Building an Open-Source AI Newsletter Engine

The Challenge: Tracking AI Advancements

Keeping up with AI breakthroughs across arXiv, GitHub, and various news sources is a monumental task. Manually juggling 40 browser tabs isn't just inefficient; it's a recipe for a laptop meltdown.

The Solution: AiLert – An Open-Source Answer

To address this, I developed AiLert, an open-source content aggregator leveraging Python and AWS. Here's a technical overview:

Core Architecture

<code># Initial (inefficient) approach
for source in sources:
    content = fetch_content(source)  # Inefficient!

# Current asynchronous implementation
async def fetch_content(session, source):
    async with session.get(source.url) as response:
        return await response.text()</code>

Key Technical Features

1. Asynchronous Content Retrieval

   • Utilizes aiohttp for concurrent requests.
   • Includes custom rate limiting to avoid overwhelming data sources.
   • Robust error handling and retry mechanisms.

2. Intelligent Deduplication

<code>def similarity_check(text1, text2):
    # Embedding-based similarity check
    emb1, emb2 = get_embeddings(text1, text2)
    score = cosine_similarity(emb1, emb2)

    # Fallback to fuzzy matching if embedding similarity is low
    return fuzz.ratio(text1, text2) if score < threshold else score</code>

3. Seamless AWS Integration

   • Leverages DynamoDB for scalable and cost-effective data storage.
   • Employs auto-scaling for optimal performance.

Overcoming Technical Hurdles

1. Memory Management

Initial attempts using SQLite resulted in a rapidly growing 8.2GB database. The solution involved migrating to DynamoDB with strategic data retention policies.

2. Content Processing

JavaScript-heavy websites and rate limits presented significant challenges. These were overcome using customized scraping techniques and intelligent retry strategies.

3. Deduplication

Identifying identical content across various formats required a multi-stage matching algorithm to ensure accuracy.

Join the AiLert Community!

We welcome contributions in several key areas:

<code>- Performance enhancements
- Improved content categorization
- Template system refinements
- API development</code>

Find the code and documentation here:

Code: https://www.php.cn/link/883a8869eeaf7ba467da2a945d7771e2
Docs: https://www.php.cn/link/883a8869eeaf7ba467da2a945d7771e2/blob/main/README.md

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