This article explores the use of bloom filters for creating a space-efficient full text search index. While they work well for small document sets, scaling them to larger corpuses reveals limitations in query performance and space efficiency compared to traditional inverted indexes. The author discusses potential solutions and why they ultimately fall short.

The article discusses how vibe coding, common in working with LLMs, is evaluated not just by speed but also by cost in terms of tokens used. It highlights the balance between fast iterations and their associated costs, suggesting that effective vibe coders will focus on minimizing token consumption while achieving results. The piece warns against turning creative exploration into a mere efficiency metric.

Google DeepMind has unveiled AlphaEvolve, an advanced AI agent capable of writing its own code and developing complex algorithms, resulting in significant computing cost savings. The system has already optimized various aspects of Google's infrastructure, improving efficiency and solving longstanding mathematical problems.