Trump kills Indian tech dreams

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This week’s coding challenge will test your knowledge of the prefix search and graph algorithms. Are you ready?

Today we will cover:

• Word Search II

• Real-Time Analytics

Read time: under 4 minutes

CODING CHALLENGE

Word Search II

Given an m x n board of characters and a list of strings words, return all words on the board.

Each word must 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 in a word.

Example 1:

Input: board = [["o","a","a","n"],["e","t","a","e"],["i","h","k","r"],["i","f","l","v"]], words = ["oath","pea","eat","rain"]
Output: ["eat","oath"]

Example 2:

Input: board = [["a","b"],["c","d"]], words = ["abcb"]
Output: []

Solve the problem here before reading the solution.

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SOLUTION

To solve this problem, we'll use a Trie data structure to efficiently store and search words. Then, we'll use depth-first search (DFS) to explore the board and find words that exist in the Trie.

Trie will help us quickly prune our search when a prefix doesn't exist in any word. This reduces unnecessary exploration.

Here are the steps:

1. Build a Trie from the input words.

2. For each cell in the board, start a depth-first search.

3. During the search, track the current word and checks if it exists in the Trie.

4. If a complete word is found, add to the result.

Time complexity will be O(m*n*4^L), where m and n are board dimensions, and L is the maximum word length. Space complexity is O(total characters in words).

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SYSTEM DESIGN

Real-Time Analytics

Companies today need to analyze data as it's generated to make quick decisions. Traditional batch processing, where data is analyzed hours or days later, isn't fast enough for many modern applications. This is where real-time analytics pipelines come in.

A real-time analytics pipeline processes data immediately as it arrives. Imagine an e-commerce website that wants to detect fraudulent transactions or a ride-sharing app that needs to adjust prices based on current demand. These applications can't wait for batch processing; they need insights now.

Here's how a typical real-time analytics pipeline works:

1. Data Ingestion: First, we need to collect data from various sources like user clicks, sensor readings, or transaction logs. Tools like Apache Kafka or Amazon Kinesis handle this continuous stream of data.

2. Stream Processing: The incoming data is processed immediately using stream processing engines like Apache Flink or Apache Storm. These tools can perform operations like filtering, aggregation, and transformation on the data in real-time.

3. Storage: Processed data is stored in databases optimized for real-time analytics. Time-series databases like InfluxDB or Prometheus are popular choices as they're designed to handle time-stamped data efficiently.

4. Serving Layer: Finally, the processed data is made available to applications through APIs or dashboards. Tools like Grafana or Tableau can visualize this real-time data.

While real-time analytics pipelines are powerful, they come with challenges. They need to handle high volumes of data without delay, maintain data accuracy, and recover gracefully from failures. The infrastructure is also more complex and expensive compared to batch processing systems.

Here's a comparison of Batch vs Real-Time Analytics:

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NEWS

This Week in the Tech World

Trump's $100K H-1B visa fee sparks tech backlash: New fee for skilled foreign worker visas threatens startup hiring while benefiting big tech giants. Experts warn changes could drive talent to competing countries like China and Canada amid AI arms race concerns.

Nvidia invests $100B in OpenAI infrastructure: Chipmaker commits massive funding for 10-gigawatt data center buildout requiring 4-5 million GPUs. Partnership highlights intimate link between AI boom's biggest drivers as OpenAI serves 700 million weekly users.

India bets $18B on semiconductor ambitions: Country approves 10 chip projects including two fabs to build domestic supply chain. Government pivots strategy to cover all chip sizes while partnering with Taiwan and UK firms despite talent and infrastructure challenges.

Micron surges 46% on AI memory demand: Memory chipmaker beats earnings with $11.32 billion revenue as high-bandwidth memory for AI chips drives triple-digit growth. Stock nearly doubles in 2025 as only US-based memory manufacturer capitalizes on AI boom.

Amazon shuts all UK Fresh stores: E-commerce giant closes 19 Fresh supermarkets in Britain, converting five to Whole Foods. Company shifts focus to online grocery delivery while maintaining 500 Whole Foods locations and touting everyday essentials success.

Apple controls all iPhone chips with A19 Pro: Company debuts custom wireless N1 and modem C1X chips alongside neural-accelerated A19 Pro processor. Architecture change prioritizes on-device AI while reducing dependence on Broadcom and Qualcomm suppliers.

Oracle names co-CEOs as AI boom continues: Cloud infrastructure president Clay Magouyrk and industries president Mike Sicilia replace Safra Catz as company pursues AI-driven growth. Stock up 85% yearly on massive contracted revenue surge to $455 billion.

Apple stock turns positive after iPhone 17 launch: Shares rally 4% to erase 2025 losses following strong demand signals for redesigned iPhone Air. Company becomes last megacap to turn positive as China pre-orders and extended ship times indicate robust sales.

BONUS

Just for laughs 😏

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Cheers,

Sahil + Sarra