Systems | Development | Analytics | API | Testing

Learn how to leverage Bitrise Build Cache to reuse build outputs and compilation data in your React Native projects. This guide from Lex Zavala walks you through the essential steps for configuring Build Cache for React Native: adding the required workflow steps, configuring command-line prefixes for better data segmentation, and using the Bitrise dashboard to track cache hit rates and invocation durations for every build.

Kong's AI Gateway applies the same architectural pattern as the API Gateway — now governing LLM, MCP, and agent traffic at the infrastructure layer. Just as API gateways abstracted rate limiting, auth, and caching across microservices, AI gateways do the same for large language models and agents — with token budgets, semantic caching, and semantic routing replacing their REST equivalents. Kong breaks this into three layers: LLM Gateway, MCP Gateway for tool calls, and Agents Gateway for agent-to-agent traffic.#Shorts.

AI innovation moves fast, but without compliant data access, even the best ML, AI, and analytics initiatives can stall. In this webinar roundtable, experts from Perforce Delphix, 3T Software Labs, and Redgate explore how organizations can accelerate AI delivery without compromising data privacy, security, or compliance. You’ll hear practical insights and real-world examples on how to remove one of the biggest bottlenecks in modern software and data workflows: access to safe, usable, production-like data.

Most AI revenue will flow through tokens — and the two bottlenecks are tokens per watt (energy cost) and tokens per second (throughput). Tokens per watt determines how much output you can generate from a fixed energy supply — already constrained and getting tighter. Tokens per second sets the ceiling on how fast that revenue can flow. Kong's AI Gateway optimizes both at the connectivity layer: semantic caching and semantic routing increase token output without adding watts or latency.#Shorts.

LLMs are absorbing the business logic of microservices for agentic use cases — but both patterns will coexist in enterprise infrastructure for a long time. Cloud-native infrastructure (microservices + APIs) keeps powering web and mobile experiences. The agentic layer — LLMs, MCP tool calls, and context traffic — runs in parallel, activating the same APIs and CRUD operations underneath. Kong manages both swim lanes: the API traffic between clients and microservices, and the context traffic flowing between agents and LLMs.#Shorts.

CLI offers speed and developer freedom for API access; MCP provides centralized security, governance, and observability at enterprise scale. With CLI, credentials live on the developer's local machine and audit trails are shell-only — fast, but ungoverned. MCP adds authentication, centralized policy enforcement, and observability across all API calls, at the cost of some speed and higher token consumption. Kong's MCP Gateway is built for teams that need the governance trade-off without giving up too much velocity.#Shorts.