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📚 Software Engineering Articles
The fastest text-to-speech API is free for 1000 minutes
Deep dive into distributed systems fundamentals and patterns
How Reddit modernized its comment infrastructure for better scalability
Pragmatic guide to evaluating LLMs for developers
Learn 5 engineering lessons from building Postgres with Rust
Service discovery at LinkedIn scales across multiple languages
🗞️ Tech and AI Trends
OpenAI sounds alarm as Google makes significant AI advances
Zig leaves GitHub, citing Microsoft's overwhelming AI focus
AWS launches three autonomous AI agents for developers
👨🏻💻 Coding Tip
Implement consistent hashing with virtual nodes to prevent hotspots in distributed systems
Time-to-digest: 5 minutes
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Pinterest built a machine learning system that uses direct user feedback to improve content recommendations across their platform. By collecting and analyzing survey data from users (Pinners), they created a model that can identify high-quality content that resonates with their audience.
The challenge: Building a scalable system that can accurately predict content quality based on limited survey data while maintaining personalization across billions of images.
Implementation highlights:
Strategic data collection: Gathered 10+ ratings per image across 5k pins from top categories
Pairwise ranking approach: Transformed 5k samples into 2.5M training pairs for better learning
L1 category separation: Trained model to compare visual quality within same interest verticals
Lightweight architecture: Used simple neural network with 92k parameters for scalable inference
Variable margin loss: Adapted loss function based on rating variance for noise handling
Results and learnings:
High accuracy: Achieved 90%+ predictions within one standard deviation of user ratings
Cross-platform wins: Improved engagement metrics across Homefeed, Search and Related Pins
Business impact: Reduced "low quality" sessions while increasing successful user interactions
The key takeaway is that incorporating direct user feedback through surveys can significantly improve recommendation systems. Pinterest's approach shows that you don't need complex architectures to achieve meaningful results - sometimes simply listening to your users is the best strategy.
ARTICLE (copilot to the rescue)
How to use GitHub Copilot Spaces to debug issues faster
ARTICLE (bug hunting adventure)
Hunting a production-only proxy bug in SvelteKit
ARTICLE (ninja engineer)
Why I ignore the spotlight as a staff engineer
ARTICLE (teaching robots to draw)
Teaching an LLM a Niche Diagraming Language
ARTICLE (ai convert confession)
AI skeptic to AI pragmatist
ESSENTIAL (measure your robot)
A pragmatic guide to LLM evals for devs
ARTICLE (no framework drama)
Vanilla CSS is all you need
ESSENTIAL (think before you bot)
Critical Thinking during the age of AI
ARTICLE (postgres clone chronicles)
5 Engineering Lessons from Replicating Amazon RDS Postgres with Rust
ESSENTIAL (graph quest begins)
Fundamental Graph Algorithms - Part I: BFS
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Brief: Google challenges AI leaders with Gemini 3 and TPU sales, putting pressure on Nvidia's hardware dominance and OpenAI's model supremacy, while ChatGPT's 800M users remain a resilient moat against competition.
Brief: Netflix announces historic $82.7B acquisition of Warner Bros., uniting iconic franchises like Harry Potter, Game of Thrones, and DC Universe with Netflix's portfolio, while Warner Bros. maintains current operations and HBO integration following Discovery's separation in 2026.
Brief: The Zig programming language migrates to Codeberg from GitHub, citing Microsoft's AI obsession and major technical issues including an unresolved CPU-draining bug that remained unfixed for months while the platform focused on AI initiatives.
Brief: AWS unveils three new frontier AI agents to enhance its Kiro IDE, promising to fix common AI coding issues by offering autonomous DevOps management and code security, claiming a project completion speed boost from 18 months to 76 days.
Brief: Cloudflare introduces its new connectivity cloud offering 60+ networking and security services through a unified platform, aiming to help companies connect, protect, and build everywhere with enhanced performance and security.
This week’s coding challenge:
This week’s tip:
Implement consistent hashing with virtual nodes and bounded load to prevent hotspots while maintaining even distribution. Use jump consistent hash for deterministic node assignment with minimal remapping.
Wen?
Distributed caches: Minimize cache misses during node additions/removals while preventing load concentration
Sharded databases: Balance query load across shards while maintaining data locality and avoiding hotspots
Load balancer backends: Distribute traffic evenly while handling server capacity differences and failures
"I've learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel."
Maya Angelou
That’s it for today! ☀️
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See you in a week — Alex.
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