Weekly Report — 2026-W13 (2026-03-23 ~ 2026-03-29)

This week, approximately 10 projects were advanced in parallel across three devices (TzJsDesktop / tianhe / DCC). Core achievements: gadget’s summarize (2930 lines → 8 modules + 72 tests) and research_scout (2934 lines → 7 sub-packages) both completed systematic refactoring, with a new natural-language paper search ask command added; TokenMonitor evolved from a macOS-exclusive tool into a cross-platform multi-device SSH cost tracking platform (including Windows-native UX, floating ball, ccusage integration, LiteLLM dynamic pricing, comprehensive security hardening, and multiple successful MSI/NSIS installer builds); Error Recovery Benchmark completed Pipeline 2 full end-to-end design and implementation plus Context Replay architecture refactoring (163 tests all passing); ccplan / cchypothesis / optimize and other Claude Code toolchain components received systematic upgrades. On the robotics research front: Pi0.5 full-task rollout evaluation was completed (revealing extreme divergence: Stack 96% vs PickPlace 6%), BOSS benchmark was engineered into production, and openvla-oft training scripts were created. MIHD spatial transcriptomics completed QueST protocol alignment and an 8-encoder benchmark framework was set up.

Weekly Overview

Metric Value
Date Range 2026-03-23 ~ 2026-03-29
Active Days 6 / 7
Total Conversations 40
Projects Involved 27
Completed Tasks 65
In-Progress Tasks 6
Total Tokens 639,747,276
Total Cost $439.02
Claude Code Tokens 599,935,711
Claude Code Cost $413.30
Codex Tokens 39,811,565
Codex Cost $25.72
Daily Average Cost $62.72

Project Progress

TokenMonitor (Desktop App) (7 days active) — 🔄 active

Completed:

  • Completed Phase E cross-platform migration, removed all macOS-only dependencies, produced the first distributable Windows NSIS/MSI installer
  • Implemented the full floating ball lifecycle (four-edge snapping, drag/click disambiguation, capsule UI, Win32 shape clipping)
  • Implemented Windows-native UX (taskbar embedding, transparent rounded corners, dynamic positioning above the system tray)
  • Implemented SSH multi-device cost tracking (ssh_config parsing, remote jq/python3/grep three-tier preprocessing, 500MB→5MB)
  • Integrated ccusage CLI (with per-view fallback) and LiteLLM dynamic pricing (2598 models, 24h cache)
  • Completed large-scale refactoring of commands.rs (2466→7 modules) and rate_limits.rs (1202→5 modules)
  • Fixed SSH sync 0-record infinite loop (format! line continuation breaking Python indentation + conditional timestamp update)
  • Fixed Dashboard 1-2Hz jitter (four-layer defense breaking the ResizeObserver↔setSize positive feedback loop)
  • Fixed chart Tooltip layout jitter (permanently reserved fixed height + fixed-height carousel panel)
  • Fixed window bottom edge jumping (position:fixed Footer + JS pre-set minHeight + removed dynamic anchor detection)
  • 5 parallel specialized Agent security audits; fixed all security issues including SSH alias injection and path traversal
  • 229 Rust + 191 frontend tests all passing, clippy zero warnings

Blockers:

  • ⚠️ Frontend glass cleanup (Phase E-3+E-9) not yet completed
  • ⚠️ Multi-device UI architecture P1-P3 layers (main interface collapse area / chart switching / single-device detail page) not yet implemented

Claude Code Toolchain (ccplan / cchypothesis / skills) (6 days active) — 🔄 active

Completed:

  • ccplan: Added Phase 0 five-step Prompt Calibration, multi-intent decomposition (coupled/related/independent), Phase 4-6 minimum discovery threshold max(3,N/2), Feature Guard Protocol, WebSearch stream interruption fix
  • cchypothesis: Designed a 6-phase hypothesis-driven debugging skill through ccplan’s full 9-phase process, then integrated an intelligent dual-track instrumentation architecture (static parallel + serial instrumentation upgrade path + Git Safety Checkpoint), validated by critic agent with 11 adversarial questions
  • optimize skill expanded to a Python/Swift/Rust/TypeScript four-language hub+spoke architecture
  • code-summarize added --for audience parameter (self/coworker/user/display weight matrix)
  • Created slurm-gpu skill (parses sinfo/squeue/scontrol, dual-layer GPU availability output)
  • Global skill library reorganized: deleted 36 irrelevant skills, moved to project-level by proximity principle

BOSS Benchmark (Robotics Evaluation) (6 days active) — 🔄 active

Completed:

  • Completed Git repository migration (YY-GX/BOSS → Junye-Chen/boss), configured proxy to bypass cluster restrictions
  • Completed zero-configuration migration to openpi LIBERO environment (module injection to register BENCHMARK_MAPPING)
  • Created eval_oss_ch.py (modified environment evaluation) and eval_skill_chain.py (skill chain evaluation) as two server-client evaluation scripts
  • Fixed 5 missing object assets (corn/egg/lemon/onion/potato), confirmed 7 LIVING_ROOM tasks at 0% success rate are an intentional zero-shot generalization test
  • Unified success rate logging and JSON result saving logic across three evaluation scripts (no longer dependent on --save_stats flag)
  • Created CLAUDE.md documentation, completed full training-evaluation pipeline engineering

Error Recovery Benchmark (5 days active) — 🔄 active

Completed:

  • Completed Pipeline 2 full end-to-end implementation: target_object threading through the data flow, Phase×Object three-dimensional uniform sampling (bucketing + backflow), D0/D1 stratified MimicGen augmentation, 163 unit tests all passing, GPU smoke test confirmed
  • E4 merged into E3 architecture refactoring, taxonomy simplified from 13 skills/26 subtypes to 12 skills/24 subtypes, 136 tests all passing
  • Context Replay comprehensive refactoring: removed observations dead code, corrected policy_adapter timing (moved to post-injection after environment stabilization), renamed render_window (corrected erroneous VLA context window narrative), batch cleanup of 22 locations across 7 files
  • Extracted 6 shared helpers into BaseErrorSkill, eliminated ~60 lines of duplicate code, fixed bare except / hot-path import and other security issues
  • macOS collection package compressed from 952MB to 1.1MB

Blockers:

  • ⚠️ set_sim_state_flat replacement for frame-by-frame replay planned but code changes not yet executed
  • ⚠️ Pipeline 2 data generation and actual training-evaluation closed-loop verification still pending

gadget (summarize / research / tools) (5 days active) — 🔄 active

Completed:

  • summarize module refactoring: daily_summary.py split from 2930 lines into 8 modules (config/remote/parsers/usage/summarizer/formatter/daily/cli), 72 tests all passing, backward-compatible shim retained
  • research_scout.py modular refactoring: 2934 lines → scout/ sub-package 7 modules, research_scout.py reduced to ~80-line thin shim, mcp_server.py zero changes
  • Added ask command (parse_ask_intent / validate_ask_plan / route_search), supporting natural-language paper search and fixing 6 runtime bugs (arXiv retry, conference token-level flexible matching, orphan directory cleanup, etc.)
  • Fixed --sync-all subprocess ModuleNotFoundError (python daily.py → python -m summarize.cli)
  • summarize skill upgraded to essay-style six-chapter format, added /code-summarize command

Robotics Learning Research (openvla-oft / openpi / LiPM) (3 days active) — 🔄 active

Completed:

  • Pi0.5 merged-LoRA D0/D1 full-task rollout evaluation completed (10 tasks, 8×A800 parallel), revealing extreme performance divergence: Stack 96-98% vs PickPlace 6%
  • Deep comparison of openvla vs openvla-oft finetune.py (action representation, FiLM/proprioception/Action Chunking, data interface differences), created complete training script run_openvla_oft.sh
  • Completed OpenPI evaluation client adaptation (WebsocketClientPolicy, image preprocessing, state vector, action chunking)
  • Fixed lerobot2rlds.py field filtering logic (joint_state field omission), added --max-episodes parameter
  • LiPM trainer.py review discovered 5 logic bugs (duplicate GPU transfer, variable name errors, backbone.eval() override, etc.)

Blockers:

  • ⚠️ Pi0.5 training interrupted by Slurm time limit at 25000 steps; success rate on fine-grained tasks (PickPlace/Threading) is extremely low, requiring more training steps

MIHD Spatial Transcriptomics (DCC) (1 day active) — 🔄 active

Completed:

  • Completed QueST cross-sample query protocol gap analysis (4 gaps: query granularity / candidate representation / niche type / evaluation metrics) and aligned implementation (K-hop mean-pool, boundary niche 7 types, NCJS metric)
  • Built 8-gene-encoder benchmark framework (Cache-First architecture), completed 4/8 encoders (HVG1500 ARI=0.3300 best, outperforming all tested foundation models)

Blockers:

  • ⚠️ UCE blocked by Figshare download failure (requires proxy)
  • ⚠️ TEDDY/Geneformer/scGPT-spatial environment installation or OOM issues pending resolution

LifeCopilot / openclaw Integration (1 day active) — ⏸️ paused

Completed:

  • Completed full Chinese documentation of the LifeCopilot codebase (OVERVIEW.md, 4 parallel Agents), and discovered systematic bias in AI-generated statistics through a verification Agent
  • Established the integration direction of building LifeCopilot as a plugin on top of openclaw’s multi-channel architecture

Blockers:

  • ⚠️ Security design (multi-channel exposure / prompt injection protection) not yet completed; session interrupted before key decisions

Key Tasks

  • ✅ gadget summarize module refactoring (2930 lines → 8 modules + 72 tests) (2026-03-24) — Split daily_summary.py into 8 modules; first wrote 47 import smoke tests to establish a safety net; eliminated three sys.path.insert hacks; retained backward-compatible shim; synchronously updated three external consumer import chains.
  • ✅ Error Recovery Benchmark Pipeline 2 full end-to-end design and implementation (2026-03-29) — brainstorming→spec→subagent-driven-development workflow; target_object threading through the data flow; three-dimensional uniform sampling bucketing; D0/D1 stratified MimicGen augmentation; 163 tests all passing; GPU smoke test confirmed.
  • ✅ gadget research ask command full implementation (2026-03-29) — Implemented after ccplan 9-dimensional intent extraction + Critic identified 12 potential issues. Fixed 6 runtime bugs: arXiv exponential backoff retry, conference token-level bidirectional subset matching, orphan directory cleanup, module import path correction.
  • ✅ TokenMonitor SSH sync ‘always up to date’ root cause fix (2026-03-29) — Root cause: Rust format! line continuation deleted Python script indentation, IndentationError was silently swallowed by 2>/dev/null, returning 0 records while timestamp was still written, forming an infinite loop. Fixed with concat! macro replacement + conditional timestamp update.
  • ✅ ccplan skill multi-round systematic upgrade (2026-03-24) — Added Phase 0 Prompt Calibration, multi-intent decomposition (coupled/related/independent parallel tracks), Phase 4-6 quantitative threshold max(3,N/2), Feature Guard Protocol, WebSearch stream interruption fix (Tool Invocation State Preservation).
  • ✅ research_scout.py modular refactoring (2934 lines → 7 sub-packages) (2026-03-25) — Split into scout/ sub-package; research_scout.py reduced to ~80-line thin shim; added SSRF protection and config value externalization; mcp_server.py zero changes; all validations passed.
  • 🔄 TokenMonitor cross-platform migration and first Windows installer (2026-03-25) — Removed all objc2/macos-private-api dependencies; three-platform matrix build; produced TokenMonitor_0.5.0_x64-setup.exe (NSIS 3.2MB). Frontend glass cleanup still pending.
  • ✅ cchypothesis hypothesis-driven debugging skill design and implementation (2026-03-27) — ccplan full 9-phase process designed 6-phase workflow, then integrated intelligent dual-track architecture (static parallel + serial instrumentation upgrade path + Git Safety Checkpoint), validated by critic agent with 11 adversarial questions, +395/-70 lines.
  • ✅ TokenMonitor comprehensive performance optimization and security hardening (2026-03-29) — 8 performance optimizations (normalize_model normalization, merge_payloads mem::take, static lookup table replacing 47-branch if chain, etc.). 5 parallel specialized Agent security audits; fixed all security issues including SSH alias injection and path traversal. 229+191 tests all passing.
  • ✅ Pi0.5 LoRA D0/D1 full-task rollout evaluation (2026-03-26) — 8×A800 parallel completed 50 trials each for 10 tasks. D0: Stack 96%, StackThree 78%, PickPlace 6%; D1: Stack 98%, StackThree 58%, PickPlace not tested. Revealed that fine-grained tasks are highly sensitive to training steps.
  • ✅ Context Replay logic fix and VLA narrative cleanup (2026-03-28) — Removed observations dead code; corrected policy_adapter timing (moved to post-injection); renamed render_window to correct erroneous narrative; batch cleanup of 22 locations across 7 files; grep verification 0 residuals; 139 tests passing.
  • ✅ TokenMonitor Dashboard 1-2Hz vertical jitter fix (2026-03-28) — Four concurrent fixes to break the ResizeObserver↔setSize positive feedback loop: RESIZE_SETTLE_DELAY 16→100ms, shallowPayloadEqual, resize throttle (500ms/3 times), is_active 2-minute grace period.
  • ✅ Error Recovery Benchmark E4 merged into E3 architecture refactoring (2026-03-29) — E4 drop_with_interaction merged as E3 dual-mode skill; taxonomy simplified from 13/26 to 12/24. User chose 2 subtypes (D0/D1) rather than AI-suggested 4. 136 tests all passing; OVERVIEW.md synchronously updated.
  • ✅ MIHD QueST cross-sample query protocol alignment implementation (2026-03-26) — Identified 4 query protocol gaps; created niche_utils.py (K-hop mean-pool, boundary niche 7 types, NCJS); added --quest_style benchmark extended mode; original mode backward compatible.
  • ✅ TokenMonitor SSH multi-device cost tracking feature (2026-03-29) — ssh_config parsing, SSH remote discovery and transfer, local cache management, Settings SSH management UI, Devices Tab, background sync scheduling. Remote preprocessing reduced data from 500MB to 5MB; added Sync Now button state feedback.
  • ✅ openvla-oft training code deep comparison and script creation (2026-03-25) — Deep comparison of action representation (discrete tokens vs L1/Diffusion), FiLM/Proprio/Action Chunking, data interface differences; created run_openvla_oft.sh (torchrun, L1 regression, dual-image input, proprioception, 150K steps).
  • ✅ TokenMonitor chart Tooltip layout jitter root cause fix (2026-03-29) — After 4 rounds of solution iteration, switched to permanently reserving a fixed-height detail panel; hover only updates content; leave retains last data; completely eliminates height animation and window resize. Panel changed to fixed-height carousel (3 models/page, scroll to switch).
  • 🔄 LifeCopilot and openclaw integration architecture direction established (2026-03-29) — Established the direction of building LifeCopilot as a plugin on top of openclaw’s multi-channel architecture (human proactively reversed integration direction). Security design (multi-channel exposure, prompt injection protection) not yet completed; session interrupted before key decisions.

Problems & Solutions

1. daily_summary.py too large (2930 lines), zero test coverage; Critic review discovered mcp_server.py import breakage risk (CRITICAL) [gadget] (2026-03-24)

Solution: First wrote 47 import smoke tests to establish a safety net; then split into 8 modules by functional block; replaced sys.path.insert with relative imports; retained backward-compatible shim; synchronously updated three external consumers.

2. ccplan workflow terminates prematurely at Phase boundaries, 9/10 Phases missing multi-turn protocol [Claude Code Toolchain] (2026-03-24)

Solution: Added CONTINUOUS EXECUTION MANDATE global constraint at the top of SKILL.md; added →NEXT: forced transition directive at the end of each Phase (10/10 full coverage); filled in missing multi-turn protocols.

3. After research_scout.py split, mcp_server.py directly importing 15 functions faces breakage risk [gadget] (2026-03-25)

Solution: Reduced research_scout.py to ~80-line thin shim; guaranteed mcp_server.py zero changes through re-export.

4. TokenMonitor SSH sync returns 0 records for all hosts, showing ‘Already up to date’ forming an unrecoverable infinite loop [TokenMonitor] (2026-03-29)

Solution: Root cause was Rust format! line continuation deleting Python indentation producing IndentationError silently swallowed by 2>/dev/null. Fixed with concat! macro; set_last_sync only writes timestamp when >=1 records; deleted stale .last-sync files.

5. Tauri v2 capability whitelist caused floating ball outerPosition()/scaleFactor() calls to silently fail, drag completely non-functional [TokenMonitor] (2026-03-26)

Solution: Added three missing permissions to capabilities/default.json (allow-outer-position/allow-scale-factor/allow-current-monitor), and added float-ball to the windows array.

6. TokenMonitor Dashboard continuously vertically jittering at 1-2Hz (multiple positive feedback loops stacked) [TokenMonitor] (2026-03-28)

Solution: Four concurrent fixes: RESIZE_SETTLE_DELAY 16→100ms, shallowPayloadEqual to skip meaningless updates, resize throttle (500ms/3 times), is_active 2-minute grace period to eliminate time-boundary oscillation.

7. Chart Tooltip appearance/disappearance triggers detail panel height change → ResizeObserver→SetWindowPos, bottom content jumping (4 rounds of solutions all ineffective) [TokenMonitor] (2026-03-29)

Solution: Abandoned dynamic height scheme; switched to permanently reserving a fixed-height detail panel; hover only updates content; leave retains last data; completely eliminates height change as the root cause.

Solution: Changed Footer to position:fixed;bottom:0 to anchor directly to viewport bottom, completely bypassing CSS layout frame latency; background container uses JS to synchronously preset style.minHeight; removed SWP_NOCOPYBITS to prevent WebView2 full-frame redraws.

9. Context Replay contained fundamental VLA conceptual error (mistakenly believed VLA needs to replay N prior frames to establish context window), policy_adapter feed timing was wrong (during clean frame phase rather than after post-injection) [Error Recovery Benchmark] (2026-03-28)

Solution: Renamed render_window to correct the naming narrative; moved policy_adapter call to Step F (after error injection and environment stabilization); batch cleanup of 22 erroneous narrative locations across 7 files.

10. BOSS evaluation script reported KeyError: ‘potato’; 7 LIVING_ROOM tasks at 0% success rate, mistakenly interpreted as model generalization failure [BOSS Benchmark] (2026-03-28)

Solution: Copied 5 missing object assets from the BOSS repository and registered them; reviewed form_boss_44_dataset.py confirming allowed_files whitelist only contains KITCHEN_SCENE; 7 LIVING_ROOM tasks are intentionally designed zero-shot generalization tests.

11. Rust commands.rs (2222 lines) and new commands/ subdirectory coexisting caused 42 compilation errors (module path ambiguity) [TokenMonitor] (2026-03-28)

Solution: Rewrote old commands.rs as ~80-line thin module root; correctly declared 6 sub-modules; deleted all duplicate functions; eliminated coexistence ambiguity.

12. arXiv conference search returned 0 results (API rate limiting with no retry, query too complex, conference name exact matching failed) [gadget] (2026-03-29)

Solution: Added exponential backoff retry (5/10/20 seconds); conference mode query uses only conference name; implemented token-level bidirectional subset matching (A⊆B or B⊆A both count as match).

13. guard-check.py had shell injection (YAML command passed directly to shell=True) and bare except swallowing all exceptions [Claude Code Toolchain] (2026-03-26)

Solution: Interactive [y/N] confirmation before execution; only catch expected JSONDecodeError; other exceptions written to stderr to retain visibility.

14. MimicGen augmentation in multi-object tasks warped the wrong object (next(iter(…)) randomly selected the first one) [Error Recovery Benchmark] (2026-03-29)

Solution: Threaded ErrorSpec.target_object through to RecoveryAugmenter to precisely locate warping anchor; D0 uses object-centric transform; D1 adds subtask-aware segmented transformation.

Lessons Learned

Architecture

  • There is an inherent 1-frame latency between Win32 SetWindowPos and browser CSS re-layout — this is a core constraint of Tauri desktop applications. Solution: anchor bottom UI with position:fixed to bypass CSS layout; background container uses JS to synchronously preset style.minHeight; avoid SWP_NOCOPYBITS to prevent WebView2 full-frame redraws.
  • When solving layout jitter, the priority question should be “can we eliminate the root cause of this change?” rather than “how can we more precisely synchronize two async systems?” Permanently reserving a fixed-height panel is more stable than dynamic expand/collapse; fixed-height viewport + internal carousel is a general pattern for scenarios where information volume varies but display space must be fixed.
  • ECL (Evolving Constraint Language) documents are an effective mechanism for solving context rot in complex multi-session projects: externalizing architectural decisions, adversarial review results, and current execution state to YAML files allows any subsequent Agent to continue from there, preventing tool call interruptions from losing workflow context.
  • Tauri v2 capabilities are whitelist-based: any window API (including basic outerPosition/scaleFactor) must be explicitly declared in the capabilities JSON; in multi-window applications each WebviewWindow is configured independently; silent failure with no error messages is the hardest type of problem to debug.
  • Correct architecture for instrumentation debugging: static parallel analysis as the main path; instrumented probing as the upgrade path for inconclusive results; each hypothesis independently completes the instrument→run→analyze→cleanup cycle; Git Safety Checkpoint at the entry point protects the user’s work; prefer git restore . over git stash to avoid stacking conflicts.
  • BOSS benchmark design mechanism: boss_44 intentionally covers 37 KITCHEN tasks via allowed_files whitelist; 7 LIVING_ROOM tasks are intentionally designed zero-shot generalization evaluations (OSS paradigm); 0% success rate is expected behavior, not model failure.
  • Safe order for Rust incremental module refactoring: first create the new file structure and have the old entry re-export; verify compilation passes; then as the final step replace/delete the old entry. commands.rs and commands/mod.rs coexisting causes module path ambiguity; the old file prevents new sub-modules from being recognized.

Debugging

  • Rust format! line continuation deletes leading spaces from the next line, breaking indentation-sensitive scripts (Python/Shell). Use concat! macro or r#""# raw string concatenation for embedded scripts. Also: 2>/dev/null silently swallows errors — remove it first when debugging; state updates (e.g., timestamps) must only execute after confirming the operation truly succeeded.
  • Jitter bugs from multiple stacked positive feedback loops must simultaneously break all loops (ResizeObserver↔setSize loop requires simultaneously adding: measurement delay, equality check, throttle, data boundary grace period); any single fix can only weaken, not eliminate.
  • Before refactoring large files, first write all external import contracts as tests (smoke tests) to establish a safety net; immediately verify backward compatibility after refactoring. An adversarial Critic discovering CRITICAL issues during the planning phase costs an order of magnitude less than fixing them after implementation.
  • AI-generated codebase documentation has systematic bias in quantitative statistics (services undercounted by 30%, timer intervals off by multiples, AI provider chains missing more than half); must be corrected through an independent verification step (parallel multi-Agent can be used). Quantitative statistics cannot be trusted directly.

Domain Knowledge

  • VLA (Vision-Language-Action) is open-loop inference: each step receives a single-frame observation and outputs an action; it does not maintain recurrent hidden state and fundamentally does not need to “replay N prior frames to establish a context window” — this assumption is a fundamental misunderstanding of how VLA works.
  • Pi0.5 LoRA fine-tuning shows extreme task performance variance: simple stacking tasks (Stack 96-98%) vs fine-grained manipulation tasks (PickPlace 6%); D1 difficulty is not always higher than D0 (Coffee D1 26% > D0 16%); initial state distribution impacts success rate more than the task itself. Fine-grained tasks are highly sensitive to training steps.
  • HVG1500 raw features (ARI=0.3300) outperform all tested foundation models (scGPT_original 0.1934, scGPT-spatial 0.1510), suggesting that in spatial transcriptomics clustering tasks, complex foundation models are not necessarily superior to simple statistical features — an important finding worth deeper investigation.

Tools

  • ccplan quantitative constraints (at least max(3,N/2) findings) are superior to qualitative descriptions (“analyze carefully”) — AI will find ways to skip qualitative requirements, while quantitative thresholds are hard to bypass. Skill Phase boundaries must have explicit →NEXT: forced transition directives; otherwise AI will “politely stop” at Phase boundaries.
  • High-latency SSH links should pre-filter/compress data on the remote side (jq→python3→grep three-tier fallback strategy ensures cross-platform compatibility) before transmission, reducing data volume 50-100x from 500MB to 5MB. SSH commands should use -o LogLevel=ERROR to control stderr output level and prevent warnings from causing false negatives.
  • arXiv conference search two-step method: broad query (conference name only) to obtain candidates → comment/journal_ref fields use token-level bidirectional subset matching to filter (A⊆B or B⊆A both count as match). LLM-generated entity names require flexible matching; token subsets are more robust than full string comparison.
  • Hub+spoke architecture is suitable for multi-language prompt skill design: hub maintains the common framework (≤140 lines), spokes focus on language-specific checks (≤80 lines); physical file separation prevents attention dilution better than section separation when Claude processes a single language.
  • gym-style evaluation frameworks should reuse env across same-task multi-trial runs (env.reset() rather than rebuilding); MuJoCo initialization can bring a 20x performance difference (880 → 44 times). This optimization pattern generalizes to all gym-style evaluation scripts.
  • Cache-First Integration is an effective design pattern for handling multi-dependency conflicts: each encoder runs in an isolated conda environment and outputs a standard .npz cache; the downstream pipeline does not need to be aware of each model’s environment differences, achieving complete decoupling.

AI Usage Notes

Effective Patterns:

  • ✓ Parallel multi-Agent (Critic/Red Team/Feasibility/Explore) systematically discovering critical constraints humans miss (MCP import breakage, Windows tray size limits, prompt dilution, resizeDebug 100+ call depth)
  • ✓ ccplan 9-dimensional intent extraction framework: refines vague requirements ~3x; adversarial review identifies CRITICAL-level risks before implementation
  • ✓ 5 parallel specialized Agent security audits: upgraded from binary yes/no security judgment to an actionable tiered improvement roadmap
  • ✓ subagent-driven-development workflow: brainstorming→spec→parallel implementation driving complex multi-module tasks like Pipeline 2
  • ✓ ECL document cross-session persistence: large multi-session projects (TokenMonitor cross-platform migration) maintain architectural decision context through ECL
  • ✓ cchypothesis hypothesis-driven debugging: converts intuitive guesses into falsifiable hypotheses for parallel investigation, effectively shortening debugging cycles

Limitations:

  • ✗ Tauri native window frame-level visual defects (frame latency, transparent gaps) exceed the detection capability of static code analysis; require manual visual verification; TokenMonitor window bottom edge jitter took 5 rounds of iteration to ultimately resolve
  • ✗ Missing domain prior knowledge: VLA open-loop inference mechanism, Pi0.5 task selection (stack as baseline), correct policy_adapter timing — all required user correction; AI tends to trust existing code comments rather than actively questioning them
  • ✗ Insufficient quantitative statistics global consistency verification: generating codebase OVERVIEW produced systematically biased statistics (service counts/timer intervals/AI provider chains); design documents retained old incorrect numbers (13/26 rather than 12/24)
  • ✗ Planning document status:verified does not equal code implemented: ccusage was marked verified but code was not integrated; required user follow-up to reveal
  • ✗ Layout problem root cause judgment bias: when facing jitter bugs, repeatedly attempted “coordinate two async systems” direction; required user to explicitly enforce strong constraints before pivoting to the correct direction (eliminate root cause of the change)
  • ✗ Insufficient secure code generation: guard-check.py was generated without proactively considering shell injection risks; only discovered after a specialized security review agent

Next Week Outlook

Core tasks for next week: ① TokenMonitor — complete frontend glass cleanup (Phase E-3+E-9) and multi-device UI architecture P1-P3 (main interface collapse area, chart mode switching, single-device detail page), advance toward official release; ② Error Recovery Benchmark — execute Pipeline 2 data generation (D0/D1 MimicGen augmentation), verify training-evaluation closed-loop, building the data foundation for the upcoming paper; ③ MIHD benchmark — complete the remaining 4 encoders (UCE requires solving the Figshare download issue, TEDDY/Geneformer need environment installation/OOM issues resolved), produce complete 8-encoder ARI/NMI comparison data; ④ LifeCopilot/openclaw integration security design (multi-channel exposure protection, prompt injection protection), advance the integration prototype; ⑤ BOSS Pi0.5 longer training (PickPlace/Threading fine-grained tasks undertrained at 25000 steps, more steps needed for validation). For gadget, continue operating the paper search pipeline and track follow-up progress on previously saved high-relevance papers.

Token Usage Statistics

Daily Cost Trend

Date Tokens (millions) Cost ($)
2026-03-24 72.3 57.99
2026-03-25 86.4 66.62
2026-03-26 191.6 126.04
2026-03-27 40.2 25.22
2026-03-28 69.7 46.39
2026-03-29 107.9 66.80
unknown 71.6 49.96

Peak Day: 2026-03-26 — $126.04 / 191.6M tokens

Claude Code

Metric Value
Total Tokens 599,935,711
Input Tokens 561,006
Output Tokens 1,391,987
Cache Creation 26,181,655
Cache Read 571,801,063
Total Cost $413.30

Model Usage Distribution

Model Cost ($) Input Tokens Output Tokens
claude-opus-4-6 392.44 248,195 926,865
claude-haiku-4-5-20251001 17.97 290,227 449,832
claude-sonnet-4-6 2.89 3,430 13,042
glm-4.7 0.00 19,154 2,248

Codex

Metric Value
Total Tokens 39,811,565
Input Tokens 39,459,933
Output Tokens 351,632
Reasoning Tokens 202,151
Cache Read 34,755,328
Total Cost $25.72

Model Usage Distribution

Model Cost ($) Input Tokens Output Tokens Reasoning Tokens
gpt-5.4 25.72 39,459,933 351,632 202,151