Daily Report — 2026-06-14

Daily Overview

• What was done: Addressed high-priority infrastructure failures across Linux and macOS environments; executed strategic architectural migrations for project dependencies; performed rigorous benchmarking of foundation models in bioinformatics contexts; and delivered major feature sets with stability improvements for consumer applications.
• How it was done: Diagnosed Secure Boot kernel module conflicts on DockerLinux via dmesg; cleared Vite optimize-deps caches on NTFS mounts; analyzed ccusage v20 CLI output schemas to implement per-source token tracking; ran 100-seed mclust sweeps to disprove STAIG reproducibility claims; and implemented per-screen history logic with synchronous cache seeding for SwiftUI apps.
• Impact: Restored GPU compute capability and build stability; established a scalable, multi-agent token tracking architecture independent of legacy split dependencies; provided definitive evidence that UNI2 embeddings outperform gene FMs in spatial clustering; and enhanced Desktop Video with robust slideshow/folder support while eliminating UI flickering and history filtering bugs.

DCC

• What was done: Audited MIHD Task 3 benchmarks (QueST-style niche query); conducted deep security review of Gadget’s ccusage migration; executed parallel seed-sweep experiments for STAIG fusion verification.
• How it was done: Inspected benchmark_rm_ideal.py and niche utilities; traced taint paths in save_usage_file; utilized SLURM with 32-worker processes for statistical analysis of clustering ARI scores.
• Impact: Defined MIHD’s shared-space embedding strategy; identified and gated path traversal vulnerabilities in code migration; proved UNI2 + staig_fusion achieves superior ARI (0.703) compared to original STAIG (0.68) and collapsed scGPT performance.

DesktopLinux

• What was done: Resolved critical NVIDIA RTX 5090 driver failure on the primary workstation; implemented Phase 1-2 of ccusage 20.x migration.
• How it was done: Removed self-signed DKMS modules in favor of Canonical-signed kernel modules via depmod; iteratively refactored JSON parsing and per-source discovery logic across daily/monthly report generators.
• Impact: Restored full CUDA acceleration for computational workflows; successfully migrated token usage tracking to unified ccusage 20.x architecture, enabling support for 15+ coding agents without silent data breaks.

MacBook

• What was done: Implemented cross-machine usage sync for TokenMonitor via export/import tagging; fixed FloatBall Vite CSS module loading errors; developed seven core features for Desktop Video including auto-slideshow and per-screen history.
• How it was done: Analyzed .vite-temp artifacts to diagnose NTFS-induced cache desync; modified Rust backend to hostname-tag exports; implemented NSCache synchronization in SwiftUI view init to eliminate flicker during tab switches.
• Impact: Enabled seamless multi-device usage tracking without cloud dependencies; restored development stability on external drives; delivered v5.0 hot-fixes ensuring sandbox compliance and robust state management for complex media workflows.

Resolved critical infrastructure issues including NVIDIA driver failures and Vite cache desyncs, completed the ccusage 20.x migration for the Gadget project, validated UNI2 as superior to scGPT/TEDDY for spatial transcriptomics clustering via STAIG fusion, and delivered significant feature expansions and UI stability fixes for Desktop Video.

Tasks

Architecture & Strategy

• ✅ ccusage 20.x Migration for Gadget Project — Designed and executed migration from legacy @ccusage/codex split to unified ccusage 20.x. Implemented per-source discovery, JSON schema normalization, and dynamic report/chart generation supporting N sources rather than fixed Claude/Codex pairs.
• ✅ STAIG Fusion Benchmarking & Encoder Evaluation — Verified STAIG equivalence, discovered mclust seed sensitivity causing non-reproducible ARI scores. Ran 100-seed sweeps proving UNI2 (0.703 ARI) outperforms original STAIG (0.68 ARI), while scGPT/TEDDY collapse under GCN fusion.
• ✅ Desktop Video Feature Expansion & Stability Fixes — Developed 7 new features including auto-slideshow, folder support, and Web URL preview restoration. Fixed critical regressions: per-screen history tracking, synchronous cache seeding to eliminate preview flicker, and secure bookmark resolution for gallery thumbnails.
• ✅ TokenMonitor Cross-Machine Sync & Vite Fix — Implemented hostname-tagged export/import for cross-device usage sync. Diagnosed and fixed FloatBall Virtual CSS loading failure caused by stale Vite optimize-deps cache on NTFS mounts.
• ✅ NVIDIA Driver Recovery on DesktopLinux — Resolved ‘Key was rejected by service’ error caused by Secure Boot blocking DKMS modules. Fixed by removing self-signed packages and loading Canonical-signed modules.
• ✅ MIHD Task 3 Benchmark Audit & GCD Alignment — Audited MIHD codebase confirming QueST-style niche query implementation. Clarified strategic distinction between MIHD’s ‘retrieval’ and OmniGCD’s ‘discovery’ tasks, establishing shared-space embedding as the theoretical link.

Implementation & Fixes

• ✅ Desktop Video Console Log Triage & Asset Robustness — Filtered macOS system logs to distinguish benign CoreMedia/WebKit noise from actionable defects. Fixed layout recursion in screensaver clock and ensured AVDataAsset throws on file write failure for reliability.

Problems & Solutions

Critical Issues

1. STAIG’s reported 0.68 ARI was non-reproducible and dependent on mclust initialization seeds; scGPT/TEDDY embeddings collapsed in spatial clustering.

Solution: Performed 100-seed statistical sweeps revealing high variance (0.49-0.64) and confirming UNI2’s superiority (max 0.703 ARI). Identified ‘gene-FM paradox’ where gene foundation models lose spatial discriminative signal compared to HVG.

Key Insight: Published benchmarks for stochastic algorithms require statistical validation; general-purpose gene encoders may be incompatible with graph contrastive learning for spatial domains.

2. ccusage 20.x unified output merges all agent sources, losing per-source granularity necessary for dual-track reporting.

Solution: Discovered ccusage <source> daily allows namespaced queries. Implemented per-source discovery via metadata and individual fetching with schema normalization, ensuring backward compatibility and future scalability.

Key Insight: CLI dependency upgrades often alter JSON schemas silently; immediate local testing of new output formats is critical before architectural changes.

3. Desktop Video gallery thumbnails were blank due to sandbox bookmark scope mismatches and missing cache keys.

Solution: Implemented dual-branch bookmark resolution (scoped + fallback) and updated cache keys to include content modification dates. Added synchronous state seeding in SwiftUI views to prevent flicker during navigation.

Key Insight: Async failures often corrupt state machines; sandboxed macOS apps require explicit non-scoped fallbacks for legacy data integrity.

4. NVIDIA RTX 5090 driver failed due to Secure Boot rejecting self-signed DKMS modules.

Solution: Removed nvidia-dkms package and loaded Canonical’s pre-signed kernel module, resolving the ‘Key was rejected’ error without disabling Secure Boot.

Key Insight: On modern Ubuntu with Secure Boot, distribution-managed signed modules are safer and more reliable than custom DKMS builds.

5. Vite dev-server failed to load FloatBall CSS module on NTFS-mounted drives due to stale .vite-temp caches.

Solution: Killed dev server, cleared node_modules/.vite and .vite-temp, and restarted. Root cause was Vite’s atomic rename failures on flaky FUSE mounts.

Key Insight: Vite’s dependency optimization is sensitive to file system stability; stale temp artifacts desync the module graph in lazily-loaded components.

6. History view showed identical lists across different screens due to global filtering and missing screen attribution.

Solution: Refactored data model to include ‘playedScreens’ mapping in WallpaperHistoryEntry. Implemented per-screen trimming and migration logic for legacy items without screen IDs.

Key Insight: Global data structures fail to support multi-tenant or multi-display contexts; schema migrations must explicitly handle backward compatibility and dimension-aware limits.

7. Security vulnerability: Path traversal possible in workflow hook exemption checks and usage file saving during ccusage migration.

Solution: Flagged exemption bypasses in check_spec.py (need canonical path resolution) and recommended defense-in-depth for save_usage_file filename sanitization, though upstream ccusage validation provided mitigation.

Key Insight: String-based path checks (startswith) are vulnerable to traversal; external tool exit codes should not be the sole security gate for file system operations.

Human vs AI Approaches

Strategic Level

Statistical Validation vs. Single-Point Debugging (STAIG)

Role	Approach
Human	User rejected single-run ARI claims, mandating a 100-seed sweep to characterize mclust variance, correctly identifying the stochastic nature of the baseline.
AI	Initially focused on deterministic debugging (implementation errors) before pivoting to experimental design after user insistence on statistical distribution analysis.

Difference Analysis: Human drove strategic experimental validation to uncover nonreproducibility, while AI provided computational execution for the sweeps.

Architectural Constraints in ccusage Migration

Role	Approach
Human	User insisted on ’local-first’, zero-friction sync strategies and maximum compatibility, rejecting initial AI proposals for cloud APIs or hybrid approaches.
AI	Initially explored external APIs and defensive hybrid designs before adapting to the user’s constraint of leveraging internal export/import structures with hostname tagging.

Difference Analysis: Human maintained strict project identity constraints (open-source/local), guiding AI away from over-engineered cloud solutions toward robust internal refactoring.

Visual Consistency vs. Logical Throttling (Desktop Video)

Role	Approach
Human	User required instant thumbnail rendering without flicker during tab switches, rejecting AI’s initial proposal of task suppression/throttling.
AI	Pivoted from logic-based throttling to structural synchronization, implementing synchronous NSCache lookups in view initialization to guarantee data availability before render.

Difference Analysis: Human focused on perceptual outcome (no flicker), forcing AI to move beyond logical delays to data-structure guarantees.

Implementation Level

Manual Code Verification vs. Static Analysis (Desktop Video Logs)

Role	Approach
Human	Manually traced AVFoundation reference chains to correct AI’s false positive warning about AVDataAsset deallocation.
AI	Autonomous agents guessed scope-based deallocation based on generic Swift patterns, failing to account for AppKit/AVFoundation specific strong reference semantics.

Difference Analysis: Human demonstrated superior domain-specific context awareness, overriding AI’s generalized static analysis with concrete code evidence.

AI Limitations

Critical Limitations

• Initial workflow agents incorrectly flagged AVDataAsset memory retention as a bug due to limited context of AppKit reference semantics.

General Limitations

• AI initially assumed niche sampling needed to be built from scratch for MIHD, failing to immediately identify existing infrastructure in niche_utils.py.
• AI struggled to explain STAIG’s ARI discrepancy without experimental verification, relying on textual analysis before pivoting to code execution.
• AI initially hypothesized CRLF line endings or syntax errors for Vite failures, requiring further evidence to identify the actual NTFS cache desync issue.
• AI attempted to propose cloud-based sync solutions for TokenMonitor, ignoring the user’s implicit ’local-first’ constraint until explicitly corrected.

Learnings

Key Learnings

• mclust EM clustering is highly sensitive to initialization basins (n>2000); single-run ARI scores are unreliable benchmarks without multi-seed validation.
• On NTFS/FUSE mounts in Linux, Vite’s atomic dependency optimization can fail silently, leaving .vite-temp artifacts that desync the dev-server module graph for lazy-loaded components.
• CoreMedia and WebKit sandbox logs are often benign noise; false positives in log triage can lead to chasing phantom bugs if framework semantics are not understood.

Practical Learnings

• Per-source token tracking requires robust schema normalization (_normalize_usage) because different agent CLIs often use incompatible JSON field names.
• Global data limits fail in multi-display environments; history/tracking systems must be dimension-aware and keyed by unique identifiers (e.g., screen UUID).

Conversation Summaries

MIHD Task 3 & OmniGCD

**✅ ** Audited MIHD’s benchmark_rm_ideal confirming QueST-style niche query infrastructure. Analyzed embeddings to show shared-space (scGPT/UNI2) outperforms PCA in cross-section retrieval. Clarified strategic distinction between MIHD’s ‘retrieval’ and OmniGCD’s ‘discovery’ tasks, proposing synthetic classes for GCD evaluation.

STAIG & Spatial Transcriptomics

**✅ ** Verified STAIG fusion equivalence but proved its 0.68 ARI claim non-reproducible via 100-seed sweeps. Demonstrated UNI2 achieves superior clustering (0.703 ARI) while gene FMs (scGPT/TEDDY) collapse under GCN, confirming the ‘gene-FM paradox’ for spatial domain tasks.

Gadget (ccusage Migration)

**✅ ** Executed comprehensive migration to ccusage 20.x unified architecture. Resolved schema breaking changes by implementing per-source discovery and normalization. Conducted security reviews identifying path traversal risks in workflow hooks, resolved via canonical path logic.

Desktop Video

**✅ ** Delivered 7 core features including auto-slideshow and folder support. Fixed critical UI regressions: implemented per-screen history tracking, synchronous cache seeding to eliminate preview flicker, and robust sandbox bookmark resolution for thumbnails. Verified AVDataAsset integrity and filtered benign system logs.

TokenMonitor

**✅ ** Implemented cross-machine usage synchronization via hostname-tagged export/import files, avoiding cloud dependencies. Fixed FloatBall virtual CSS module loading errors caused by Vite cache desync on NTFS mounts.

Token Usage