A pull-request review bot in Spring Boot 4.1.1 and Spring AI 2.0.1: verify the webhook signature, chunk the diff with new-file line numbers, review each chunk, drop comments on lines the model invented, and keep the bill down with a cache and a chunk cap. Runs with a scripted model and no API key.
A fraud-detection topology on Kafka Streams 4.2.1 and Spring Boot 4.1.1: cosine-similarity scoring against known-fraud vectors, a 5-minute windowed count, and a blocklist KTable join, all tested with TopologyTestDriver and no broker.
Build two agents that hand work to each other with Google's Agent Development Kit for Java 1.11.0: an LlmAgent with a tool, a SequentialAgent, and session state as the hand-off. Runs without an API key, using a scripted stand-in model that is labelled as such.
Draw an agent as a graph of nodes, edges and shared state, pause it for a human with a checkpoint, and resume it later. A loan-approval agent on LangGraph4j 1.9.5, with the one default (releaseThread) that silently erases a finished run.
25 interview questions on RAG, embeddings, tool calling, MCP, agents, evaluation and cost control for Java developers, with short answers in accordions and runnable proof for the tricky ones.
Kafka share groups (KIP-932) let many consumers share one partition and acknowledge records one by one. Measured against a real Kafka 4.3.1 broker with Spring Kafka 4.1.1: what you gain, what you lose, and when RabbitMQ is still the answer.
Spring Cloud 2026.0.0-M1 adds an HTTP notifier to Config Server and fixes how Gateway handles forwarded headers from untrusted proxies. I ran both and show what changes and what to test now.
Jev is a hosted TypeSafe AI API that answers yes/no, pick-one and score questions about your data. Here is the Spring AI starter, how to wire it, and a confidence-gated model router, verified offline.
Package an agent's know-how as SKILL.md folders and let Spring AI load them on demand with one tool. What the model sees, what a load costs, and the edge cases I reproduced.
Spring AI 2.1.0-M1 adds VectorStore.upsert() that takes vectors you already have. Here is what it does, which stores support it, and how to move between stores without calling the embedding model again.