🍔🧠 How DropBox Built Multimedia Search for 700M Users

Happy Monday! ☀️

Welcome to the 489 new hungry minds who have joined us since last Monday!

If you aren't subscribed yet, join smart, curious, and hungry folks by subscribing here.

This week I also include a special recommendation newsletter if you want to learn system design with visuals 👇️ 

📚 Software Engineering Articles

• This product finally solved DevOPS

• Learn how a rate limiter works with this practical system design guide

• Build the world's fastest VIN decoder from scratch

• Smart cache warming strategies to boost application performance

• How to handle duplicates in retry mechanisms

• N+1 query problem explained and solved in 4 minutes

🗞️ Tech and AI Trends

• Reddit transforms into a search engine powerhouse

• Anthropic blocks OpenAI from accessing Claude models

• Google launches Gemini Deep Think for parallel reasoning

👨🏻‍💻 Coding Tip

• Use partial indexes in PostgreSQL to optimize query performance and reduce storage

Time-to-digest: 5 minutes

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How Dropbox Built Multimedia Search For Dash 🫨

Searching through thousands of unlabeled images and videos is a nightmare for knowledge workers. Dropbox tackled this by building a scalable multimedia search system that makes finding visual content as easy as finding text documents in their universal search product, Dash.

The challenge: Build a cost-effective system that can process and search through massive amounts of media files while maintaining low latency and high relevance, despite limited metadata and heavy compute requirements.

Implementation highlights:

• Metadata-first indexing: Extract lightweight features first (file paths, EXIF data) to enable basic search with minimal overhead

• Just-in-time previews: Generate previews on-demand rather than upfront to optimize storage and compute costs

• Location-aware queries: Built custom geocoding logic to enable searching by location where photos were taken

• Parallel processing: Run preview generation, ranking, and permission checks concurrently to minimize latency

• Smart caching: Store previews for 30 days and leverage existing Dropbox infrastructure for efficiency

Results and learnings:

• Scalable performance: Successfully processes 97% of media files while maintaining responsive search

• Cost optimization: Achieved 3x storage efficiency for images and 13x for video compared to raw storage

• Developer velocity: Parallel workflows and clear API boundaries enabled faster team execution

This solution shows how thoughtful architecture decisions around caching, parallel processing, and metadata extraction can make multimedia search both performant and cost-effective.

ESSENTIAL (testing gone wrong)
The #1 Mistake in Unit Testing (and How to Fix It)

ESSENTIAL (retry, retry, oops)
Retries Have an Evil Twin: Duplicates

ESSENTIAL (metrics go brrr)
Essential System Design Performance Metrics

ARTICLE (vim-wizardry unlocked)
Why I Switched to Vim Keybindings

GITHUB REPO (trade like a boss)
A high-performance algorithmic trading platform and event-driven backtester

ARTICLE (database drama solved)
What is the N+1 Query Problem and How to Solve it?

ARTICLE (claude’s coding spree)
6 Weeks of Claude Code

ARTICLE (chrome goes zoom)
Making Sense of the Performance Extensibility API

ARTICLE (AI hype debunked)
No, AI is not Making Engineers 10x as Productive

ARTICLE (flashy list magic)
FlashList v2: A Ground-Up Rewrite for React Native's New Architecture

Want to reach 190,000+ engineers?

Let’s work together! Whether it’s your product, service, or event, we’d love to help you connect with this awesome community.

🔍 Reddit Aims to Unify Search Interface in Bid to Rival Major Search Engines (2 min)

Brief: Reddit is working on a unified search interface as part of its strategy to compete with dominant search engines, potentially leveraging its vast community-driven content for better results.

🤖 Anthropic Blocks OpenAI’s Access to Claude Models in Legal Dispute (2 min)

Brief: Anthropic has revoked OpenAI’s access to its Claude AI models amid an escalating legal dispute, leaving OpenAI to seek alternatives for its AI research and services.

🤖 Google Unveils Gemini DeepThink AI for Parallel Reasoning (2 min)

Brief: Google introduces Gemini DeepThink, an AI model designed to evaluate multiple ideas simultaneously, enhancing reasoning and problem-solving efficiency.

🤖 OpenAI Launches Customizable Open-Weight AI Models (2 min)

Brief: OpenAI releases gpt-oss-120b and gpt-oss-20b, two open-weight models under Apache 2.0 license, designed for agentic tasks, customization, and commercial use with full chain-of-thought reasoning.

🤖 GPT-5 Arrives: Faster, Cheaper, and More Reliable Than Ever (4 min)

Brief: OpenAI's GPT-5 offers three model tiers (regular, mini, nano) with competitive pricing and reduced hallucinations, making it a strong contender in the AI race.

🤖 OpenAI’s GPT-OSS Models Now Available on AWS (4 min)

Brief: AWS adds OpenAI’s open-weight GPT-OSS-120B and GPT-OSS-20B models to Bedrock and SageMaker, enabling developers to build AI applications with full infrastructure control and advanced reasoning capabilities.

This week’s coding challenge:

This week’s tip:

Create efficient database indices by using partial indexes with WHERE clauses to significantly reduce index size and improve query performance on specific data subsets. PostgreSQL's partial indices allow you to create smaller, more focused indexes that only include rows matching certain conditions.

Wen?

• Large tables with skewed data: When certain status values represent a small, frequently-queried subset of all rows.

• Time-series data management: Index recent data more aggressively while maintaining lighter indices for historical data.

• Multi-tenant systems: Create focused indices for high-traffic customers while keeping the overall index footprint manageable.

“The harder you work, the harder it is to surrender.”
Vince Lombardi

That’s it for today! ☀️

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Thanks for spending part of your Monday morning with Hungry Minds.
See you in a week — Alex.

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