How Garbage collection works?

Garbage collection is an automatic memory management feature used in programming languages to reclaim memory that is no longer being used by the program. It frees developers from having to manually allocate and deallocate memory, which can be error-prone.

How Garbage Collection Works in Different Languages

Java

Java provides several garbage collectors optimized for different use cases:

• Serial GC: Best for single-threaded environments or small apps

• Parallel GC ("Throughput Collector"): Performs collection in parallel

• CMS (Concurrent Mark-Sweep) GC: Low-latency, aims to minimize pause times

• G1 (Garbage-First) GC: Balances throughput and latency

• Z GC (ZGC): Low-latency GC for apps requiring large heaps and minimal pauses

Python

Python's garbage collection uses a combination of reference counting and a cyclic garbage collector:

• Reference Counting: Each object has a count of references to it. When the count reaches zero, the memory is freed.

• Cyclic Garbage Collector: Handles circular references that reference counting alone cannot resolve.

Go

Go uses a concurrent mark-and-sweep garbage collector that operates concurrently with the application. This minimizes "stop-the-world" pauses where the application has to be halted for GC.

Key Takeaways

• Garbage collection automates memory management, preventing many memory-related bugs

• Different languages use different GC algorithms optimized for various factors like latency, throughput, and heap size

• Understanding how GC works can help write more efficient and memory-safe code, even in garbage-collected languages

By automating memory management, garbage collection makes development faster and eliminates entire classes of bugs. However, GC is not free - it has an overhead and can cause performance issues if not tuned properly. As a developer, it's useful to understand how GC works in your language to optimize memory usage and minimize GC pause times.

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How Anthropic built Artifact

• Anthropic built an innovative new feature called Artifacts that allows creating websites, code snippets, documents and more using their AI assistant Claude. It was released alongside their latest language model Claude 3.5 Sonnet in June 2024.

• The feature started as a scrappy prototype by a research scientist to speed up testing website generation with Claude. It was then iterated on by a small team including a product designer and engineer to get it production-ready in just 3 months.

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• The team dogfooded the AI assistant extensively during development - using Claude to help generate frontend code, research browser APIs, and rapidly prototype ideas. The more advanced 3.5 Sonnet model pushed them to be more ambitious in what Artifacts could do prior to launch.

• Read further at Pragmatic Engineer.