AI COMPANION / BUGJOURNAL
Completed MIHD multimodal fusion research and benchmarking with critical Flow Matching analysis, operationalized the Error Recovery Benchmark through VLA policy server integration and natural error capture pipelines, resolved cross-framework API mismatches for Pi0 rollouts, and expanded the daily summarization CLI infrastructure.
Executed comprehensive infrastructure upgrades for multi-device Gadget pipeline orchestration and Hugo static site deployment, scaled the Error Recovery robotics benchmark to v4.2 with 118 scenes, advanced MIHD spatial omics fusion benchmarks across HPC clusters, and resolved critical MuJoCo control-loop physics visualization artifacts.
Completed major architectural refactoring of MIHD benchmarking pipeline (two-phase extraction-evaluation architecture), fixed critical error recovery benchmark bugs (force injection mechanism, detector triggering, per-detector cooldown), enhanced CalendarPro with batch delete and Google Calendar permission fixes, and upgraded gadget/summarize tool with config system, rclone cloud sync, and multi-API backend support
Comprehensive multi-project development day: fixed MIHD spatial transcriptomics data issues and designed 7-phase enhancement plan; refactored gadget daily report tool to two-phase multi-device architecture and established Git repository; implemented error recovery benchmark validators and fixed critical demo replay environment mismatch; built CalendarPro recurring task auto-scheduling, fixed production bugs, and implemented P0/P1 roadmap with Random Thoughts feature
Strategically structured the MIHD multimodal fusion roadmap and successfully extended the ccusage CLI to support localized GLM model pricing calculations.
Executed spatial transcriptomics fusion benchmarking and generated repository contributor guidelines while auditing the current implementation status and blockers for the Error Recovery Benchmark project.
Enhanced MIHD visualization pipelines with dynamic metric overlays and executed large-scale benchmarking across multiple DLPFC spatial transcriptomics slides.
Integrated ground-truth RM-Ideal spatial niche comparison into QueST’s native code and developed a new MIHD framework to benchmark embedding accuracy against this structural metric.
Consolidated spatial transcriptomics project repositories and reverse-engineered complex mathematical metrics while rapidly delivering a functional automated error recovery benchmark MVP, resolving critical environment mismatches, physics simulation backends, and cross-cluster network routing limitations.
This day spanned forensic alignment debugging and runtime optimization for the MIHD spatial benchmark pipeline, alongside the construction of a secure, consent-driven automated reporting and public submission infrastructure.