Temporal and Kafka orchestrate small language models reliably through durable workflows, ordered events, idempotency, retries, replay, and context preservation.
AWS and Azure handle identities and audit logging in fundamentally different ways, changing what you see in your security logs when someone tries to access your data.
Our identity-aware MCP server built in Python rejects anonymous agents, validates OAuth 2.1 via JWKS, enforces tool-level scopes/roles, and logs full delegation chain.
GraphQL was good at a time, then it simmered off. Is GraphQL about to make a comeback because of AI? Will GraphQL be able to serve better for AI Agents?
We model a supply chain in Neo4j using Apache Spark to load data, NetworkX to identify critical nodes, and Cypher to find alternative routes after a disruption.
We eliminated per-record Python-side Protobuf parsing and JVM-to-Python crossings by letting Flink's native Protobuf format decode records directly into typed columns.
By combining Quarkus Flow, LangChain4j, MCP tools, and AGENTS.md, developers can construct deterministic, tool-augmented, and enterprise-governed AI agent loops.
A zero-trust framework for cloud migrations, grounded in real enterprise deployment lessons. Perimeter security doesn't hold up once workloads move to the cloud.
RFC 10008's new QUERY method is safe and cacheable like GET but carries content like POST. This article explains the spec and runs it on Quarkus today.
I built six AI visibility tools without a traditional backend, using one Cloudflare Worker to solve CORS while all scoring logic runs client-side in the browser.
A launch traffic spike hit cold-loaded LLM containers; shared-memory crashes and KV-cache OOMs taught us why GPU autoscaling needs warm floors, not reactive scaling.
A direct CNN benchmark on CIFAR-10 shows TensorFlow and PyTorch achieve identical accuracy (~68%). Choose TensorFlow for production and PyTorch for flexibility.