Meta cuts 10% of staff

Welcome back!

This week’s problem uses two different algorithmic patterns in the same problem. Let’s see if you can solve it.

Today we will cover:

• Word Search

• Data Lake vs Data Warehouse 

Read time: under 4 minutes

CODING CHALLENGE

Word Search

Given an m x n grid of characters board and a string word, return true if word exists in the grid.

The word can be constructed from letters of sequentially adjacent cells, where adjacent cells are horizontally or vertically neighboring. The same letter cell may not be used more than once.

Example 1:

Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCCED"

Example 2:

Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCB"
Output: false

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 with backtracking. The idea is to start at each cell of the board and try to match the characters of the word by exploring adjacent cells recursively.

We'll define a helper function that performs the DFS. This function will check if the current cell matches the current character of the word and then explore its neighboring cells. We'll use a visited set to ensure we don't use the same cell more than once.

The time complexity is O(m * n * 4^L), where m and n are the dimensions of the board, and L is the length of the word. In the worst case, we might explore 4 directions for each character of the word.

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SYSTEM DESIGN

Data Lake vs Data Warehouse

Organizations collect massive amounts of data, but storing and analyzing it efficiently can be challenging. Two popular solutions are Data Lakes and Data Warehouses. Let's understand when to use each one.

A Data Warehouse is like a well-organized library where data is processed, structured, and stored in a specific format. Before data enters a warehouse, it goes through ETL (Extract, Transform, Load) process - cleaning, formatting, and organizing the data respectively. This makes it easy to analyze but takes time to set up and requires you to define the structure upfront.

A Data Lake, on the other hand, is more like a storage container where you can dump any type of data in its raw form - structured, semi-structured, or unstructured. There's no need to process the data before storing it. This makes it quick to set up and flexible for future needs, but analyzing the data requires more work later.

Data Warehouses are great when you know exactly what kind of analysis you need. For example, if you're tracking sales performance, a Data Warehouse can efficiently store and process sales data in a way that makes reporting easy.

Data Lakes shine when you're not sure how you'll use the data in the future. They're perfect for machine learning projects where you might need raw data in different formats, or when storing data that doesn't fit neatly into tables, like images or text documents.

Here's a comparison of Data Lake vs Data Warehouse:

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NEWS

This Week in the Tech World

Meta Cuts 10% Of Staff: Meta laid off roughly 10% of its global workforce while reassigning 7,000 employees to new AI workflow initiatives and flattening management layers. Restructuring touched about 20% of the company in total.

Karpathy Joins Anthropic: OpenAI co-founder and former Tesla AI lead Andrej Karpathy is heading to Anthropic to build a team using Claude to accelerate pretraining research, the latest in a string of xAI and OpenAI alumni decamping to the Claude maker.

Google I/O Unveils Gemini 3.5: Google rolled out Gemini 3.5 Flash with agentic upgrades and 4x faster output, debuted personal agent Gemini Spark for AI Ultra subscribers, and previewed Android XR glasses with hardware partners Samsung, Warby Parker, and XREAL.

Anthropic Locks In Colossus: Anthropic will pay xAI $1.25B per month through 2029 for all compute at the Colossus 1 data center in Tennessee, funneling over $40B to Elon Musk's AI outfit as Grok usage slumps and frees up servers for a rival.

Huawei AI Chip Revenue Surges: Huawei's AI chip business is on track to grow 60% to about $12B as Beijing steers domestic buyers away from Nvidia, while Tencent and Alibaba talked up their own homegrown silicon and Nvidia's H200 export license to China sits idle.

Radar Joins Unicorn Club: Retail anti-theft and inventory startup Radar, backed by American Eagle CEO Jay Schottenstein, raised a $170M Series B at a valuation north of $1B, co-led by Gideon Strategic Partners and Nimble Partners, with Align Ventures joining the party.

Anthropic Flags Finance Cyber Flaws: Anthropic will brief the Financial Stability Board on cybersecurity vulnerabilities its unreleased Mythos Preview model uncovered across the global financial system, with G20 finance ministries and central banks pulled into the call.

BONUS

Just for laughs 😏

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Sahil + Sarra