Elon killed GPT. But Gemini won.

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This week's problem will twist your brain in knots. But once you understand it, the solution is surprisingly simple. All the best!

Today we will cover:

• Copy List with Random Pointer

• Database Sharding Strategies

Read time: under 4 minutes

CODING CHALLENGE

Copy List with Random Pointer

A linked list of length n is given such that each node contains an additional random pointer, which could point to any node in the list, or null.

Construct a deep copy of the list. The deep copy should consist of exactly n brand new nodes, where each new node has its value set to the value of its corresponding original node. Both the next and random pointer of the new nodes should point to new nodes in the copied list such that the pointers in the original list and copied list represent the same list state. None of the pointers in the new list should point to nodes in the original list.

For example, if there are two nodes X and Y in the original list, where X.random --> Y, then for the corresponding two nodes x and y in the copied list, x.random --> y.

Return the head of the copied linked list.

The linked list is represented in the input/output as a list of n nodes. Each node is represented as a pair of [val, random_index] where:

val: an integer representing Node.val

random_index: the index of the node (range from 0 to n-1) that the random pointer points to, or null if it does not point to any node.

Your code will only be given the head of the original linked list.

Example 1:

Input: head = [[7,null],[13,0],[11,4],[10,2],[1,0]]
Output: [[7,null],[13,0],[11,4],[10,2],[1,0]]

Example 2:

Input: head = [[1,1],[2,1]]
Output: [[1,1],[2,1]]

Solve the problem here before reading the solution.

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SOLUTION

To solve this problem, we'll use a hash map to create a deep copy of the linked list while maintaining the original list's structure. The hash map will help us map original nodes to their corresponding new nodes.

We'll do this in two passes:

1. First, create new nodes and map original nodes to their copies.

2. Then, set the next and random pointers for the new nodes.

This approach ensures that we create a completely independent copy of the original linked list, with new nodes that have the same values and pointer relationships.

The time complexity is O(n), where n is the number of nodes in the linked list. We make two passes through the list.

The space complexity is also O(n) due to the hash map storing node mappings.

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SYSTEM DESIGN

Database Sharding Strategies

As your application grows, a single database server might struggle to handle all the data and user requests. Database sharding solves this by splitting your data across multiple servers, but choosing the right sharding strategy is crucial for performance.

Range-based Sharding divides data based on ranges of a specific field. For example, if you're storing user data, you might put users with IDs 1-1000000 on one server, 1000001-2000000 on another, and so on. This strategy works well when you need to retrieve data within specific ranges, like finding all orders from the last month. However, if the chosen ranges aren't balanced, some servers might end up with more traffic than others.

Hash-based Sharding uses a hash function to determine which server stores each piece of data. When storing user data, you might hash the user ID and use the result to pick a server. Hash-based sharding typically distributes data more evenly than range-based sharding, but it makes range queries harder since related data might be spread across different servers.

Geographic Sharding stores data on servers physically closest to where it's most frequently accessed. For a social media platform, European users' data might be stored on servers in Europe, while Asian users' data stays on Asian servers. This reduces latency but can complicate data access when users interact across regions.

Here's a comparison of these strategies:

The choice of sharding strategy depends on your specific needs. Range-based sharding works well for time-series data, hash-based sharding is great for evenly distributed workloads, and geographic sharding shines in global applications where latency matters.

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NEWS

This Week in the Tech World

Gemini 3 Released: Google has released Gemini 3, its most powerful AI model yet. The update brings advanced reasoning, autonomous coding tools, and interactive search.

xAI Launches Grok 4.1: xAI released Grok 4.1 with improved reasoning and reduced hallucinations. The update adds an Agent Tools API for building autonomous agents.

OpenAI Releases GPT-5.1: OpenAI has launched GPT-5.1, giving users more control over ChatGPT's tone and style. The update aims to refine the AI's personality and responses.

Google Debuts Antigravity: Google unveiled Antigravity, a new agentic development platform. The tool focuses on coding productivity over hype, offering robust autonomous help.

Chrome Zero-Day Found: Google warns users to update Chrome immediately to fix a severe zero-day flaw. Attackers are actively exploiting the vulnerability in the wild.

LeCun Leaves Meta AI: Meta confirmed AI Chief Yann LeCun is leaving to found a startup. The move marks a significant shift for Meta's AI leadership and future strategy.

BONUS

Just for laughs 😏

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Cheers,

Sahil + Sarra