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📚 Software Engineering Articles
Master these 7 must-know patterns for system design interviews
Built a React framework 4x faster than Next.js
5 proven techniques to write maintainable code that lasts
Learn these LLM terms explained in simple English
HTTP complexity revealed: beyond the basics
🗞️ Tech and AI Trends
AWS CEO slams AI replacing juniors as "dumbest idea"
Waymo gets permit to test self-driving cars in NYC
Meta freezes AI hiring amid market concerns
👨🏻💻 Coding Tip
Use awk with custom field separators to extract data from logs
Time-to-digest: 5 minutes
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Reddit built a sophisticated notification system that processes millions of posts daily to deliver personalized push notifications at scale. The system combines causal modeling, real-time retrieval, and deep learning to ensure users receive relevant content without notification fatigue.
The challenge: Balance user engagement with notification fatigue while processing millions of posts in real-time and maintaining high personalization accuracy.
Implementation highlights:
Smart budgeting: Uses causal modeling to determine optimal daily notification limits per user
Two-tower retrieval: Implements fast candidate selection using embedding-based similarity matching
Multi-task learning: Employs deep neural networks to predict multiple engagement signals simultaneously
Dynamic reranking: Applies product-driven adjustments to maintain content diversity and freshness
Queue-based architecture: Ensures reliable delivery through asynchronous processing pipeline
Results and learnings:
Achieved real-time processing of millions of daily posts
Maintained high user engagement while minimizing notification fatigue
Successfully scaled to tens of millions of users with personalized delivery
Reddit's approach shows that building effective notification systems requires more than just technical prowess - it needs deep understanding of user behavior and engagement patterns. Their multi-stage pipeline demonstrates how to balance ML sophistication with practical product needs.
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Brief: AWS CEO Matt Garman calls replacing junior employees with AI "the dumbest thing," arguing they’re cost-effective and key to future skill development, while advocating for AI as a tool for learning, not replacement.
Brief: AWS CEO Matt Garman slams the idea of replacing junior employees with AI, arguing they’re cost-effective and critical for long-term skill development, while advocating for AI as a training tool instead.
Brief: AGENTS.md is emerging as the standardized Markdown file for AI coding agents, providing project-specific instructions to complement traditional READMEs and streamline collaboration between developers and AI helpers.
Brief: Meta halts AI hiring after market turmoil and concerns over overinvestment, reversing its aggressive talent acquisition strategy that included $1B offers to top researchers.
Brief: Waymo secures critical permits to launch commercial autonomous rides in New York City, marking Alphabet's first robotaxi expansion to a dense urban environment.
This week’s coding challenge:
This week’s tip:
Use awk pattern matching with custom field separators to extract structured data from messy logs using Extended Regular Expressions (ERE). The -F flag sets field separators, while pattern blocks like /pattern/{action} filter lines before processing.
Wen?
Legacy system analysis: Parse inconsistent log formats without writing custom parsers.
Quick data extraction: Pull specific fields from JSON-like logs without full parsing overhead.
Real-time monitoring: Filter and transform log streams on-the-fly in monitoring pipelines.
“We cannot change anything unless we accept it.”
Carl Jung
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