Meta Engineering Blog

What you will find there

Meta publishes on engineering.fb.com across infrastructure, AI, security, data, networking and open source. Recent posts (checked September 2026) give a feel for the range:

• ZGateway: learnings from putting a proxy in front of ZippyDB, Meta's distributed key value store.
• Open sourcing Rebalancer, a library for solving assignment problems such as placing shards on servers.
• MetaRoCE, an RDMA transport designed for AI scale Ethernet networks, and MTIA 300, Meta's training chip.
• Inside Petal, a transoceanic subsea cable project.
• Scam Alert on WhatsApp, built with end to end encryption and verifiability guarantees.
• Several posts on ads ranking architecture and training large ads models efficiently.

Older posts cover classics such as TAO (the social graph store), memcache at scale, and many open source projects that came from Meta, including React, PyTorch and RocksDB.

Why it is worth reading

• Scale you cannot get elsewhere. Few organisations operate at this size, and the posts often explain the constraint that forced a design.
• Migration stories. The best posts describe replacing a critical system without downtime. Those lessons transfer directly to much smaller companies.
• Open source context. When Meta releases a tool, the blog post usually explains the problem it was built for, which helps you judge whether you need it.

How to use it for learning

The blog works best as case study material once you know the vocabulary. A useful exercise:

1. Read a post about a storage or caching system.
2. Close it and redraw the architecture from memory.
3. Compare. The gaps show you what to study next (consistency models, sharding, replication).

Pick infrastructure posts over product announcements when you are short on time.

Weak spots

• Uneven accessibility. Some posts, especially on AI hardware and networking, assume specialist knowledge. Skip those if they are not your field.
• Selective detail. Posts describe the approach and results but rarely every trade off or failure.
• Volume. Several posts a week at times, so following everything is not realistic. Use the category filters.

Common questions

Is this good for system design preparation? Yes, as real world examples after you have learned the fundamentals. Posts on caching, storage and messaging are the most relevant.

Where should I start? Search for posts about ZippyDB, TAO or memcache for the most instructive storage stories.

Is it only about AI now? No. AI is prominent, but storage, networking, security and developer tooling still appear regularly.

Pair it with

• Designing Data-Intensive Applications for the theory behind the storage posts.
• Papers We Love for Meta's academic papers, such as those on TAO and memcache.

Best for: backend and infrastructure engineers who already know the fundamentals and want detailed case studies at extreme scale.