20260608.md
These 5 documents are the session's "thinking-aid" corpus. The
synthesis is the *index*; together they're the minimum-sufficient
context to re-anchor any future session.
PR3 of the test_full_live_workflow_imgui_assert fix sequence.
When a prior live_gui test in the same session crashes the GUI (e.g.
via an ImGui IM_ASSERT from cumulative panel state), the controller's
_io_pool gets shut down. The next test starts in a degraded state
but only discovers this 120s later when its project switch times
out with a confusing 'cannot schedule new futures after shutdown'
error.
This commit adds a /api/gui_health pre-flight check at the start of
test_full_live_workflow. If the GUI is degraded, the test fails
fast (within 1s) with a clear, actionable message that includes:
- The exact RuntimeError that caused the degradation
- The full traceback of the last ImGui scope mismatch
- A note that the new test cannot proceed with a dirty state
Per user feedback 2026-06-08: 'I don't want a batch to be too fragile
where I can't restart the app and continue with the next test file
if it fails. Just has to note that the new file didn't get to deal
with a dirty state.'
Also includes the planning documents written earlier in this session:
- TODO_test_full_live_workflow_v2.md (task list)
- test_full_live_workflow_imgui_assert_20260608.md (root cause report)
- test_full_live_workflow_propagation_digest_20260608.md (solutions digest)
- batch_resilience_plan_20260608.md (batch resilience plan)
Verification:
- test_full_live_workflow in isolation: 13.45s PASS (health=True, no degrade)
- 4 sims + test_full_live_workflow in batch: 76.46s (1 FAIL fast, 4 sims PASS)
- Without PR3 fix: 200s FAIL with confusing 120s timeout
- With PR3 fix: 76s FAIL with clear 'GUI is degraded' message
- The fast-fail is observable, not silent (per user's 'wrap might be
worth it if that properly lets us handle the assert')
Every direct dep in pyproject.toml now has a ~X.Y.Z bound
(patch-only). The 7 unconstrained deps (imgui-bundle,
anthropic, google-genai, openai, fastapi, mcp, uvicorn,
plus tomli-w) get explicit tilde bounds discovered from
uv.lock. The 6 >=X.Y.Z deps are normalized to tilde-style
(pinned to the current lock version).
The local-rag optional dep (sentence-transformers) is also
tilde-pinned.
requirements.txt is deleted (was redundant with uv.lock;
the uv project uses uv.lock as the canonical lock file,
which is regenerated locally and gitignored per project
policy at .gitignore:9).
Re-running the audit confirms 0 PIN_VIOLATION (was 7). The
final.md report records the post-cleanup state.
Also adds --report-name CLI flag to the audit script
(default 'initial') so the script can write either
initial.md (Phase 1) or final.md (Phase 2) into the same
report directory.
scripts/audit_license_cve.py: 4 internal checks (license +
CVE + pin + source-header), policy tables (allowlist of
permissive/weak-copyleft/public-domain, blocklist of
non-OSI/restricted-source), and a main() that runs all 4
and emits line-per-violation to stdout + a markdown report.
Tests (26 unit + integration) cover license classifier (16
variants across MIT, BSD, Apache, LGPL, MPL, CC0, WTFPL,
GPL, AGPL, SSPL, BSL, Commons Clause, Elastic, Anti-996,
Hippocratic, unknown), pin check (3), source-header check
(3), license check via importlib.metadata (1), CVE check
via subprocess pip-audit (2), and a smoke test of the main
loop (1).
No new pip deps in the project: pure stdlib
(importlib.metadata, tomllib, pathlib, re) + subprocess to
pip-audit (optional dev tool, installed via 'uv tool install
pip-audit' if user wants CVE checks).
Initial report at docs/reports/license_cve_audit/2026-06-07/
records the current state. The Phase 2 commit will apply
the fixes (tilde-pin, delete requirements.txt); the Phase 3
commit will add --strict mode + baseline file for CI.
Single-session planning digest that captures:
- The 5 tracks fully specced + planned (test_batching, qwen_llama_grok,
data_oriented_error_handling, data_structure_strengthening,
mcp_architecture_refactor)
- Cross-cutting design themes (data-oriented, audit-driven, per-track
commit + git note, out-of-scope-by-default)
- The audit + data foundation (scripts/audit_weak_types.py; 430 -> 60
finding; 0 strong patterns; 26 unique type strings; 86% concentrated
in 6 files)
- The dependency graph + recommended execution order
- Follow-up tracks already planned in spec §12.1 of each track
- Recommended future tracks (post-tracks documentation is the top pick)
- Risks, open questions, and a complete file index
This is the kind of reference document that:
- Future planners consult to understand the codebase's current state
- The implementing agent uses to coordinate across tracks
- The user reviews as a digest of the planning work
Written in the project's docs/reports/ directory alongside the existing
Phase 5 reports (PHASE5_STABILISATION_REPORT.md, MUTATION_MATRIX_PHASE5.md, etc.).
Phase 1, Tasks T1.2 + T1.4 of the startup_speedup_20260606 track.
NEW: scripts/audit_main_thread_imports.py
Static CI gate that AST-walks the import graph reachable from
sloppy.py and fails (exit 1) if any heavy module is imported at the
top of a main-thread-reachable file. Walks into if/elif/else and
try/except branches (which run at import time) but skips function
bodies (which only run when called). Allowlist: stdlib + the lean
gui_2 skeleton (imgui_bundle, defer, src.imgui_scopes, src.theme_2,
src.theme_models, src.paths, src.models, src.events).
NEW: scripts/audit_gui2_imports.py
Read-only analysis tool that lists every top-level and function-level
import in src/gui_2.py, classified by location. Used in Phase 5D to
identify which imports to remove.
NEW: tests/test_audit_main_thread_imports.py
9 tests covering: --help exits 0, clean stdlib-only passes, heavy
third-party fails, google.genai fails, transitive walks, function-
body imports ignored, if-branch imports flagged, try-block imports
flagged, file:line reported. All 9 pass.
NEW: docs/reports/startup_baseline_20260606.txt
3-run median cold-start benchmark. Worst offenders: src.gui_2
(1770ms), simulation.user_agent (1517ms), google.genai (1001ms),
openai (482ms), anthropic (441ms), imgui_bundle (255ms),
src.theme_nerv* (485ms combined), src.markdown_table (243ms),
src.command_palette (242ms).
NEW: docs/reports/startup_audit_20260606.txt
Audit output on the CURRENT codebase. Reports 67 violations across
the main-thread import graph (incl. numpy in src/gui_2.py:9,
tomli_w in src/gui_2.py:18, fastapi + requests in src/app_controller,
tree_sitter_* in src/file_cache, pydantic in src/models, plus all
the src.* subsystem imports that drag in heavy transitive deps).
Phase 3-5 of the track will resolve these one by one.
After Phase 3-5, this audit must exit 0 (no violations).
Co-located reports in docs/reports/ per project convention; the other
agent finished their work in docs/superpowers/ and is unrelated.
The AI client decoupling was never properly implemented and added
unnecessary complexity. The actual startup bottleneck was RAG initialization
which is now handled via async initialization.
Report written to docs/reports/ai_decoupling_revert_report.md
ed
conductor-tier2