Daily Log — 2026-02-12

Today’s Overview

• What was done: Completed two independent tasks across two devices: translated and restructured the MIHD project’s English enhancement plan into a structured Chinese version, and added GLM model billing support to the ccusage tool with format/type-check validation passing
• How it was done: On DCC, read the file and used AI to compress and output a Chinese summary; on tianhe, explored ccusage’s pricing loading chain, added local JSON file reading and merging logic in _pricing-fetcher.ts, and resolved multiple rounds of TypeScript/ESLint errors until all checks passed
• Why it matters: The MIHD plan document is now significantly more readable, improving team alignment; ccusage can now automatically read a local ccusage.json to calculate GLM model costs, resolving the core pain point of previously only supporting Claude billing

DCC

• What was done: Read and translated the MIHD project’s /docs/ENHANCEMENT_PLAN.md, producing a structured Chinese version of the enhancement plan
• How it was done: AI read the original Markdown file, compressed and distilled each of the 6 BIG AIMs one by one, preserving key implementation details such as filenames, CLI arguments, and config fields
• Why it matters: Provides a clear Chinese reference document for the phased implementation of MIHD normalization, Q-Former, batch correction, and other features

tianhe

• What was done: Implemented GLM/local pricing file loading for ccusage, fixed multiple TypeScript and ESLint errors until format/typecheck/targeted tests all passed
• How it was done: Explored the _pricing-fetcher.ts → _macro.ts → packages/internal/pricing.ts chain, implemented loadLocalPricing() using node:fs/promises to read local JSON and merge with pre-fetched Claude pricing; iteratively fixed issues including incorrect Result.try() usage, ESLint errors on the process global, and sample_spec metadata leaking into the pricing table
• Why it matters: ccusage now supports loading any LiteLLM-format pricing via the CCUSAGE_PRICING_FILE environment variable or the default path ~/.ccusage/ccusage.json, fundamentally resolving the GLM billing problem

Implemented and debugged GLM billing support for ccusage on tianhe; organized the Chinese version of the MIHD enhancement plan on DCC

Today’s Tasks

Architecture & Strategy

• 🔄 ccusage GLM model billing support — Added loadLocalPricing() function in apps/ccusage/src/_pricing-fetcher.ts to load pricing data from a local LiteLLM-format JSON file (defaults to ~/.ccusage/ccusage.json, overridable via CCUSAGE_PRICING_FILE) and merge it into the offline cache. format/typecheck and targeted _pricing-fetcher.ts tests pass; global test failures in apps/amp are unrelated to this change

Implementation & Fixes

• ✅ MIHD enhancement plan Chinese translation — Translated the 6 BIG AIMs in ENHANCEMENT_PLAN.md (normalization, UNI2+scGPT experiments, Q-Former/LLaVA, Niche query, batch correction, end-to-end configurability) into a structured Chinese version, preserving key implementation details such as filenames, CLI arguments, and config fields

Issues & Solutions

Critical Issues

1. Result.try() in the @praha/byethrow library returns a function rather than a direct result — the AI’s initial code treated it as a direct result, causing TypeScript type errors

Solution: Changed const parsedResult = Result.try({...}) to const parseLocalPricing = Result.try({...}); const parsedResult = parseLocalPricing();

Key insight: Result.try() is a higher-order function that returns a reusable parser function; calling that function returns the Result<T, E>. This differs from common Result monad implementations such as Rust or fp-ts, and requires special attention

2. The local ccusage.json contains metadata keys such as sample_spec, which after valibot schema parsing were treated as empty pricing entries, causing a test assertion to fail — the assertion required that only entries with token cost fields should be loaded

Solution: After schema parsing succeeds, added an additional check for input_cost_per_token != null || output_cost_per_token != null to filter out entries with no pricing data

Key insight: LiteLLM pricing JSON files mix documentation entries (like sample_spec) with real model entries; business-level filtering is required beyond the schema layer

General Issues

3. ESLint rules prohibit direct use of the global process object — the AI used process.env.CCUSAGE_PRICING_FILE in new code, causing a lint failure

Solution: Added import process from 'node:process' at the top of the file

Key insight: This project enforces explicit import of all Node.js built-ins via the node: protocol — a strict ESLint constraint

4. pnpm was not available in the ccusage environment, and corepack failed to download pnpm due to a network permission error (EPERM), making it impossible to run format/typecheck/test

Solution: The user manually installed pnpm and re-ran pnpm install with elevated privileges, successfully installing 930 packages

Key insight: HPC cluster environments may lack write access to ~/.cache/node/corepack and have restricted npm registry network access, requiring the user to manually install the package manager

Human Thinking vs. AI Thinking

Strategic Level

GLM Pricing Data Source

Role	Approach
Human	The user proactively pointed out that a local ccusage.json file containing GLM entries already existed at /HOME/sysu_gbli2/sysu_gbli2xy_1/.ccusage/ccusage.json, and directly asked the AI to leverage that existing file
AI	The AI’s initial approach was to manually add a GLM provider prefix in the code and extend the isClaudeModel filter function, unaware that the user already had a complete local pricing file

Analysis: The human knew a ready-made data asset existed and avoided reinventing the wheel; the AI defaulted to modifying code logic rather than reusing an existing data file

Implementation Level

Handling the pnpm Environment Issue

Role	Approach
Human	The user manually installed pnpm to resolve the environment issue, reporting back “now I installed pnpm”
AI	The AI attempted workarounds such as using corepack and adjusting XDG_CACHE_HOME, all of which failed

Analysis: For toolchain installation issues on an HPC cluster, the human’s direct approach (installing the tool) was far more efficient than the AI’s roundabout solutions

AI Limitations

Critical Limitations

• Misused the Result.try() API from @praha/byethrow: assumed it behaved like common Result monad implementations (returning a result directly), when in fact it returns a reusable parser function that must be called to obtain the result — this required two rounds of fixes
• Did not account for non-pricing metadata keys (e.g., sample_spec) mixed into the LiteLLM pricing JSON on first implementation, leading to test failures before the business-level token cost field filtering was added

General Limitations

• Failed to anticipate the network and permission constraints of an HPC cluster environment; multiple attempts to install pnpm via corepack failed due to EPERM or network fetch errors, wasting many turns before recognizing that the user needed to resolve it manually

Today’s Takeaways

Core Takeaways

• @praha/byethrow’s Result.try({try, catch}) returns a function (factory pattern) rather than executing immediately and returning a Result; this differs from common Result implementations like Rust or fp-ts and deserves special attention
• ccusage’s offline pricing is pre-filtered at build time via the isClaudeModel function in _macro.ts, retaining only Claude-related models; extending multi-model support requires synchronized changes in both that macro and _pricing-fetcher.ts
• The MIHD project plan is implemented in strict dependency order: config foundation (Idea 6 Phase 1) → normalization → UNI2+scGPT experiments → Q-Former/LLaVA → Niche query → batch correction → full config finalization; this ordering is designed to avoid accumulating refactoring costs

Practical Takeaways

• When working with Node.js projects on HPC clusters, package managers (pnpm) and corepack may fail due to filesystem permissions or network isolation — confirm toolchain availability upfront

Session Summaries

MIHD

✅ MIHD Enhancement Plan Translation: Structured Chinese Version of 6 BIG AIMs 23:05:59.274 | codex The user requested translation of the MIHD project’s ENHANCEMENT_PLAN.md into Chinese. After reading the file, the AI compressed and distilled each of the 6 modules — normalization, UNI2+scGPT experiments, Q-Former/LLaVA fusion, Niche query, batch correction, and end-to-end configurability — while preserving implementation details such as filenames, CLI arguments, and YAML config snippets. The final output was a complete Chinese plan document including dependency order, implementation phases, and validation approaches.

ccusage

🔄 Adding GLM Model Billing Support to ccusage: Local Pricing File Loading Implementation and Multi-Round Debugging 15:47:15.640 | codex The user first asked about basic ccusage usage, then raised the need to calculate GLM model costs. The AI explored the pricing chain (_pricing-fetcher.ts → _macro.ts → packages/internal/pricing.ts) and found that offline mode only pre-fetches Claude models. The user pointed out a local ccusage.json containing GLM entries, and the AI proceeded to implement the loadLocalPricing() function. The debugging phase involved four issues: missing pnpm, TypeScript errors from Result.try() misuse, ESLint errors on the process global, and metadata keys leaking into the pricing table. In the end, format/typecheck and targeted _pricing-fetcher.ts tests all passed; unrelated apps/amp test failures were left unresolved.

Token Usage

Claude Code

Summary

Metric	Value
Total Tokens	2,812,835
Input Tokens	45,385
Output Tokens	144
Cache Created	368,229
Cache Read	2,399,077
Cache Hit Rate	86.7%
Total Cost (USD)	$1.7933

Model Breakdown

Model	Input	Output	Cache Created	Cache Read	Cost	Share
claude-opus-4-6	15	63	137,190	899,384	$1.3088	73.0%
claude-haiku-4-5-20251001	45,370	81	231,039	1,499,693	$0.4845	27.0%

Codex

Summary

Metric	Value
Total Tokens	2,078,880
Input Tokens	2,058,788
Output Tokens	20,092
Reasoning Tokens	9,479
Cache Read	1,840,256
Total Cost (USD)	$0.9858

Model Breakdown

Model	Input	Output	Reasoning	Cache Read	Cost	Share
gpt-5.2-codex	56,282	1,260	0	45,440	$0.0446	4.5%
gpt-5.3-codex	2,002,506	18,832	9,479	1,794,816	$0.9412	95.5%