Amazon Layoffs Hit Engineers

Welcome back!

Today’s problem is a popular one at FAANG interviews. Get ready to brush up on your graph algorithms!

Here’s what to expect this week:

• Course Schedule problem

• Batch vs Stream processing

Read time: under 4 minutes

CODING CHALLENGE

Course Schedule

There are a total of numCourses courses you have to take, labeled from 0 to numCourses - 1. You are given an array prerequisites where prerequisites[i] = [ai, bi] indicates that you must take course bi first if you want to take course ai.

For example, the pair [0, 1], indicates that to take course 0 you have to first take course 1.

Return true if you can finish all courses. Otherwise, return false.

Example 1:

Input: numCourses = 2, prerequisites = [[1,0]]
Output: true
Explanation: There are a total of 2 courses to take. 
To take course 1 you should have finished course 0. So it is possible.

Example 2:

Input: numCourses = 2, prerequisites = [[1,0],[0,1]]
Output: false
Explanation: There are a total of 2 courses to take. 
To take course 1 you should have finished course 0, and to take course 0 you should also have finished course 1. So it is impossible.

Solve the problem here before reading the solution.

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SOLUTION

To solve this problem, we'll use a depth-first search (DFS) approach to detect cycles in the course dependency graph. If there's a cycle, it means we can't finish all courses.

We'll represent the graph using an adjacency list. Then, we'll perform DFS for each course. During DFS, we'll keep track of visited courses and courses in the current path. If we encounter a course that's already in the current path, we've found a cycle.

The time complexity of this solution is O(V + E), where V is the number of courses and E is the number of prerequisites. That’s because we visit each course once and each prerequisite once.

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SYSTEM DESIGN

Batch vs Stream processing

There are two popular methods to process data: Batch processing and Stream processing. Let’s understand the main differences between the two.

In Batch processing, we collect data over time and processes it in large chunks at set intervals. This approach shines when you need to analyze historical data, generate periodic reports, or perform large-scale data migrations. It's more cost-effective and simpler to implement since you do not need constant processing power.

Stream processing, on the other hand, handles data in real-time as it arrives. Think of monitoring live traffic or detecting fraud in banking transactions. It's ideal when immediate insights are needed. Stream processing requires more complex infrastructure and higher costs to maintain continuous processing capabilities.

Batch processing often uses tools like Apache Spark or Apache Airflow, while stream processing relies on platforms like Apache Kafka or RabbitMQ.

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NEWS

This Week in the Tech World

Amazon Layoffs Hit Engineers: Amazon's restructuring is cutting 2,400+ jobs in Washington, with 40% affecting engineering roles as the company pivots resources toward AI.

OpenAI Launches ChatGPT Health: OpenAI announced a dedicated Health space in ChatGPT for medical record integration. 230M users ask health questions weekly.

Nvidia Unveils Vera Rubin AI Platform: Nvidia announced the Vera Rubin architecture at CES 2026, promising up to 5x inference performance over Blackwell. Production begins H2 2026.

Samsung Shows Galaxy Z TriFold: Samsung demoed its dual-hinge foldable at CES, featuring a 10-inch display when unfolded. Expected US launch Q1 2026 at ~$2,500.

LEGO Launches Smart Bricks: LEGO unveiled Smart Play at CES with chips smaller than a stud. Bricks communicate via BrickNet without apps. Star Wars sets launch March 1.

Dell Revives XPS Brand: Dell brought back XPS at CES with redesigned 14" and 16" laptops running Panther Lake. Physical function keys return after user backlash.

ChatGPT Leaving WhatsApp: OpenAI announced ChatGPT will no longer be available on WhatsApp after January 15, 2026, due to policy changes from Meta.

Intel Launches Panther Lake CPUs: Intel's Core Ultra Series 3 debuts on the 18A node, claiming 77% faster gaming vs Lunar Lake. Stock jumped 6.5% on the news.

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Cheers,

Sahil + Sarra