You updated a Kubernetes Secret and then did the ritual: kubectl rollout restart. There is a better way. mamori watches your Secrets and ConfigMaps with native informers and reconciles the change into your running Go process, atomically and validated, without bouncing a single pod.
In GCP Secret Manager, secret versions are immutable and you rotate by adding a new version and moving the “latest” alias. But your Go service resolved “latest” once at boot and froze it there. mamori watches the secret, notices when “latest” moves, and reconciles the new version into your running process, validated and redacted.
Azure Key Vault versions your secrets and lets you rotate them behind the scenes, but your Go service resolved the secret once at boot and never looked again. mamori watches Key Vault and reconciles a new version into your running process, typed, validated, and redacted, without a restart.
AWS Secrets Manager will happily rotate your database password every 30 days. The problem is your Go service, which read that password once at boot and has no idea it changed. mamori closes that gap: it watches Secrets Manager and Parameter Store and reconciles rotations into your running process, typed, validated, and redacted.
Every Go service ends up hand-rolling the same thing: a config loader, a secrets fetcher, a ticker to refresh them, a mutex, and a prayer that nothing leaks into the logs. mamori is a new open-source Go library that loads configuration and secrets from more than 30 sources into typed, validated structs, and keeps them reconciled at runtime, without a restart.
Genkit flows are great at orchestrating LLMs, tools and RAG, but they live and die with the process that runs them. The new genkitx-temporal plugin lets you run any Genkit flow as a Temporal Workflow, giving you retries, durable history, timeouts, cancellation and a UI to inspect every execution.
Building AI applications without a local debugging tool is like writing backend code without a debugger. A look at the “shift-left” philosophy applied to Gen AI development, and a hands-on comparison of Genkit Developer UI, Vercel AI SDK DevTools and Mastra Studio.
A side-by-side comparison of the two leading middleware systems in the JS/TS Gen AI ecosystem: Vercel AI SDK’s wrapLanguageModel and Genkit’s generateMiddleware. APIs, mental model, built-ins, composition, observability and when to pick each.
A deep dive into the new Genkit middleware system for JavaScript/TypeScript: built-in middleware (filesystem, skills, toolApproval, retry, fallback), how to build your own with generateMiddleware, and the new model/tool/generate interception hooks.
Aprende a empezar con Genkit en JS/TS desde cero: instalacion, primer flow, ejecucion local y uso de la Developer UI.
Python has dominated the Gen AI conversation, but it is not the only, nor the best, option for production. Here is why Go (and Genkit Go in particular) is a stronger bet for serious AI services in 2026.
A practical, in-depth comparison of the top Generative AI frameworks in 2026: Genkit, Vercel AI SDK, Mastra, LangChain, and Google ADK, from someone who has built with all of them. (English)
A practical, in-depth comparison of the top Generative AI frameworks for Java in 2026: Genkit Java, Spring AI, LangChain4j, and Google ADK (English)
An interactive CLI chat tool with AI-powered web search using Genkit agents and tool calling
Deploy AI-powered flows to Azure Functions using Genkit and the Azure OpenAI plugin
Deploy AI-powered flows to AWS Lambda using Genkit and the AWS Bedrock plugin
Building a weather service using Genkit in Node.js with AI integration
The rise of AI agents powered by Large Language Models (LLMs) has revolutionized how we interact with technology. However, effectively evaluating these agents requires a multifaceted approach that goes beyond traditional metrics
The NLU powered by LLMs that just works
