LifeTrace

English

简体中文

English

简体中文

Appearance

Research Scenario: Academic Research Material Management System ​

Scenario Description ​

Dr. Zhang is a computer science researcher working on machine learning PhD research. Her daily work includes:

  • 📚 Reading large amounts of academic papers (10-20 per week)
  • 💻 Writing and debugging experimental code
  • 📊 Analyzing experimental data and visualizing results
  • ✍️ Writing papers and technical reports
  • 🔬 Participating in academic discussions and seminars

But she often faces these challenges:

  • 🤔 "Read a paper months ago about a method, can't remember where it was"
  • 📄 "Remember a formula or algorithm but can't find which paper"
  • 🔍 "Want to find previous experimental results for comparison, but too many data files"
  • 📝 "Need to cite when writing papers but can't recall specific chart sources"
  • ⏰ "Don't know where time was spent on research tasks"

After using LifeTrace, Dr. Zhang built a personal "research knowledge base" that greatly improved research efficiency.

Usage ​

1. Build Personal Research Knowledge Base ​

Dr. Zhang starts LifeTrace when beginning research work each day:

bash
# Start LifeTrace service
python start_all_services.py

LifeTrace automatically records:

  • 📖 Paper content in PDF reader
  • 💻 Code and results in Jupyter Notebook
  • 📊 Visualization charts of experimental data
  • 🌐 Academic resources browsed in browser
  • ✍️ Paper writing process in LaTeX editor

2. Quick Paper Retrieval ​

When Dr. Zhang wants to find "that paper about attention mechanism":

bash
curl -X POST http://localhost:8840/api/semantic-search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "attention mechanism Transformer",
    "limit": 20
  }'

Or search in the Web interface, the system will:

  • 🎯 Find all related paper reading records
  • 📄 Display key paragraphs and formulas from papers
  • 📅 Sort by reading time
  • 🔗 Provide complete context (content before and after pages)

3. Experimental Results Comparison ​

Find historical results of specific experiments:

bash
curl -X POST http://localhost:8840/api/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "model accuracy baseline",
    "filters": {
      "apps": ["Jupyter", "VSCode"],
      "time_range": {
        "start": "2025-09-01",
        "end": "2025-10-12"
      }
    }
  }'

Quickly find:

  • 📊 Performance comparison charts of different model versions
  • 🔢 Specific numerical results
  • ⚙️ Hyperparameter settings used at that time
  • 📝 Experimental notes and observations

Actual Results ​

After using LifeTrace for one year, Dr. Zhang's research efficiency significantly improved:

📈 Research Output Increased ​

  • Paper publications increased 40%: Faster finding of relevant literature and data
  • Experiment iteration speed improved 2x: Quick review of historical experimental results
  • Literature review quality improved: Complete literature reading records

⏱️ Time Efficiency Improved ​

  • Literature search time reduced 85%: From average 20 minutes to 3 minutes
  • Faster experiment reproduction: Complete parameter and code records
  • Writing efficiency improved 50%: Quickly find citation materials

💡 Research Quality Improved ​

  • Avoid repetitive work: Clearly know what experiments were done
  • Clearer research ideas: Complete research trajectory records
  • More cross-domain connections: Easy to find connections between different research

Configuration Recommendations ​

Research Scenario Specific Configuration ​

yaml
screenshot:
  interval: 120  # Every 2 minutes, suitable for deep thinking scenarios
  quality: 95    # High quality, ensure formulas and charts are clear
  smart_capture: true
  
  # Increase capture frequency for specific applications
  app_specific:
    PDF_Reader:
      interval: 60  # Once per minute when reading papers
    Jupyter:
      interval: 90  # Every 1.5 minutes during experiments
    LaTeX:
      interval: 180 # Every 3 minutes when writing

ocr:
  # Enable advanced OCR for academic scenarios
  engine: rapidocr
  language: en  # Mainly English
  recognize_formulas: true  # Recognize mathematical formulas
  recognize_tables: true    # Recognize tables

apps:
  whitelist:
    - Adobe Acrobat  # PDF reading
    - Zotero        # Reference management
    - Jupyter       # Experiments
    - VSCode        # Code
    - PyCharm       # Development
    - Overleaf      # LaTeX writing
    - Chrome        # Academic search
    - MATLAB        # Numerical computation

Best Practices ​

1. Build Research Tag System ​

Build systematic tags for research content:

bash
# Add paper tags
curl -X POST http://localhost:8840/api/tags \
  -H "Content-Type: application/json" \
  -d '{
    "screenshot_id": "paper_123",
    "tags": ["deep-learning", "attention-mechanism", "important-paper", "Transformer"]
  }'

Recommended tag categories:

  • Research topics: deep-learning, machine-learning, NLP, CV
  • Paper types: important-paper, survey, basic-theory, applied-research
  • Experiment types: baseline, ablation-study, comparative-experiment, innovative-method
  • Status tags: to-study-deeply, implemented, to-reproduce, inspiration-source

User Testimonial ​

"LifeTrace is my research assistant. It used to take half an hour to find a paper I read months ago, now it only takes 30 seconds. More importantly, it helps me build a searchable personal knowledge base that allows me to better leverage past research accumulation." —— Dr. Zhang, Computer Science Researcher

Next Steps ​