software-engineering.
106 writings found
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How Meta's ZGateway Proxy Solves the Million-Client Problem
Understanding how a shared proxy tier manages connection meshes, enables cross-client batching, and scales reliability at hyperscale infrastructure.
The Proxy Layer Pattern: Where Infrastructure Gets Smart
How Meta's ZGateway shows why interposing a managed tier between clients and backends solves problems no individual client library can.
Why Proxies Win at Scale: Learning from Meta's ZGateway
How interposing a stateless proxy tier between millions of clients and a shared backend solves reliability and efficiency problems that client libraries cannot.
MetaRoCE: Rethinking Network Transport for AI at Scale
Meta's new MetaRoCE protocol redesigns Ethernet transport for million-GPU clusters, prioritizing edge intelligence over fabric control. What this means for infrastructure engineers.
Why Lines of Code Still Matter When AI Writes Your Software
AI coding agents are changing productivity metrics. We need new frameworks to measure what agents actually deliver, and what gets lost in the speed.
Proving AI Agents Work: Why Lean Language Matters Now
Leo de Moura on using Lean to verify AI agent correctness. How automated reasoning and probabilistic models converge to build trustworthy systems.
Why Lines of Code Still Matter for AI-Assisted Development
Exploring how coding agents change productivity metrics, conceptual integrity, and why team structure still matters in the age of AI.
Why Lines of Code Still Matter When Agents Write Them
AI coding agents are changing productivity metrics. When agents can produce 1000 lines of debugged code daily versus 50-200 for humans, LOC becomes meaningful again - but only with discipline.
Lines of Code Still Matter When Agents Write Them
Why measuring productivity by lines of code makes sense with AI coding agents, and the real challenge isn't speed but maintaining conceptual integrity as code grows cheaper to write.
WhatsApp's On-Device ML for Scam Detection: A Privacy-First Approach
WhatsApp's new Scam Alert uses on-device ML to catch scams without accessing message content. Here's why this architecture matters for privacy-conscious development.
Lines of Code Still Matter When Agents Write Them
Why measuring productivity by lines of code makes sense for AI coding agents, and why teams still need humans to maintain conceptual integrity.
WhatsApp's On-Device ML Scam Detection Sets a Privacy Precedent
WhatsApp's new Scam Alert uses on-device ML to catch fraud while preserving end-to-end encryption. Here's what this means for privacy-first AI.
WhatsApp's On-Device ML Model: Privacy-First Scam Detection
WhatsApp launches Scam Alert, an on-device ML model that detects scams without sending message content to servers. What this means for privacy-preserving AI.
AI Context Architecture: Why Boundaries Make Better Agents
Understanding how context architecture removes ambiguity from AI agents and creates predictable, safe outcomes for developers building with LLMs.
WhatsApp's On-Device Scam Detection Shows Privacy and Security Can Coexist
WhatsApp's new Scam Alert uses on-device ML to detect fraud without compromising end-to-end encryption. What this means for privacy-first security architecture.