“jsonl”的搜索结果

从Claude Code的本地使用日志汇总令牌用量和美元预估费用，按模型、会话或日期查看支出，比较预算并导出CSV，避免重复计算会话累计值。

• 比较今天与昨天的支出
• 按模型和会话查看费用
• 比较预算与实际支出

协助配置vLLM，将大语言模型作为API提供服务或批量生成文本，涵盖部署、性能监控、量化和多GPU分工，也用于排查显存不足与响应缓慢。

• 提供兼容OpenAI的API
• 批量处理大量提示词
• 在有限显存下运行模型

通过API查询和更新Shopify的商品、订单、客户及库存，使用curl获取所需数据，支持价格修改、分地点库存调整、自定义字段读写和批量数据导出。

• 搜索商品并更新价格
• 获取订单和客户信息
• 查询并调整各地点库存

指导通过评价回答的强化学习对大语言模型进行后训练。结合Megatron-LM与SGLang，涵盖数据准备、并行执行及自定义生成流程。

• 开始语言模型的GRPO训练
• 并行运行回答生成与训练
• 训练使用工具的智能体

按助手消息计算Claude Code单次会话的费用，找出高额回复，并结合输入输出词元、缓存读写、所用模型及费用分布，调查费用产生的原因。

• 调查被标记为高费用的会话
• 检查缓存写入费用
• 调查指定时间后的高费用消息

Review recent Claude Code chats to identify development strengths, recurring difficulties, and learning priorities. Receive a personalized report with practical recommendations and relevant HackerNews articles, delivered to your own Slack direct messages.

中文简介正在准备中。当前显示英语简介。

• Reviewing development habits from recent chats
• Prioritizing recurring technical difficulties
• Finding learning articles relevant to current work

Create and audit JSON-LD, structured data that describes page content to search engines. Check JSON syntax, schema types, and required fields for articles, products, FAQs, and breadcrumbs, then guide validation for enhanced search results.

中文简介正在准备中。当前显示英语简介。

• Creating structured data for articles and products
• Checking FAQ and breadcrumb structured data
• Finding JSON syntax errors and missing required fields

Execute a written work plan by assigning implementation and verification to subagents. Track progress, isolate work in Git worktrees, record test evidence, and require independent verification before marking tasks complete, with optional pull request delivery.

中文简介正在准备中。当前显示英语简介。

• Executing a saved work plan
• Assigning independent tasks to parallel agents
• Resuming work with recorded progress and evidence

Get started with omo through a guided feature tour and workflow introduction. Optional setup migration and past-session analysis connect its features to existing work habits, while memory tools record useful preferences for future sessions.

中文简介正在准备中。当前显示英语简介。

• Learning omo's features and basic workflow
• Migrating settings from another coding agent
• Reviewing past agent sessions for work patterns

Manage Obsidian notes through its Local REST API. Search a vault, create or update notes, append content, and execute Obsidian commands from the command line, with JSON output for agent workflows.

中文简介正在准备中。当前显示英语简介。

• Find meeting notes in a vault
• Create project notes and append updates
• Find notes by metadata conditions

Retrieve U.S. federal financial data from the Treasury’s public API, including debt, revenue, spending, interest rates, and exchange rates. Filter dates and fields, retrieve multiple pages, and organize results into tables for analysis.

中文简介正在准备中。当前显示英语简介。

• Tracking U.S. national debt over time
• Comparing monthly federal revenue and spending
• Retrieving Treasury reporting exchange rates

Build Python AI applications on Microsoft Foundry with azure-ai-projects. Provides examples for creating agents, connecting tools, managing conversations and project resources, and running evaluations through Foundry and OpenAI-compatible clients.

中文简介正在准备中。当前显示英语简介。

• Build an agent that searches uploaded documents
• Implement conversation threads and response handling
• List model deployments and project connections

Inspect local AI coding session logs with agenttrace to identify cost spikes, delays, failed tools, and retry loops. Produce readable reports, compare attempts, and configure automated checks for unusual or unhealthy runs.

中文简介正在准备中。当前显示英语简介。

• Investigate slow or expensive AI coding sessions
• Review failed runs before retrying
• Compare tool usage and verification across attempts

Turn graded execution traces and production logs into model training examples and preferred-versus-rejected response pairs. Select useful records, remove sensitive information, exclude evaluation examples, and preserve links to the original runs.

中文简介正在准备中。当前显示英语简介。

• Convert high-scoring successful runs into training examples
• Use expert-corrected failures as training examples
• Pair passing and failing responses to the same task

Export an approved fine-tuned model into a format suited to its hardware and runtime. Choose weight packaging and quantization, which reduces numerical precision, then compare outputs before and after export to check for regressions.

中文简介正在准备中。当前显示英语简介。

• Exporting a checkpoint approved for deployment
• Choosing a model format for a target GPU or laptop
• Comparing outputs before and after export

Investigate incorrect, missing, or unexpectedly changed OpenViking memories by tracing memory files back to archived change records and, when needed, source messages. Return an evidence trail, identify gaps, and suggest next steps without modifying data.

中文简介正在准备中。当前显示英语简介。

• Investigate incorrect memory content
• Check unexpected memory paths or owners
• Trace missing or deleted memories

Create and manage Arize datasets for evaluation and experiments using the ax CLI. Import records from files, append examples, inspect dataset versions, and export a chosen version for offline analysis, with checks for field consistency and export completeness.

中文简介正在准备中。当前显示英语简介。

• Creating evaluation datasets from files
• Adding examples to an existing test set
• Inspecting dataset metadata and versions

Reconstruct activity on a compromised computer by combining file timestamps, event logs, browser history, and other evidence into one timeline. Extract and filter events with Plaso, then review and annotate them collaboratively in Timesketch.

中文简介正在准备中。当前显示英语简介。

• Reconstructing the sequence of a computer compromise
• Comparing events across logs and file records
• Investigating timestamp changes and cleared logs

Build a shared incident timeline in Timesketch from endpoint, server, and cloud logs. Search events, add tags and investigator notes, reconstruct the attack sequence, and organize findings into a story for incident reporting.

中文简介正在准备中。当前显示英语简介。

• Reconstructing the sequence of an incident
• Reviewing evidence with multiple analysts
• Combining Plaso output and other logs

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

中文简介正在准备中。当前显示英语简介。

• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

中文简介正在准备中。当前显示英语简介。

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Review completed AI work by scoring task difficulty and execution quality separately. A fixed scoring matrix, arguments for higher and lower ratings, and saved score history help examine weaknesses and detect repetitive scoring patterns across sessions.

中文简介正在准备中。当前显示英语简介。

• Evaluate a completed feature implementation
• Review the quality and coverage of a bug fix
• Reflect on work completed during a session

Summarize WeChat group conversations into organized digests with discussion highlights, verified speaker attribution, and message statistics. Keep group history, participant profiles, and factual notes across runs, or summarize only messages received since the previous digest.

中文简介正在准备中。当前显示英语简介。

• Catching up on a week's WeChat group discussions
• Summarizing messages since the previous digest
• Reviewing message counts and active participants

Organize content, contacts, knowledge, and goals in structured files. Use recorded writing preferences and past posts to draft content, prepare meeting briefs from relationship history, and review weekly progress.

中文简介正在准备中。当前显示英语简介。

• Drafting posts in a consistent personal voice
• Preparing meeting briefs from contact history
• Reviewing weekly progress and goals