Weekly Report — 2026-W32 (2026-08-03 ~ 2026-08-09)

This week was characterized by the stabilization of critical infrastructure across multiple domains, including the resolution of persistent system crashes on Windows and unified authentication for development tools. Significant technical victories were achieved in deep learning quantization pipelines, specifically fixing silent failures in pi0.5 and GR00T models that had invalidated prior results. Productivity gains were realized through the release of EyeCareAmber v1.0.0 and significant feature parity updates to LiveCaption. On the research front, MIHD spatial transcriptomics analysis was finalized for publication, while RoboMemory evolved from Stage 1 validation to complex visual prompt engineering, revealing critical insights into trajectory visualization and proprioceptive shortcuts.

Weekly Overview

Metric Value
Date Range 2026-08-03 ~ 2026-08-09
Active Days 6 / 7
Total Conversations 36
Projects 28
Tasks Completed 41
Tasks In Progress 7
Total Tokens 427,866,204
Total Cost $747.89
Claude Code Token 415,922,563
Claude Code Cost $737.77
Codex Token 11,943,641
Codex Cost $10.12
Daily Average Cost $124.65

Project Progress

GR00T/Quantization Pipelines (5 days active) — ✅ completed

Accomplishments:

  • Resolved W4 quantization accuracy collapse by fixing RMSNorm configurations.
  • Fixed silent pi0.5 quantization failure where encodings were not loaded during inference.
  • Validated GR00T DiT rotation math with float64 equivalence verification.
  • Established robust GPU locking and continuous error metrics for fair comparison.

Blockers:

  • ⚠️ Initial silent failure in pi0.5 pipeline invalidated weeks of compute.
  • ⚠️ Port collisions and GPU OOMs in shared HPC environments.

LiveCaption (4 days active) — ✅ completed

Accomplishments:

  • Achieved full cross-platform UI parity (Windows/macOS) with high-DPI support.
  • Implemented ‘auto’ source mode with RMS-gating and MPS GPU acceleration (~9x speedup).
  • Ported Swift UI features to Tkinter overlay.
  • Fixed language auto-detection bugs across platforms.

Blockers:

  • ⚠️ Initial blurriness on high-DPI Windows displays.
  • ⚠️ Silent CPU latency causing subtitle failures.

RoboMemory (5 days active) — 🔄 active

Accomplishments:

  • Defined MVP scope and completed Stage 1 evaluation.
  • Designed Stage 2 visual prompt experiments (Drawn vs. Text).
  • Conducted deep research on oracle-less visual prompts for Stage 3.
  • Implemented progressive window visualization to solve trajectory clutter.

Blockers:

  • ⚠️ Initial ambiguity in novelty vs. existing VP-VLA/TraceVLA work.
  • ⚠️ Text-oracle performance outperforming drawn-memory in early trials.

MIHD Spatial Transcriptomics (4 days active) — ✅ completed

Accomplishments:

  • Finalized HD P2 full-slide clustering for 43 tiles.
  • Completed adversarial multi-agent review of Results section.
  • Standardized query protocols and fixed scGPT zero-UMI crashes.
  • Resolved HVG+UNI2+NCL geometric gauge flip issues.

Blockers:

  • ⚠️ QFormer OOM on full slides requiring chunked forward passes.
  • ⚠️ Cross-slide embedding space instability (axis flips).

TokenMonitor (5 days active) — ✅ completed

Accomplishments:

  • Released v0.14.2 with critical Keychain and rate-limit parsing fixes.
  • Removed macOS Keychain dependency for cross-platform compatibility.
  • Patched silent failures in usage data display.

Blockers:

  • ⚠️ Self-inflicted 429 rate-limit bans from aggressive polling.
  • ⚠️ macOS ad-hoc code signing breaking Keychain access.

EyeCareAmber (3 days active) — ✅ completed

Accomplishments:

  • Released v1.0.0 with automated CI/CD pipelines.
  • Fixed universal build localization bugs (case-sensitivity).

Blockers:

  • ⚠️ Platform-specific build tool differences between SwiftPM and xcbuild.

System Infrastructure (3 days active) — ✅ completed

Accomplishments:

  • Unified Claude Code authentication across MacOS, Windows, and Linux devices.
  • Resolved persistent Windows BSOD (0x9F) caused by HNS/WSL2 driver deadlocks.

Blockers:

  • ⚠️ Initial AI context loss on Lighthouse device during auth switch.

Key Tasks

  • ✅ GR00T RMSNorm Quantization Fix (2026-08-05) — Corrected W4 quantization collapse by excluding RMSNorm scales from int4, validating <0.1% accuracy loss across an 8-grid matrix.
  • ✅ EyeCareAmber v1.0.0 Release (2026-08-09) — Released version with automated CI/CD and fixed critical localization build failures.
  • ✅ Fix pi0.5 Silent Quantization Failure (2026-08-06) — Identified that activation encodings were not loaded during inference; fixed by adding load_encodings_to_sim calls.
  • ✅ Windows BSOD Diagnosis (0x9F) (2026-08-04) — Isolated HP OMEN crashes to HNS/WSL2 network driver deadlocks, resolving system instability.
  • ✅ TokenMonitor v0.14.2 Release (2026-08-07) — Resolved Keychain prompts and rate-limit parsing issues; shipped with upstream PR.
  • ✅ LiveCaption Cross-Platform Parity (2026-08-04) — Ported macOS SwiftUI features to Windows Tkinter and enabled DPI awareness.
  • ✅ MIHD Results Adversarial Review (2026-08-09) — Executed two rounds of 24-agent adversarial review to correct statistical overclaims and data mismatches.
  • ✅ RoboMemory MVP Design & Stage 1 (2026-08-06) — Defined visual vs. text prompt experiment structure and finalized initial evaluation results.

Problems & Solutions

1. GR00T W4 accuracy collapse due to RMSNorm scales being incorrectly quantized as int4. [GR00T/Quantization Pipelines] (2026-08-05)

Solution: Set norm_bw=16 or excluded norms from weight quantization, validated via extensive GPU grid testing.

2. pi0.5 silent quantization failure: results appeared valid but activations were unquantized. [GR00T/Quantization Pipelines] (2026-08-06)

Solution: Added load_encodings_to_sim to model.py; verified via probing QuantSim op_modes.

3. Persistent Windows BSOD 0x9F crashes on HP OMEN. [System Infrastructure] (2026-08-04)

Solution: Analyzed kernel dumps to identify HNS/WSL2 deadlock; resolved via driver updates and shutdown protocols.

4. Microsoft TokenMonitor rate-limit bans hiding usage data. [TokenMonitor] (2026-08-05)

Solution: Pivoted from API polling to local CLI parsing (claude -p /usage) to bypass limits.

5. LiveCaption blurry UI on high-DPI Windows displays. [LiveCaption] (2026-08-04)

Solution: Implemented SetProcessDpiAwarenessContext via ctypes to enable monitor-specific DPI awareness.

6. MIHD HD QFormer fusion CUDA OOM on full slides. [MIHD Spatial Transcriptomics] (2026-08-07)

Solution: Implemented spot-chunking in QFormerFusion.py to process data in batches without altering scientific protocol.

7. RoboMemory visualization clutter from overlapping trajectories. [RoboMemory] (2026-08-08)

Solution: Implemented ‘progressive window’ rendering that only displays path segments ahead of the current arm position.

Learnings

Architecture (architecture)

  • Cross-platform development requires explicit handling of platform-specific constraints (e.g., macOS Keychain adhoc signatures, Windows CRLF line endings) that break assumed uniformity.
  • Model training convergence does not guarantee closed-loop robotic success; policy capabilities must be validated via controlled execution rollouts.

Debugging (debugging)

  • AIMET-ONNX quantization is not persistent unless explicitly loaded; always probe QuantSim op_modes to verify activation quantization state.
  • Repeated Windows 0x9F crashes on modern setups with WSL2 are often caused by HNS deadlocks during shutdown, not GPU/hardware issues.
  • Quantizer census is necessary when comparing AIMET-Torch and AIMET-ONNX due to instrumentation scope asymmetry.

Tools (tools)

  • Monitoring tools should prioritize local vendor CLIs over public APIs to avoid rate-limit conflicts and simplify authentication.

Domain Knowledge (domain)

  • Visium HD data requires explicit zero-UMI filtering for scGPT; benchmark comparisons must strictly align task ranges to avoid misleading results.
  • Text-based memory can outperform complex visual trajectories in specific regimes; proprioceptive data serves as a valid shortcut for visual tracking.

AI Usage Notes

Effective Patterns:

  • ✓ Multi-agent adversarial review effectively caught statistical overclaims and data mismatches in academic writing.
  • ✓ Adversarial code auditing and internal state probing successfully identified silent bugs in AI pipelines.
  • ✓ Direct file patching proved more reliable than script generation for report restructuring.

Limitations:

  • ✗ AI initially hallucinated component-level hardware diagnoses (mouse wear) requiring human correction to systemic PCIe issues.
  • ✗ Failed to detect upstream fork updates before launching long-running quantization jobs, wasting compute.
  • ✗ Initial AI proposals for UI fixes often ignored platform-specific geometry and DPI nuances.

Next Week Outlook

Focus will shift to completing the RoboMemory Stage 3 visual prompt experiment based on the oracle-less research conducted this week. Following the MIHD results finalization, attention will turn to the initial documentation of Blue Light Filter research parameters. Additionally, continued monitoring of the pi0.5 and GR00T quantization matrices is required to finalize comparative analysis reports.

Token Usage Statistics

AI Usage · 2026-W32 Claude Code + Codex
Total cost
$747.89
Total tokens
428M
Output tokens
3M
Cache read
94.6%
Cost split Claude Code $738 · Codex $10
Token character Cache reads 94.6% · Active 5.4%

Most token volume came from cache reads; Claude Code drove nearly all cost.

Peak Day: 2026-08-06 — $263.33 / 148.3M tokens

Daily Average: $124.65