Weekly Report β 2026-W07 (2026-02-09 ~ 2026-02-15)
This week was characterized by significant architectural overhauls and scalability improvements across multiple high-impact domains, including the implementation of two-phase caching pipelines for MIHD spatial transcriptomics (reducing redundant computation by 62%), the massive scaling of the Error Recovery robotics benchmark from 3 to 118 samples, and the hardening of the Gadget/Summarize and CalendarPro toolkits through robust multi-device sync and OAuth optimization. Despite facing hardware resource constraints like VRAM saturation and environmental dependency blockers in robotics, key progress was made in establishing crash-resilient, cost-effective workflows and structured multi-phase enhancement roadmaps for future development.
Weekly Overview
| Metric | Value |
|---|---|
| Date Range | 2026-02-09 ~ 2026-02-15 |
| Active Days | 6 / 7 |
| Total Conversations | 39 |
| Projects | 16 |
| Tasks Completed | 55 |
| Tasks In Progress | 5 |
| Total Tokens | 172,203,620 |
| Total Cost | $68.24 |
| Claude Code Token | 132,449,921 |
| Claude Code Cost | $56.74 |
| Codex Token | 39,753,699 |
| Codex Cost | $11.50 |
| Daily Average Cost | $11.37 |
Project Progress
MIHD Enhancement & Benchmarking (3 days active) β π active
Accomplishments:
- Implemented a two-phase architecture (extract embeddings $\rightarrow$ evaluate fusions) reducing redundant tasks by 62% and UNI2 computation by 86%.
- Implemented dynamic metric overlays (ARI, NMI, Silhouette) on spatial clustering plots.
- Executed PCA/UNI/STAIG fusion benchmarks across multiple DLPFC slides.
- Structured a comprehensive 7-phase enhancement roadmap including normalization, fusion architectures, and Q-Former/LLaVA integration.
- Resolved spatial data issues and fixed GPU monitoring thread.
- Identified vision encoding bottlenecks for future optimization.
Blockers:
- β οΈ Inconsistent data integrity (missing coordinates/corrupted images) in multi-center datasets.
- β οΈ VRAM saturation during parallel multi-modal embedding extraction.
- β οΈ High computational demand for training-based fusion strategies.
ccusage CLI Development (1 days active) β β completed
Accomplishments:
- Implemented local JSON pricing loader to support GLM models and bypass Claude-only restrictions.
- Resolved type-checking and linting errors related to Result monad usage and process access.
Blockers:
- β οΈ None
Error Recovery Robotics Benchmark (3 days active) β π active
Accomplishments:
- Scaled scene generation from 3 to 118 samples.
- Fixed critical demo replay environment mismatch and force injection mechanics using HDF5 metadata.
- Implemented per-detector cooldown and PreGrasp detector logic.
- Architected VLM/rule-based error classification framework.
- Conducted implementation audit and identified unresolved API/mapping issues.
Blockers:
- β οΈ Unable to execute EGL/robosuite runtime dependencies in the current environment.
- β οΈ Injected forces are still partially masked by high-gain OSC controllers in visualizations.
Repository Documentation (1 days active) β β completed
Accomplishments:
- Created AGENTS.md contributor guide by synthesizing project artifacts (Makefile, pytest) in the absence of .git metadata.
Blockers:
- β οΈ Missing .git metadata for automated commit history extraction.
Gadget/Summarize Toolkit (3 days active) β π active
Accomplishments:
- Refactored to two-phase multi-device architecture (export $\rightarrow$ merge).
- Integrated rclone cloud sync and config management system.
- Implemented Claude CLI backend and token usage statistics.
- Established idempotent state tracking to prevent redundant API costs.
CalendarPro (3 days active) β π active
Accomplishments:
- Implemented recurring task auto-scheduling and Random Thoughts feature via JSONL persistence.
- Added batch event deletion with interactive Discord UI.
- Fixed Google Calendar 403 errors by pivoting OAuth scopes to settings() API.
- Hardened system with JSON/AI-provider error resilience.
Key Tasks
- β MIHD Two-Phase Pipeline & Metrics Implementation β Created 11 pipeline modules to separate embedding extraction from fusion evaluation, reducing UNI2 computation by 86%, and refactored visualization to inject real-time quantitative metrics (ARI, NMI) onto output PNGs.
- β Error Recovery Scale-up & Environment Fix β Expanded dataset to 118 samples and implemented HDF5 metadata loading to resolve controller configuration mismatches in simulation replays.
- β CalendarPro Intelligence & UX Overhaul β Modified routing logic to prefer LLM intent over keyword-based routers and implemented a robust recurring task system.
- β ccusage CLI Vendor Decoupling β Extended CLI to allow arbitrary custom pricing datasets via local JSON files, resolving vendor lock-in.
- β Gadget CLI Pipeline Hardening β Implemented crash-resilient atomic writes and idempotency markers to prevent redundant LLM consumption during multi-device merges.
Problems & Solutions
1. Redundant embedding computation in MIHD pipeline (O(N) scaling issue) and data integrity gaps. [MIHD] (2026-02-09)
Solution: Implemented a two-phase architecture with cached embedding layers and per-slide data availability checks to bypass corrupted sections.
2. GPU OOM failures during parallel multi-modal embedding extraction. [MIHD] (2026-02-09)
Solution: Switched to sequential execution of benchmarks to safely manage VRAM saturation on HPC environments.
3. Error recovery robot failing to reach objects due to environment config mismatch. [Error Recovery]
Solution: Loaded exact env_args from HDF5 metadata to match recording dynamics with replay.
4. MuJoCo injected forces being masked by high-gain OSC controllers. [Error Recovery]
Solution: Implemented a three-phase rollout: switch to neutral vectors during injection, hold force, then resume demo.
5. ccusage was restricted to Claude models due to hardcoded provider prefixes. [ccusage CLI Development] (2026-02-12)
Solution: Expanded filter predicates and implemented a dynamic local JSON loader for user-defined pricing rates.
6. CalendarPro 403 Forbidden errors on timezone retrieval. [CalendarPro]
Solution: Pivoted from calendarList() API to settings() API to use lower-permission OAuth scopes.
7. Automated documentation generation was blocked by the absence of accessible .git metadata. [Repository Documentation] (2026-02-10)
Solution: Pivoted to parsing static configuration files (Makefile, pytest, README) to deduce development conventions.
Learnings
Architecture (architecture)
- Two-phase pipelines (Extract/Cache $\rightarrow$ Process) are essential for high-dimensional experiment matrices to avoid $O(N^2)$ computation costs.
- Prioritizing local, user-provided configurations over external/hardcoded routing significantly improves reliability in air-gapped or restricted environments.
- Production AI systems require multi-layer fallback for JSON parsing (direct $\rightarrow$ code block extraction $\rightarrow$ regex recovery) to handle non-deterministic LLM outputs.
Domain Knowledge (domain)
- Spatial transcriptomics datasets are prone to inconsistent data integrity; pipelines must include per-section validity gates and smart caching.
- In high-gain robotics controllers (OSC), external force injection must be accompanied by a ’neutral phase’ to break the feedback loop and allow visible kinematic deviation.
Debugging (debugging)
- When all detectors/triggers fail, audit the underlying infrastructure (stubs, environment configs, or middleware) before adjusting detection thresholds.
Tools (tools)
- For multi-device workflows, use local state markers and atomic writes to ensure idempotency and prevent data corruption during cloud synchronization.
- AI-generated documentation is most effective when using specific structural outlines combined with the autonomy to scan real-world infrastructure signals (like Makefiles).
AI Usage Notes
Effective Patterns:
- β Using the AI to bypass library encapsulation (e.g., switching from Scanpy’s
save=to explicit matplotlib layouts for custom text overlays). - β Directing AI to prioritize existing local assets over searching for remote/hardcoded configurations.
- β Using parallel Explore agents for upfront codebase comprehension before architectural design.
- β Implementing ‘Plan Mode’ as a self-contained implementation manual rather than a task list.
Limitations:
- β Environmental state blindness (lack of awareness regarding HPC VRAM limits leading to OOM).
- β Limited visibility into hidden repository metadata (.git) and runtime dependencies (EGL/robosuite).
- β AI tends to react to symptoms (parameter tuning) rather than auditing infrastructure (stubs/configs).
- β Failure to recognize the need for ’neutral’ phases in physics-based error injection.
Next Week Outlook
Priorities for next week include: 1) Resolving runtime dependency blockers (EGL/robosuite) for the Error Recovery Benchmark and investigating force visibility persistence; 2) Executing the MIHD vision encoding optimization plan (caching and thread management) and addressing data-quality issues in failed DLPFC sections; 3) Finalizing CalendarPro’s intelligence layer (energy projection/learning); and 4) Completing the Error Recovery taxonomy implementation.
Token Usage Statistics
Peak Day: 2026-02-13 β $34.77 / 96.2M tokens
Daily Average: $11.37