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  • 🍔🧠 Graph Algorithms: Robinhood's Fraud-Fighting

🍔🧠 Graph Algorithms: Robinhood's Fraud-Fighting

PLUS: Ray-Ban's multimodal AI glasses 🕶️, locally run large language models ⚡, building a meme search engine 🔍

Author

Alex Zajac
April 29th, 2024

Happy Monday! ☀️

Welcome to the 311 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’S MENU 🥗

  1. 📚 Dropbox's scaling journey to 100k. Build a meme search engine with vector databases. Launch AI models locally with minimal setup.

  2. 🗞️ Ray-Ban smart glasses enhanced with multimodal AI. Biden signs TikTok ban if ByteDance doesn't sell. Tesla teases Uber's rival self-driving ride-hailing app.

  3. 👨🏻‍💻 Quick byte: MongoDB partial indexing optimizes large dataset queries.

Reading time: 5 minutes

Food for Thought
A mindset, an example, and an action item to start the week
❝

‘I find that the harder I work, the more luck I seem to have.’

Thomas Jefferson

Mindset: Luck favors the persistent; relentless effort begets fortuitous opportunities.

Example: Apple's success stemmed from Steve Jobs' unwavering commitment to innovation.

Action item: Identify one task you've been procrastinating on and devote focused effort to it today.

The Rabbit Hole
Deep dives, trends, and resources curated to stay ahead

How Robinhood uses Graph Algorithms to prevent Fraud

How Robinhood uses algorithms like Connected Components, PageRank and more. Plus, what makes an outstanding programmer, four important software design principles and more.

HeartBeats: How Distributed Systems Stay Alive

#8 System Design - Heartbeats

How Dropbox Scaled to 100 Thousand Users in a Year After Launch

#43: Break Into Early Architecture of Dropbox (6 minutes)

Never struggle to give feedback again (even to your manager)

7 simple steps to tackle any feedback situation

How to scale databases

In this issue, we will discuss how to solve one of the most common software bottlenecks in production: database scalability. There are two types of scaling strategies: vertical and horizontal. Also, we will see how Figma scaled their Postgre database to almost infinite scalability

Practical Learning Strategies for Engineers

How I Improved my Learning Processes

LUC #51: Domain-Driven Design Demystified: Bridging Development and Business Needs

Plus, how elasticsearch works, what makes an API RESTful, and server-sent events explained

💾 SIDE DISHES 💾

ESSENTIAL (containers)
A tech deep dive on Containers

ARTICLE (ChatGPT)
How does ChatGPT work?

The Weekly Digest
Software, AI, and startup news worth your time

Brief: Ray-Ban Meta smart glasses now integrate advanced multimodal AI technology, allowing users to interact with their surroundings through real-time data processing and interpretation for various tasks.

Brief: Microsoft introduces Phi-3 Mini, a compact AI model with 3.8 billion parameters, offering efficiency and high performance in comparison to larger models, poised to disrupt the AI landscape.

Brief: US Senate and House pass a bill forcing TikTok's Chinese owner ByteDance to sell within 270 days or face US market expulsion amid national security concerns.

Brief: Synthesia's innovative use of generative neural networks propels the creation of lifelike AI avatars for corporate communication, leveraging data from 1,000 professional actors to enhance content engagement.

Brief: Tesla unveils a glimpse of its upcoming self-driving ride-hailing app, integrating Uber-like features with unique capabilities for autonomous vehicles, indicating progress towards its long-anticipated 'Tesla Network'.

Brief: Apple is diving into the AI realm by crafting its bespoke AI server processor utilizing TSMC's cutting-edge 3nm process, set for production by late 2025.

The Quick Byte
One coding tip because you’re technical after all

This week’s coding challenge:

Coding Challenge #59 - Netcat

This challenge is to build your own version of netcat.

This week’s tip:

MongoDB's dynamic partial indexing feature allows for highly efficient data retrieval by creating indexes that only include documents that meet a specified filter condition. This reduces index size and improves performance for queries that match the filter.

Wen?

  • Optimizing Query Performance: Especially useful in large collections where only a subset of documents is frequently queried.

  • Reducing Resource Usage: Minimizes the memory and storage footprint of indexes by excluding irrelevant documents.

  • Conditional Data Access: Ideal for scenarios where access patterns are conditional, such as temporal data or multi-tenant systems where data relevancy changes over time.

Why?

  • Efficiency in Large Datasets: Significantly improves query performance and reduces operational costs in large datasets.

  • Resource Optimization: Reduces the overhead of maintaining large indexes by focusing only on relevant subsets of data.

  • Scalability and Flexibility: Offers a scalable indexing strategy that adapts to dynamic conditions and evolving data access patterns.

Burp-A-Laugh
The most important meal of your day

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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