Building use-cases

tl;dr We can build intelligent, learning systems by using an append-only observation log with flexible JSONB data and LLMs to process and synthesize insight and knowledge.

Core architecture: the observation log

At the core of our system lies a single, massive log called the observation_log. This runs on TimescaleDB and follows an append-only pattern. Every piece of data, processed thought, and new insight gets written as a new entry. Nothing ever gets deleted or changed in place. This is how we prevent catastrophic forgetting at the data layer.

The magic for flexibility comes from each entry's JSONB payload. This means the structure of what we store can change on the fly. No rigid schemas breaking when we discover something new. If the LLM figures out a new way to categorize information, it just starts doing it. The LLM acts as the cognitive engine, processing raw inputs, structuring them, and analyzing the entire log to extract new knowledge.

How the brain learns

The brain learns through a continuous loop, similar to human learning but supercharged:

graph TD
    A["Raw Data Ingestion"] --> B["Information Processing (External LLMs/Scripts)"];
    B --> C{"Observation Log"};
    C --> D("Knowledge Synthesis & Coining Terms");
    D -- "New Knowledge & Coined Terms" --> C;
    subgraph "True Second Brain"
        C
        D
    end

    subgraph "Supporting Systems & External Processes"
        A
        B
    end

    style A fill:#f9f,stroke:#333,stroke-width:2px,color:black
    style B fill:#ccf,stroke:#333,stroke-width:2px,color:black
    style C fill:#lightgrey,stroke:#333,stroke-width:4px
    style D fill:#cfc,stroke:#333,stroke-width:2px,color:black

1. Data ingestion: Raw signals come in from various sources - API outputs, system logs, text from chats or documents
2. Information processing: Dedicated scripts and LLMs structure the raw data, extract key entities, and identify relationships
3. Observation logging: The processed information gets written to the observation_log as a permanent record
4. Knowledge synthesis: The internal LLM scans accumulated information using TimescaleDB features like continuous aggregates to identify patterns and correlations
5. Knowledge persistence: New insights and coined terms get written back into the observation_log as enriched observations

Example: tracking tech trends

Let's see how this works by analyzing Hacker News data to spot emerging tech trends.

Step 1: Data ingestion and processing

We start with Hacker News posts and comments. An LLM job processes this through:

• Named Entity Recognition (NER) to identify technologies and companies
• Sentiment analysis to gauge community reaction
• Trend detection to spot emerging patterns

The processed information gets structured into the observation_log:

{
  "context_id": "hackernews:post:12345678",
  "content_summary": "Big buzz around 'XYZ Corp's new Vector DB'. People love the scalability.",
  "entities": [
    {"name": "XYZ Corp", "type": "company"}, 
    {"name": "Vector DB", "type": "technology_category"}
  ],
  "sentiment": {
    "topic": "XYZ Vector DB", 
    "score": 0.85, 
    "label": "positive"
  }
}

Step 2: Find patterns

The brain's internal LLM analyzes these observations over time. It might notice:

• Increasing mentions of vector databases
• Positive sentiment in discussions
• Growing job postings requesting vector DB experience

This leads to the synthesis of new knowledge:

{
  "context_id": "system:knowledge_synthesis:2025-08-01",
  "content": "Identified emerging trend in vector database adoption...",
  "coined_terms": [
    {
      "name": "Vector Database Adoption Wave",
      "confidence": 0.92,
      "supporting_evidence": [
        "hackernews:post:12345678",
        "hackernews:post:12345679"
      ]
    }
  ]
}

Step 3: Use the insights

This synthesized knowledge can then be used to:

1. Guide product development decisions
2. Inform technical blog content
3. Shape hiring strategies
4. Influence technology stack choices

Apply to other domains

The same pattern can be applied to other domains:

1. CRM intelligence: Process customer interactions to identify patterns in successful sales approaches
2. Project performance: Analyze completed projects to extract best practices and optimal team structures
3. Token usage analytics: Monitor blockchain transactions to understand platform dynamics

Each use-case follows the same pattern:

1. Ingest and process raw data
2. Structure it into the observation_log
3. Allow the brain to synthesize new knowledge
4. Apply the insights to improve decision-making

Wrap

The power of this system lies in its ability to continuously learn and adapt. By maintaining an append-only log with flexible data structures, we create a foundation for true machine learning. The system doesn't just store information - it actively synthesizes new knowledge and applies it to improve outcomes.

The key to success is starting with a clear use-case and following the pattern of data ingestion, processing, synthesis, and application. This creates a virtuous cycle where the system gets smarter with each new piece of information it processes.

Next: Access brainery use-cases