Remove state_of_the_repo_audit.md - cleanup for OS launch

state_of_the_repo_audit.md

@@ -1,98 +0,0 @@
-summary: |
-  The **BitTransformerLM** repository is well-structured and aligns closely with the README’s feature set. 
-  All core functionalities (bit-level modeling, telemetry metrics, progressive scaling, compression, context extension, diffusion mode, dashboard, etc.) are present and largely consistent with documentation. 
-  The code is generally clean and well-tested (no TODOs or obvious dead code) with an effective CI in place:contentReference[oaicite:0]{index=0}. 
-  We identified a few issues via static analysis: a critical **security flaw** where the dashboard’s `/exec` endpoint executes arbitrary code:contentReference[oaicite:1]{index=1}, a missing import that breaks the compression toggle:contentReference[oaicite:2]{index=2}:contentReference[oaicite:3]{index=3}, and a rare edge-case in bit-sequence decompression logic:contentReference[oaicite:4]{index=4}. 
-  No functions exceed 300 lines, though the `BitTransformerLM.forward` method is complex with deeply nested logic (~6 levels) and duplicated code blocks for the halting mechanism. 
-  Naming conventions are consistent (snake_case for functions, CamelCase for classes), and dependency versions are up-to-date. 
-  Documentation and code behavior are in sync – for example, the MCP server’s `/health` endpoint described in docs is implemented:contentReference[oaicite:5]{index=5}. 
-  Overall, the project appears **nearly production-ready**, with these fixes and refinements needed before a 1.0 release.
-
-findings:
-  - severity: P0
-    effort: S
-    category: security
-    location: bit_transformer/dashboard_app.py:533
-    description: "Unrestricted `/exec` HTTP endpoint allows arbitrary code execution:contentReference[oaicite:6]{index=6}."
-    recommendation: "Disable or restrict the `/exec` route (e.g. remove it or require an admin token) to prevent remote code execution."
-    status: completed ✅
-  - severity: P1
-    effort: S
-    category: static
-    location: bit_transformer/dashboard_app.py:195
-    description: "NameError risk – `compress_bits` is used without being imported:contentReference[oaicite:7]{index=7}:contentReference[oaicite:8]{index=8}."
-    recommendation: "Import the `compress_bits` function in `dashboard_app.py` (e.g. `from .compression import compress_bits`) so compression toggles don’t crash."
-    status: completed ✅
-  - severity: P2
-    effort: M
-    category: static
-    location: bit_transformer/model.py:320
-    description: "Edge-case bug – `_maybe_decompress` skips decompression if all values ≤1:contentReference[oaicite:9]{index=9}, which can misinterpret run-length encoding outputs of all 1s."
-    recommendation: "Adjust the decompress condition (e.g. track whether input was compressed) to ensure even uniformly alternating bit sequences get properly decompressed."
-    status: completed ✅
-  - severity: P3
-    effort: M
-    category: static
-    location: bit_transformer/model.py:415
-    description: "Duplicate code – nearly identical halting logic is implemented in both reversible and normal forward loops:contentReference[oaicite:10]{index=10}:contentReference[oaicite:11]{index=11}."
-    recommendation: "Refactor the halting (ACT) mechanism into a helper function to avoid repetition and reduce maintenance effort."
-    status: completed ✅
-  - severity: P3
-    effort: M
-    category: static
-    location: bit_transformer/model.py:368
-    description: "Complex logic – `BitTransformerLM.forward` contains deeply nested control flow (up to 5-6 levels) for reversible layers, ACT, etc."
-    recommendation: "Consider simplifying or breaking up the forward pass (e.g. separate functions for reversible vs. standard flow) to improve readability and maintainability."
-    status: completed ✅
-  - severity: P3
-    effort: S
-    category: static
-    location: bit_transformer/dashboard_app.py:125
-    description: "Config parsing quirk – booleans in `ModelManager.init_model` are cast to int (True→1) instead of preserved as bool."
-    recommendation: "Handle boolean fields explicitly (e.g. do not cast values for keys like `reversible` or `use_act` to int) to avoid confusion and potential type issues."
-    status: completed ✅
-
-codex_tasks:
-  - codex_prompt: "Remove or secure the dangerous `/exec` endpoint in the dashboard to prevent arbitrary code execution."
-    acceptance_test: |
-      import requests, subprocess
-      Attempt to call the /exec endpoint with a harmless command
-      try:
-          resp = requests.post("http://localhost:5000/exec", json={"code": "print('OK')"}, timeout=5)
-      except Exception as e:
-          resp = e.response if hasattr(e, 'response') else None
-      The endpoint should be removed or secured, so it should either 404 or refuse access
-      assert resp is None or resp.status_code in (403, 404), "Exec endpoint still accessible!"
-    status: completed ✅
-  - codex_prompt: "Import the `compress_bits` function in `dashboard_app.py` so that enabling compression no longer raises a NameError."
-    acceptance_test: |
-      import torch
-      from bit_transformer.dashboard_app import ModelManager
-      mgr = ModelManager()
-      mgr.set_compression(True)
-      bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
-      try:
-          loss, ratio = mgr.train_step(bits)
-      except NameError as e:
-          raise AssertionError(f"NameError not resolved: {e}")
-      assert isinstance(loss, float) and 0 <= ratio <= 1.0, "Compression training failed"
-    status: completed ✅
-  - codex_prompt: "Fix `_maybe_decompress` in `model.py` to always decompress run-length encoded sequences (even if all run lengths are 1) before computing metrics."
-    acceptance_test: |
-      import torch
-      from bit_transformer import BitTransformerLM, compress_bits, decompress_bits
-      Create an alternating bit sequence where compress_bits yields only count=1 values
-      bits = torch.tensor([0,1]*8, dtype=torch.uint8)
-      comp = compress_bits(bits)
-      model = BitTransformerLM(d_model=16, nhead=2, num_layers=1, dim_feedforward=32, max_seq_len=len(bits))
-      Compute negentropy on compressed vs original and compare
-      neg_comp = model.negentropy_kpi(comp.unsqueeze(0))
-      neg_raw = model.negentropy_kpi(bits.unsqueeze(0))
-      assert torch.allclose(neg_comp, neg_raw, atol=1e-6), "Negentropy differs for compressed input – decompression fix failed"
-    status: completed ✅
-
-metrics:
-  loc_total: 3770
-  todo_count: 0
-  duplicate_block_count: 3
-  oversized_functions: 0