⚡ DevPulse AI

Automated Tech Intelligence Architecture

Automated Signal Extraction • Multi-Source Ingestion • AI-Ready Digests
"Stop drinking from the firehose. Start analyzing the stream."

---

� The Mission

DevPulse is a high-velocity intelligence engine designed to solve the Information Overload Paradox facing modern engineers and researchers.

In an ecosystem where hundreds of papers, repositories, and releases drop daily, manual tracking is inefficient. DevPulse operates as an autonomous background daemon, scraping, ranking, and synthesizing high-signal updates into a single, structured daily briefing.

🏗️ System Architecture

DevPulse employs a microservices-based crawling and scoring mesh. The system is decoupled into an ingestion layer (FastAPI), a processing layer (Scoring Engine), and a presentation layer (n8n/Email).

graph TD
    subgraph Sources ["📡 Data Ingestion Layer"]
        GH[GitHub Trends]
        HF[HuggingFace Papers]
        PT[PyTorch Ecosystem]
        X[Research Feeds]
    end

    subgraph Cortex ["🧠 Intelligence Cortex (Backend)"]
        API[FastAPI Gateway]
        Worker[Async Crawlers]
        Rank[Heuristic Scoring Engine]
        DB[(SQLite / Postgres)]
    end

    subgraph Delivery ["🚀 Delivery Pipeline"]
        N8N[n8n Workflow Orchestrator]
        LLM[LLM Summarization Node]
        Mail[HTML Digest Generator]
    end

    GH & HF & PT & X --> Worker
    Worker --> Rank
    Rank --> DB
    DB --> API
    N8N -->|Fetch High-Signal Items| API
    N8N --> LLM
    LLM --> Mail
    Mail -->|SMTP| User((End User))

Loading

🧠 Core Systems

1. The Ingestion Mesh (Backend)

Built on FastAPI, the backend acts as the central nervous system.

• Async Collectors: Concurrent workers scrape targeted data sources (GitHub, HF, etc.) without blocking the main event loop.
• Normalization: Diverse data formats (JSON, RSS, HTML) are normalized into a unified Item schema.
• Resilience: Implements retry logic and error bounding for external APIs.

2. Heuristic Scoring Engine

Data is meaningless without prioritization. DevPulse implements a custom ranking algorithm:

• Velocity Tracking: Measures "stars per hour" or "fork velocity" for GitHub repos.
• Impact Weighting: Up-ranks major version releases (e.g., v2.0 > v2.0.1).
• Keyword Resonance: Boosts items matching "LLM", "Agent", "Transformer" (configurable).

3. Orchestration & Delivery (n8n)

We utilize n8n for flexible, low-code workflow orchestration:

• Decoupled Logic: The backend provides raw data; n8n handles the "business logic" of presentation.
• AI Integration: Plug-and-play nodes for Gemini/OpenAI to generate executive summaries of the day's data.
• HTML Injection: Generates clean, mobile-responsive email layouts dynamically.

�️ Tech Stack Specifications

Component	Technology	Role
Core Service	Python 3.10+, FastAPI	API, Data Processing, Scheduling
Server	Uvicorn (ASGI)	High-performance async server
Persistence	SQLite / Postgres (SQLAlchemy)	Relational storage of tech items
Networking	HTTPX	Non-blocking external requests
Orchestrator	n8n (Dockerized)	Workflow automation & email delivery
Infrastructure	Docker Compose	Service definition & networking

� Deployment Sequence

Deploying DevPulse is designed to be a "zero-friction" operation.

Prerequisites

• Docker Engine & Docker Compose
• Optional: OpenAI/Gemini API Key (for summaries)

Installation

1. Clone the Repository

   git clone https://github.com/yourusername/devpulse-ai.git
   cd devpulse-ai

2. Initialize Environment

   cp .env.example .env
   # Edit .env to configure your scraper targets and API keys

3. Ignite Services

   docker compose up -d --build

   Status Report:
   🟢 Backend: http://localhost:8000
   🟢 n8n Workflow: http://localhost:5678

🕹️ API & Usage

The backend exposes a clean REST interface. You can integrate DevPulse into your own dashboards.

Identify Top 10 High-Signal Items:

curl "http://localhost:8000/digest/json?limit=10&threshold=0.8"

Force Trigger Crawl (Admin):

curl -X POST "http://localhost:8000/admin/crawl"

🔭 Future Horizons

DevPulse is evolving from Passive Collection to Active Agentic Analysis.

• Vector Database Integration: Store embeddings of Readmes/Papers for semantic search.
• Agentic Research: "DevPulse, find me all new transformer architectures released this week."
• Personalized Filtering: Per-user interest graphs (e.g., "Only Computer Vision").

---

View Implementation Plan • Report Bug

Designed with ❤️ for the Builders.