System Architecture

A breakdown of Anthropic CEO’s “Powerful AI” essay — and how engineers can prepare in a post-AI world

A deep dive into how CDNs work, including a from-scratch implementation of a geo DNS-based CDN with edge caching

webpack is a module bundler. Its main purpose is to bundle JavaScript files for usage in a browser, yet it is also capable of transforming, bundling, or packaging just about any resource or asset.

Beyond protocols: How to rearchitect your business for an AI-first world

This post first appeared on Nick Tune’s Medium page and is being republished here with the author’s permission.Example architecture flow reverse-engineered

From models to systems

An in-depth look at multi-modal perception systems, explaining how vision, language, and sensor fusion enable real-time understanding in modern robots.

While some consider prompting is a manual hack, context Engineering is a scalable discipline. Learn how to build AI systems that manage their own information flow using MCP and context caching.

When millions click at once, auto-scaling won’t save you — smart systems survive with load shedding, isolation and lots of brutal game-day drills.

DIDs that target a distributed ledger face significant practical challenges in bootstrapping enough meaningful trusted data around identities to incentivize mass adoption. We propose a new DID method using a web domain's existing reputation.

We migrated a GPS tracker from Elixir to AnyCable and sped up the infrastructure based on AWS ECS, Fargate, and CloudFormation for Sera.

Have beautiful data in Zarr? Show us on Bluesky!

Explore the journey of building a declarative data stack - from architectural decisions to practical implementation. Learn how to separate business logic from technical implementation using templates, automation, and modern orchestration tools.

On agent orchestration patterns, why design and critical thinking are the new bottlenecks, and whether we should let go of looking at code

How cursor pagination breaks under full-text search, and how to fix it in Quarkus without killing performance or trust

Learn more about an often-overlooked bottleneck in scaled postgres systems: postmaster contention. This deep dive into a production issue highlights how this surfaces and explains the underlying causes.

Making sense of ~5 years of vendor speak, 10 different architectures that are actually the same, semantic layers finally maturing, and a Bartridge in a B-tree.

Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.

Learn what toil is in SRE, how it affects operational work, and why minimizing it is crucial for efficiency and morale.

Google Cloud Bigtable is an HBase-compatible, enterprise-grade NoSQL database with low single-digit millisecond latency and limitless scale.

Why caching is a trade-off, not a solution Browser cache (HTTP headers, service workers, bfcache) CDN caching (edge, invalidation, cache keys) Application-le...

What makes a resilient system? It needs to be strong, but flexible. Pace Layers, sometimes called pace layering, is a framework created by Stewart Brand of the Long Now Foundation that explores how different parts of civilisation change at different speeds. Fast-moving layers like fashion bring novelty and experimentation, while slower layers like culture and nature provide stability and memory. Together, the layers support, reinforce, and challenge each other—creating robust, adaptable societies. Pace layers is a model I have come back to again and again. What is the Pace Layers Framework? The pace layering model has six levels, with each deeper layer moving more slowly than the one above it. In Brand's words: Fast learns, slow remembers. Fast proposes, slow disposes. Fast is discontinuous, slow is continuous. Fast and small instructs slow and big by accrued innovation and occasional revolution. Slow and big controls small and fast by constraint and constancy. Fast gets all our attention, slow has all the power. In short: the fast layers innovate, the slow layers stabilise. Brand argued that all durable dynamic systems have this sort of structure—it’s what makes them adaptable and resilient. According to pace layering, the six layers of pace and size in the working structure of a robust and adaptable civilisation are: Fashion/Art Commerce Infrastructure Governance Culture Nature Pace Layering Example: Electric Scooters An example brings pace layers home to me. Here, I consider electric scooters, but you can take almost any innovation and think through the layers. Fashion/Art New scooter designs, colours, marketing campaigns, and influencer hype cycles. These come and go quickly—some stick, most fade. Commerce Scooter rental startups, pricing models, venture capital funding, and competition with bikes, cars, and other transport. Commerce runs slower than fashion, but still moves quickly through experimentation. Infrastructure Charging docks, scooter lanes, designated parking areas, and integration with city planning—all these take time. Cities and transport systems have to adapt, if electric scooters persist, for them to thrive rather than die out. Governance The laws and regulations surrounding scooters often lag behind the pace of commerce, including speed limits, taxes, penalties, incentives, safety requirements, age restrictions, or bans. Governance responds as the effects of scooters unfold. Culture Culture in the deep sense of how this changes mobility and ideas around travel. It may affect socialising, where people work and live, and what we choose to do as a society. Culture evolves slowly as habits and norms change in tandem with governance and infrastructure. Nature The environmental realities of scooters vs other transport means. Batteries, natural resources, long-term sustainability, and the effect on the climate. Applying the Pace Layers Model to AI Applying pace layers to the growth and development of Artificial Intelligence (AI) is also instructive: Fashion: AI tools and interfaces change daily. Commerce: Models and capabilities change weekly or monthly. Companies compete on quarterly and annual cycles. Infrastructure: Investment, data centres and energy generation are being approved and built slowly. Governance: The rules, ethics, court cases, and legal challenges surrounding the regulation of AI content and companies are developing on a case-by-case basis. Culture: How AI changes work, employment, where people live, and power structures are all in progress. Nature: Effects on the planet, energy and climate take longer to play out. AI can feel like it's changing every day, yet its deepest consequences will play out over decades and centuries. Pace Layers and Time Hierarchy In his book The Clock of the Long Now, Brand also discusses time hierarchy in nature. He describes a coniferous forest: The needle changes within a year. The tree crown over several years. The patch over many decades. The stand over a couple of centuries. The forest over a thousand years. The biome over ten thousand years. The faster elements are constrained by the slower ones: the needle by the crown, the crown by the patch, and so on up to the biome. Yet innovation still percolates through the system via evolutionary competition. Occasionally, shocks—fire, disease, human activity—disrupt the whole structure, sometimes all the way to the biome. You can see this idea in my sketch on Time Hierarchy. Considering the Pace Layers Model I find pace layers thinking interesting as a way to differentiate between the fast and slower changes I see around me. It helps me make better sense of the world and see the age-old tussle between innovation and stability. Related Ideas to Pace Layers Also see: Time Hierarchy The Overview Effect The Overton Window: the range of politically acceptable ideas Human pace The Gartner Hype Cycle Marchetti's Constant The Destiny Instinct The S-Curve The Law of Unintended Consequences I revised my original pace layers sketch because I kept discussing it with people and thought it needed a better visual. Read more on Pace Layers at the Long Now

RNGD is FuriosaAI's second AI accelerator. Its Tensor Contraction Processor architecture offers unmatched performance and efficiency for data centers.

Stephen Wolfram explains the science of ruliology--studying what abstract rules do--and integral applications of the science in his own work.

Documentation for Cedar policy language

How we learned to stop worrying and love writeable root filesystems.

So that we may educate as well as horrify: the internals of our new Sprites execution platform.

Goodhart’s Law says that “when a measure becomes a target, it ceases to be a good measure.”

How the Codex CLI and MCP turn the 'stale CMDB' problem into a solved reasoning task—starting with Kubernetes