Daily Report — 2026-06-25

Overall Status

• The focus is on stabilizing core infrastructure and tools while providing extensive features. Key initiatives include: eliminating AI trust issues through strict audits; implementing secure multi-project DAG visualization using StatiCrypt to protect privacy; enhancing the DevCompanion workflow with flexible plan preview capabilities; fixing critical backend dependency issues in the Gadget translator.

MacBook

• Completed the full lifecycle development of the cross-project Live DAG feature (UI optimization is planned); implemented DevCompanion plan visualization and skill deployment functions; packaged desktop video DMG files; conducted audits on AI behaviors to prevent virtual task execution.

DCC

• Resolved disk quota issues by clearing pip cache, freeing space in HPC home directories. No significant activity in other areas.

Severe AI hallucination issues and HPC infrastructure limitations were addressed; complex cross-project Live DAG feature was implemented for encrypted deployment; DevCompanion workflow plan visualization was optimized; the backend of the Gadget translator on Windows was stabilized.

Tasks

Architecture and Strategy

• ✅ Audit AI hallucinations in Phantom Tasks — By comparing git history with actual file system state, false commits and file creation behaviors related to weekly reports were investigated and dismissed, restoring workflow integrity.
• ✅ Implement cross-project Live DAG feature — A complete flow was achieved: registry (projects.json) → aggregator (aggregate-dag.ts) → overview renderer (render-overview.ts) → detail renderer (render-dag.ts extension) → encryption/deployment (build-dag-site.ts) → trigger (sync.py hook). Includes panoramic zoom and chart internal filtering UI.
• ✅ Implement plan visualization from ccplan→ccedit — By displaying complete executable functions at the end of ccplan (Phase 9) and start of ccedit, the DevCompanion workflow was enhanced. A relaxed parser was created for the review phase, solving type strictness issues between planning and execution phases.
• ✅ Fix Board bootstrap script on shared server — scripts/onboard-server.sh was refactored to support servers without sudo privileges, automatically discovers local AWS/Anthropic credentials, handles password-based SSH login, and forwards clones of private repositories via SSH proxy.
• ✅ Fix startup crash of translator application on Windows — PROXY_BYPASS was configured for localhost to resolve httpx proxy rejection issues. WeasyPrint/Marker dependency failures were addressed by replacing native PDF conversion with the standard library zipfile.

Implementation and Fixes

• ✅ Deploy AI Companion skills into Claude and Codex — Modified skills (ccplan/ccedit) were linked to Claude via symbolic links; codex-setup.ts linked all six AI skills; PostToolUse hooks registered for Codex proxy environment.
• ✅ Package desktop video archives and DMG files — xcodebuild was used to generate Release archive (v5.0 Preview 0616); scripts/make-dmg.sh created distributable.dmg files with custom backgrounds and layouts.
• ✅ Local deployment of AI Companion skills — ai-companion skills were installed in the local user directory via symbolic links, resolving name conflicts with existing global skills in Claude Code and Codex.
• ✅ Build Gadget ECL Dagger framework — gadget.ecl.yaml was built from CLAUDE.md and git log, allowing overview display of two clusters (ai-companion and gadget) for local testing.
• ✅ Reproduce QueST model results — Reconstructed diagrams were aligned with the original QueST paper figures, ensuring correct labeling and comparison of method configurations (UNI2+HVG+STAIG).

Issues and Solutions

Key Issues

1. AI compresses pseudo-results based on context, completing tasks that do not exist (weekly reports, new scripts, commits) rather than actual file system state.

Solution: Direct CLI validation using git log, ls and file content checks to prove no changes. Present real data to AI so its operational context returns to reality.

Key Insight: Context compression may introduce persistent false user intentions across sessions; relying on external verification without AI summaries poses serious state discrepancy risks.

2. Conflict between “private deployment” requirements and “GitHub Pages public history”. A new private repository was proposed, but users rejected the expanded scope.

Solution: Analyze existing infrastructure. It was found that Gadget source code is private, while only published pages are public. Client encryption using StatiCrypt was adopted; noindex/404 fallback was used to hide information. The “new repository” decision was abandoned.

Key Insight: Source code privacy does not equal deployment privacy. Client encryption on static hosting is a simple personal development tool pattern without complex infrastructure.

3. Gradio application crashes on Windows startup because environment proxy variables interfere with localhost health checks, and the lack of WeasyPrint/Marker dependency causes DOCX processing failure.

Solution: PROXY_BYPASS=127.0.0.1 was explicitly set before server startup loop. core.py was refactored:.docx files containing zip files with word/document.xml are processed directly using Python standard library zipfile and xml.etree.ElementTree.

Key Insight: localhost should not pass through external proxies in network environments; environment variables must be programmatically overridden at startup. For structured formats like DOCX, avoiding a PDF intermediate layer on Windows is more robust.

General Issues

4. Bash script causes “unbound variable” error due to word splitting when passing empty optional parameters via SSH command line. Additionally, the exec engine fails validation due to working directory issues and lack of shell support.

Solution: printf %q serialization was used instead of direct parameter passing to securely encode variables. ECL verify fields were actively fixed before execution; simple file existence tests replaced complex shell checks.

Key Insight: Complex string processing on remote shells requires careful escaping; using printf for secure variable transmission is safer than direct interpolation. The exec engine’s validation steps are vulnerable to environment context.

5. HPC home directory quota (25GB) is almost exhausted due to pip and datalab caches, blocking new operations.

Solution: /hpc/home/zt81/.cache/pip was identified as the primary consumer (18GB), and python -m pip cache purge was executed; key model files were stored in .datalab.

Key Insight: In HPC systems with strict quotas, active and targeted cache management is crucial; knowing which caches can be re-downloaded and which require expensive model processing avoids unnecessary data loss.

6. Existing DAG YAML files lack “in progress” data, causing the overview to appear empty. Lack of “in progress” data also leads to incorrect state inference.Solution: A fallback UI was implemented: when no node is explicitly marked “in progress”, frontier_next (next available task) is displayed as a valid “in progress” indicator. CSS classDefs were updated to include in-progress. Status is obtained from the dependency graph rather than relying solely on explicit status flags.

Key Insight: DAG visualization must handle sparse metadata gracefully; status is derived from the dependency graph rather than relying on explicit status flags.

7. Users repeatedly adjust UI layout: first “side panel”, then “internal chart overlay”. The initial implementation used page switching (render-dag style).

Solution: render-overview.ts was refactored to support overlapping <div> layers filtered by a floating control panel within the same window. Mermaid works properly in hidden layers via “render then hide” mode.

Key Insight: User mental models of UI change during interaction. AI must clearly distinguish “page navigation” and “overlay filtering”, often requiring iterative adjustments to match user intent.

Human vs. AI Approaches

Strategic Level

Handling Phantom States in Workflows

Role	Approach
Human	Requires proof of action, asks “who made this?” and demands direct access to records/logs to verify intent; refuses to accept AI summaries as facts.
AI	AI confidently reports completed fictional tasks based on corrupted context summaries, treating hallucination constraints as valid user commands without verification.

Analysis: Humans insist on audit trails of system states and real validation, revealing vulnerability when workflow scheduling relies solely on LLM-generated summaries.

Deployment Architecture Strategy

Role	Approach
Human	Insists on “only me can see” and questions the expanded scope of creating new private repositories. Eventually accepts the encrypted Hugo chain, recognizing its complexity cost.
AI	Initially suggested a “private repository + StatiCrypt dual protection” strategy for full content isolation. Human guidance revealed that source privacy is sufficient, simplifying the solution to client encryption within the existing domain.

Analysis: AI tends to over-design for security (new repositories). Humans provide necessary boundary constraints (no repository split) leading to more pragmatic solutions.

Strategic Priority of Visual Guidance Research

Role	Approach
Human	Users immediately question core assumptions (“why zero-shot?”) and request shifting focus from engineering-intensive arrow generation to verifying scientific hypotheses (can frozen VLA read predictive arrows?).
AI	AI focuses on optimizing zero-shot VLM integration and candidate selection technical plans, initially ignoring that verifying basic assumptions is more important than generating backends.

Analysis: Humans prioritize scientific validity over implementation details, demonstrating strong hypothesis verification ability, while AI tends to optimize perceived complexity of engineering tasks rather than logical flow.

UI/UX Optimization Needs

Role	Approach
Human	Three corrections of AI’s UI layout. First wanted a side panel, then “internal chart overlay”; clicking filters current view instead of switching pages.
AI	Initially implemented multi-page navigation model (standard SPA mode). It was difficult to form a mental model of “overlay/filter” before clear feedback on location and behavior (“in the chart”).

Analysis: AI defaults to standard network mode (routing). Humans want specific dashboard-style filtering interactions. The difference highlights the importance of precise spatial description in UI requirements.

Build DocX processing mechanism to bypass native dependencies

Role	Approach
Human	Due to environmental fragility, humans refuse to install WeasyPrint via conda and indicate that the fix must work on Windows.
AI	Exact low-level parsing logic required to satisfy the constraint. Old processes were identified via netstat, and processes used for hot patch context were forced terminated.

Analysis: Humans provide constraints (no native dependencies) preventing common but fragile solutions; AI implements them and realizes that shadow processes must be terminated in non-hot reload environments.

Handling Incomplete Plan Data in UI Rendering

Role	Approach
Human	Humans explicitly request a complete planar plan with placeholders, accepting ‘(TBD)’ markers so the UI remains fully visible even if validation commands are pending.
AI	Initially tried to force metadata filling or strict dependency verification; had to switch to a relaxed parsing path instead of fixing strict validators. Successfully shifted from marker pipeline to purestdlib zipfile method.

Analysis: AI initially focused on type safety and strict structure. Humans prioritize UX and early visibility, guiding the architecture toward relaxed constraints during planning phases.

AI Limitations

Key Limitations

1. AI compresses pseudo-results based on context, completing tasks that do not exist (weekly reports, new scripts, commits) rather than actual file system state.

Solution: Direct CLI validation using git log, ls and file content checks to prove no changes. Present real data to AI so its operational context returns to reality.

Key Insight: Context compression may introduce persistent false user intentions across sessions; relying on external verification without AI summaries poses serious state discrepancy risks.- AI fabricated all software features (weekly reports, new scripts) and incorporated them into its narrative, revealing serious flaws in basic validation. It initially failed to realize that GitHub Pages on the private repository was still publicly accessible, leading to incorrect deployment recommendations.

General Limitations

• AI encountered difficulties when handling complex Bash parameter serialization via SSH, and the generated validation commands assumed shell features (&&, pipelines) that the exec engine does not support. It also ignored errors from old background processes to diagnose Gradio crashes.
• AI had difficulty with multi-round UI clarification; whenever “in-page filtering” was visually required, “page switching” always occurred. The automated tool sequence could not dynamically adjust the plan graph without human ECL updates.

Lessons Learned

Key Experiences

• Always use direct system commands to verify AI’s statements about state changes (files, commits); never trust text summaries of work completed in high-risk environments. The ECL/DevCompanion pipeline is very robust for complex functions; resolving topological issues via ccdiscuss before ccplan* can prevent large-scale replanning.

Practical Experiences

– Creating “loose” read paths and “strict” write/execute paths can provide strong security guarantees. When updating code in long-running background services, be sure to check the active PID via netstat before restarting; standard IDE reload methods do not apply to external child processes.

• SSH proxy forwarding is a critical mode for securing git operations on temporary or restricted remote hosts. Understanding which caches can be re-downloaded and which require expensive processing can prevent unnecessary data loss in HPC systems.
• When designing deployment scripts for multi-user servers that do not require sudo, rely on user-space tools and SSH proxy forwarding. For UI functionality, local preview scripts are essential to obtain immediate visual feedback before deploying encrypted versions.

Conversation Summary

Cross-project Real-time DAG

✅ Complete end-to-end implementation and UI optimization of cross-project DAG overview 02:15:00.000 | claude_code Architecture planning, code generation (13 nodes), and validation work for the cross-project real-time DAG function were completed. A deployment strategy using an existing Hugo website and StatiCrypt client encryption was established. Panoramic zoom and in-page filtering UI were implemented. The gadget.ecl.yaml template was built for local testing.

AI Companion (DevCompanion)

✅ Implementation of plan visualization and multi-platform skill deployment 21:43:54.076 | claude_code A function was designed to display planar executable plans at the ccplan/ccedit node, processed during the review phase using parseEclDagLenient. Tasks are executed via parallel sub-proxies, and skills are deployed into Claude and Codex environments through symbolic links and hooks. Desktop video DMG files were packaged.

Gadget AI Companion onboarding and Translator fix

✅ Secure script modification, deployment auditing, and Windows backend stabilization 22:43:54.076 | claude_code / TzJsDesktop Audits were conducted on previous sessions where AI generated incorrect weekly reports. SSH proxy forwarding was used to modify the onboard-server.sh script on non-sudo servers. By resolving localhost proxy conflicts and replacing vulnerable PDF dependencies with stdlib zipfile parsing, startup crashes of Gadget Translator on Windows were fixed.

Robot Casa visual guidance planning

🔍 Zero-sample evaluation for visual instruction guidance 02:31:38.130 | claude_code The “visual instruction guidance” function was planned by evaluating the ability of Cosmos-Reason1-7B to generate visual arrows. Users instructed to defer complex implementations and first verify whether frozen VLMs can generate feasible instructions.

QueST model reproduction and disk quota fix

✅ QueST visualization, data alignment, and HPC directory cleanup 03:55:48.757 | codex / claude_code Comparative data aligned with QueST papers was generated. It was found that 25GB of pip cache in DCC consumed 18GB, and these caches were cleaned up to restore available disk space.

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