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

这是一个高效的一周，其特点是重大的架构转型，包括单体研究 pipeline 的模块化、TokenMonitor desktop application 的跨平台迁移，以及大规模 bioinformatics 和 robotics 训练 workflow 的优化。大量精力投入于解决与跨平台 UI 稳定性、科学模型 alignment 以及高效跨设备数据同步相关的深层技术债。

Weekly Overview

Metric	Value
Date Range	2026-03-23 ~ 2026-03-29
Active Days	7 / 7
Total Conversations	24
Projects	21
Tasks Completed	34
Tasks In Progress	2
Total Tokens	323,343,475
Total Cost	$199.35
Claude Code Token	157,721,908
Claude Code Cost	$100.00
Codex Token	165,621,567
Codex Cost	$99.35
Daily Average Cost	$28.48

Project Progress

TokenMonitor Desktop Application (5 days active) — 🔄 active

Accomplishments:

通过剥离 native 依赖，完成了完整的跨平台迁移（macOS 到 Windows/Linux）
实现了安全的 SSH cost tracking 和双层 debug logging
解决了关键的 UI/UX 问题，包括 window resize jitter、footer shifting 和 chart hover flickering
设计了统一的 Tauri/Svelte installer pipeline，并集成了自动化 pricing engine

Blockers:

⚠️ native Win32 window management 与 WebView2/CSS reflow cycles 之间的 race conditions

Gadget Research Toolkit (3 days active) — 🔄 active

Accomplishments:

将 3000 行的单体 Python pipeline 分解为模块化、作用域明确的 components
将 code-summarization skills 重构为自适应的 academic-style 格式
实现了 Hub-and-Spoke 多语言 prompt framework，以防止 context dilution

OpenPI / BOSS Benchmark & Robotics (3 days active) — 🔄 active

Accomplishments:

将 BOSS benchmark 集成到 LIBERO environment 中
构建了 Error Recovery Benchmark v5 和 hybrid MimicGen augmentation strategies 的架构
稳定了 MuJoCo/SLURM training workflows，并解决了 BC-RNN observation space deficiencies

Blockers:

⚠️ HPC environments 中的 network proxy 和 DNS restrictions

MIHD/STGD Spatial Transcriptomics (2 days active) — 🔄 active

Accomplishments:

重构了单体 repository，以稳定 cross-section visualization toolchains
通过 scGPT checkpoint restoration 和 STAIG alignment 恢复了理论上的 ARI performance ceilings

Key Tasks

✅ TokenMonitor Cross-Platform Migration & Security Hardening — 剥离了 macOS 依赖，实现了安全的 SSH config parsing，并利用 Tauri 为 Windows/Linux 目标构建了稳健的多 OS CI/CD build system。
✅ Gadget Pipeline Modularization — 成功将单体的 daily_summary.py 研究 module 分解为八个模块化、schema-driven 的 packages，并配备了统一的 configuration loaders。
✅ MIHD/STGD Pipeline Optimization — 统一了 Visium HD coordinate mapping 和 vision encoder routing，以稳定跨多个 tissue slices 的 bioinformatics visualization toolchains。
✅ cchypothesis Skill Implementation — 设计并部署了基于 hypothesis-driven 的 debugging workflow，利用 ECL-driven constraint planning 和 dual-track triage。
✅ Error Recovery Benchmark v5 Architecture — 为 robotics training 开发了用于 uniform sampling 和 HDF5-to-LeRobot conversion pipelines 的架构。

Problems & Solutions

1. MIHD cross-section embedding failure due to orthogonal vector spaces from independent per-chunk dimensionality reduction. [MIHD/STGD] (2026-03-23)

Solution: 将研究转向 foundation model baselines (scGPT) 和显式的 joint projection matrices，以确保共享的 latent bases。

2. Multi-turn AI skill workflows experiencing catastrophic state loss during human input pauses. [cchelper/Gadget] (2026-03-23)

Solution: 使用专门的 API tools (AskUserQuestion) 通过正式的 Conversation Loop Protocol 替换了对话式 text-prompt gaps，以实现持久化的 variable tracking。

Solution: 使用 ‘position: fixed’ 实现 absolute viewport anchoring，并在发出 native IPC commands 之前通过 JS pre-layout 同步 #app height。

4. Silent ML framework degradation where scGPT attribute omission or BC-RNN missing keys caused failed training without errors. [OpenPI / Robotics] (2026-03-29)

Solution: 在 weight loading 之前强制执行显式的 class attribute assignment，并实现了 pre-clustering shape validation routines。

Learnings

Architecture (architecture)

在 Tauri/Svelte 中实现真正的跨平台 robustness，需要在 dependency layer 完全消除 native OS bindings（如 objc2），而不是依赖 conditional compilation patches。
在 IPC-heavy 的 desktop apps 中，absolute viewport anchoring 和 JS-side pre-measurement 是必须的，用以弥合 native window management 与 web-view rendering 之间的同步 gap。

Domain Knowledge (domain)

ML pipelines 中的 silent framework defaults（例如 JAX/CUDA routing 或 checkpoint loading）需要严格的 version pinning 和 deterministic key mapping，以防止 numerical degradation。

Debugging (debugging)

带有强制 evidence logging 和 read-only investigation stages 的 hypothesis-driven workflows，能显著减少复杂系统 troubleshooting 过程中的 confirmation bias。

Tools (tools)

对于跨设备同步，remote metadata extraction 比传统的 full-file mirroring 效率高得多，尤其是在 bandwidth-constrained 的 HPC environments 中。

AI Usage Notes

Effective Patterns:

✓ 用于 architectural traceability 的 ECL-driven constraint planning
✓ 用于大规模 refactoring validation 的 parallel multi-agent code reviews
✓ 用于管理多语言 skill ecosystems 的 Hub-and-Spoke prompt architectures

Limitations:

✗ 无法自主检测 silent ML failures 中的 numerical/weight degradation
✗ 在没有显式的 code-base-to-visual mapping 的情况下，难以解释 visual/video symptoms
✗ 过度依赖标准的 web/OS defaults，而非特定的 native API requirements

Next Week Outlook

优先级将转向扩展新的模块化 Gadget toolkit，利用新构建的 v5 benchmark 推进 robotics training pipelines，并完成 TokenMonitor 的 production release，包括完善的 CI/CD workflows 和经过 security-hardened 的 SSH integration。

Token Usage Statistics

Peak Day: 2026-03-29 — $85.48 / 146.2M tokens

Daily Average: $28.48

Weekly Summary 2026-W13

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

Weekly Overview

Project Progress

TokenMonitor Desktop Application (5 days active) — 🔄 active

Gadget Research Toolkit (3 days active) — 🔄 active

OpenPI / BOSS Benchmark & Robotics (3 days active) — 🔄 active

MIHD/STGD Spatial Transcriptomics (2 days active) — 🔄 active

Key Tasks

Problems & Solutions

1. MIHD cross-section embedding failure due to orthogonal vector spaces from independent per-chunk dimensionality reduction. [MIHD/STGD] (2026-03-23)

2. Multi-turn AI skill workflows experiencing catastrophic state loss during human input pauses. [cchelper/Gadget] (2026-03-23)

3. TokenMonitor UI jitter and footer jumping caused by 1-frame latency between native IPC window calls and CSS reflow. [TokenMonitor] (2026-03-27)

4. Silent ML framework degradation where scGPT attribute omission or BC-RNN missing keys caused failed training without errors. [OpenPI / Robotics] (2026-03-29)

Learnings

Architecture (architecture)

Domain Knowledge (domain)

Debugging (debugging)

Tools (tools)

AI Usage Notes

Next Week Outlook

Token Usage Statistics

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

Weekly Overview#

Project Progress#

TokenMonitor Desktop Application (5 days active) — 🔄 active#

Gadget Research Toolkit (3 days active) — 🔄 active#

OpenPI / BOSS Benchmark & Robotics (3 days active) — 🔄 active#

MIHD/STGD Spatial Transcriptomics (2 days active) — 🔄 active#

Key Tasks#

Problems & Solutions#

1. MIHD cross-section embedding failure due to orthogonal vector spaces from independent per-chunk dimensionality reduction. [MIHD/STGD] (2026-03-23)#

2. Multi-turn AI skill workflows experiencing catastrophic state loss during human input pauses. [cchelper/Gadget] (2026-03-23)#

3. TokenMonitor UI jitter and footer jumping caused by 1-frame latency between native IPC window calls and CSS reflow. [TokenMonitor] (2026-03-27)#

4. Silent ML framework degradation where scGPT attribute omission or BC-RNN missing keys caused failed training without errors. [OpenPI / Robotics] (2026-03-29)#

Learnings#

Architecture (architecture)#

Domain Knowledge (domain)#

Debugging (debugging)#

Tools (tools)#

AI Usage Notes#

Next Week Outlook#

Token Usage Statistics#

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

Weekly Overview

Project Progress

TokenMonitor Desktop Application (5 days active) — 🔄 active

Gadget Research Toolkit (3 days active) — 🔄 active

OpenPI / BOSS Benchmark & Robotics (3 days active) — 🔄 active

MIHD/STGD Spatial Transcriptomics (2 days active) — 🔄 active

Key Tasks

Problems & Solutions

1. MIHD cross-section embedding failure due to orthogonal vector spaces from independent per-chunk dimensionality reduction. [MIHD/STGD] (2026-03-23)

2. Multi-turn AI skill workflows experiencing catastrophic state loss during human input pauses. [cchelper/Gadget] (2026-03-23)

3. TokenMonitor UI jitter and footer jumping caused by 1-frame latency between native IPC window calls and CSS reflow. [TokenMonitor] (2026-03-27)

4. Silent ML framework degradation where scGPT attribute omission or BC-RNN missing keys caused failed training without errors. [OpenPI / Robotics] (2026-03-29)

Learnings

Architecture (architecture)

Domain Knowledge (domain)

Debugging (debugging)

Tools (tools)

AI Usage Notes

Next Week Outlook

Token Usage Statistics