Daily Log — 2026-02-13

Today’s Overview

  • What I did: Translated the MIHD project’s English enhancement plan document (ENHANCEMENT_PLAN.md) into Chinese
  • How I did it: Used Codex’s exec_command tool to read the original document, then wrote the translated content to a new file using heredoc syntax
  • Why it matters: Provides Chinese-readable documentation of the MIHD enhancement plan, lowering the barrier for team members reading English technical docs

Translated the MIHD project enhancement plan document to Chinese and wrote it to a new file

Today’s Tasks

Implementation & Fixes

  • ✅ Translated ENHANCEMENT_PLAN.md to Chinese — Read the contents of /hpc/group/yizhanglab/zt81/MIHD/docs/ENHANCEMENT_PLAN.md (covering 6 enhancement directions including normalization, Q-Former, QueST integration, and config refactoring), then wrote the translation to ENHANCEMENT_PLAN_CN.md

Human Thinking vs. AI Thinking

Implementation Level

How the Document Translation Task Was Executed

Role Approach
Human User explicitly requested the translation be written to a file, not printed to the chat
AI AI first listed the directory to confirm the environment, then read the source document in segments, and finally wrote to the new file using heredoc cat

Analysis: The user focused on the final deliverable (file on disk), while the AI focused on the execution path (read then write in steps) — the two approaches aligned and complemented each other.

AI Limitations

Notable Limitations

  • The AI only read the first 400 lines of the source document (via two sed commands); if the document exceeds 400 lines, the translation may be incomplete

Today’s Takeaways

Practical Insights

  • In an HPC environment (remote Linux server), Codex can directly operate on the remote filesystem via exec_command, making it well-suited for batch document processing tasks

Session Summary

✅ Translated the MIHD enhancement plan from English to Chinese and saved it 05:52:05.645 | codex The user requested that ENHANCEMENT_PLAN.md on the HPC server be translated to Chinese and saved as a new file. The AI first confirmed the directory structure via ls, then used sed to read the source content in segments (covering 6 enhancement directions including normalization, Q-Former, and QueST batch correction), and finally wrote the output to ENHANCEMENT_PLAN_CN.md using heredoc syntax. The task completed successfully; the AI provided the final file path and noted that translation style could be adjusted as needed.

Token Usage

Claude Code

Overview

Metric Value
Total Tokens 20,727,194
Input Tokens 62,729
Output Tokens 2,128
Cache Creation 2,267,949
Cache Read 18,394,388
Cache Hit Rate 89.0%
Total Cost (USD) $9.9883

Model Breakdown

Model Input Output Cache Creation Cache Read Cost Share
claude-opus-4-6 8,891 654 443,897 3,628,750 $4.6495 46.5%
claude-haiku-4-5-20251001 53,436 831 1,426,784 12,162,805 $3.0574 30.6%
claude-sonnet-4-5-20250929 402 643 397,268 2,602,833 $2.2815 22.8%

Usage by Device

Device Total Tokens Input Output Cost
DCC 7,596,706 38,434 621 $3.4224
TzJsDesktop 13,130,488 24,295 1,507 $6.5659

Codex

Overview

Metric Value
Total Tokens 11,370,042
Input Tokens 11,340,003
Output Tokens 30,039
Reasoning Tokens 4,807
Cache Read 11,006,976
Total Cost (USD) $2.9296

Model Breakdown

Model Input Output Reasoning Cache Read Cost Share
gpt-5.2-codex 121,197 13,120 128 93,184 $0.2490 8.5%
gpt-5.3-codex 11,218,806 16,919 4,679 10,913,792 $2.6806 91.5%