Weekly Report — 2026-W23 (2026-06-01 ~ 2026-06-07)
This week was characterized by high-stakes emergency recovery and the establishment of rigorous engineering standards. Major achievements included the successful restoration of the MIHD research repository from data loss, the deployment of a stabilized AI Dev Companion CI suite, and the comprehensive onboarding of the Gadget project. A significant shift in development methodology was implemented, moving from direct code modification to a ’test-first’ verification framework for the RecoverBench project, ensuring scientific and technical integrity through automated adversarial validation and strict acceptance standards.
Weekly Overview
| Metric | Value |
|---|---|
| Date Range | 2026-06-01 ~ 2026-06-07 |
| Active Days | 6 / 7 |
| Total Conversations | 17 |
| Projects | 13 |
| Tasks Completed | 27 |
| Tasks In Progress | 1 |
| Total Tokens | 833,682,848 |
| Total Cost | $962.51 |
| Claude Code Token | 832,338,900 |
| Claude Code Cost | $960.36 |
| Codex Token | 1,343,948 |
| Codex Cost | $2.15 |
| Daily Average Cost | $137.50 |
Project Progress
MIHD Research Recovery (4 days active) — ✅ completed
Accomplishments:
- Recovered two months of uncommitted research data via NetApp snapshots
- Validated StaigFusionTrainer equivalence through seeded ARI/NMI metric comparisons
- Fixed critical STAIG fusion silent failures and GPU scheduling starvation issues
RecoverBench / Error Recovery Benchmark (3 days active) — 🔄 active
Accomplishments:
- Conducted a multi-source audit to reconcile paper, code, and dataset discrepancies
- Designed a three-layer ‘Test-First’ execution plan and 5Q acceptance standards
- Implemented automated adversarial verification for documentation integrity
Gadget Project (2 days active) — 🔄 active
Accomplishments:
- Completed full-scale codebase onboarding and risk-tiered refactoring planning
- Generated a 667-item refactoring inventory
- Applied verified code cleanups in parsers.py
TokenMonitor / TzJ2006 Fork (3 days active) — 🔄 active
Accomplishments:
- Resolved auto-update signing and macOS compatibility issues
- Implemented a cross-platform single-instance guard via loopback port locking
- Optimized repository size by 99% by purging build artifacts
AI Dev Companion (2 days active) — 🔄 active
Accomplishments:
- Stabilized CI pipeline by fixing tsconfig errors and stale tests
- Designed ‘Vendor-Tier-1’ portable architecture to eliminate absolute path dependencies
- Delivered the /ccoverview bilingual skill
Key Tasks
- ✅ MIHD Repository Recovery and Data Integrity Fix — Recovered MIHD repo from accidental deletion and resolved silent data corruption in STAIG fusion via md5 validation and SLURM reruns.
- ✅ Git History Rewrite Optimization — Reclaimed ~302MB by removing build artifacts from TokenMonitor using targeted amending to maintain upstream synchronization.
- ✅ Claude Code Cloud Provider Migration — Migrated Claude Code infrastructure to AWS Platform with SigV4 auth for lower latency and direct billing.
- ✅ AI Dev Companion CI Stabilization — Fixed root tsconfig build errors and repaired 92 stale tests to restore CI/CD stability.
- ✅ TokenMonitor Fork Auto-Update Pipeline Fix — Resolved discovery failures by generating new Tauri keypairs and decoupling minisign from Apple certs.
- ✅ Gadget Project Onboarding — Executed full-scale codebase onboarding, producing a 667-item risk-tiered refactoring plan.
- ✅ Test-First Verification Methodology — Established a 3-layer testing strategy and 5Q acceptance standards for the RecoverBench project.
- ✅ RecoverBench Multi-Source Audit — Reconciled discrepancies between paper metrics, code behavior, and remote dev environments.
Problems & Solutions
1. MIHD STAIG fusion produced byte-identical results due to bypassing gene encoder layers. [MIHD]
Solution: Modified pipeline_config.yaml to enforce encoder usage and verified via md5sums.
2. TokenMonitor auto-update failed on macOS due to conflated Apple signing and minisign requirements. [TokenMonitor]
Solution: Decoupled signing logic in workflow.mjs to allow minisign updates without Apple Developer IDs.
3. AI Companion builds failed due to monorepo module resolution conflicts. [AI Dev Companion]
Solution: Set root tsconfig ‘include’ to [] to prevent cascading build failures.
4. Accidental commit of 302MB build artifacts threatened upstream sync for TokenMonitor. [TokenMonitor]
Solution: Used targeted ‘git commit –amend’ on the tip to preserve ancestor integrity and sync status.
5. Discrepancy between RecoverBench paper metrics (48.6%) and actual checkpoint scores (45.7%). [RecoverBench]
Solution: Conducted parallel audit to identify the discrepancy as a cherry-picked aggregate and updated documentation to match reality.
Learnings
Architecture (architecture)
- Root tsconfigs in monorepos should be pure references to avoid module resolution conflicts. Multi-repo CLI tools must normalize paths and check target state, not just file presence, for idempotency.
Debugging (debugging)
- Empirical binary verification (md5sums) is essential in scientific pipelines to detect silent failures where downstream modules mask upstream encoder errors. In HPC, hardware type availability (e.g., GPU variants) can cause job stalls more than partition priority.
Tools (tools)
- When cleaning forked repositories, selective amending of the tip commit is safer for preserving upstream relationships than broad history filtering. Single-instance app logic must be synchronous and independent of the event loop.
Domain Knowledge (domain)
- Scientific model equivalence should be validated via metric divergence (ARI/NMI) rather than bit-identical reproducibility due to inherent RNG variance.
AI Usage Notes
Effective Patterns:
- ✓ Phased onboarding (e.g., /cconboard) for large Python repositories
- ✓ Using structured 5Q (What, Why, How, Expected, Verify) frameworks to drive documentation quality
- ✓ Automated adversarial verification of documentation against the codebase
Limitations:
- ✗ API rate limiting during large-scale parallel agent workflows
- ✗ Initial misdiagnosis of environmental errors (signing vs. port conflicts) due to context bias
- ✗ Failure to predict service-side 529 overloading
Next Week Outlook
Priorities for next week include executing the ‘Phase 5’ implementation for the Gadget project according to the risk-tiered plan, transitioning the RecoverBench project from the verification phase to active code alignment using the newly established L1/L2/L3 test hierarchy, and resolving the blocked Error Recovery Benchmark analysis once API stability is restored.
Token Usage Statistics
Peak Day: 2026-06-02 — $506.66 / 439.1M tokens
Daily Average: $137.50