Oracle Developers

Look at a simple way to try CNCF Jaeger on OCI, which runs Jaeger on a single OCI Compute virtual machine, together with the Jaeger HotROD demo application.

The result: a useful first step into distributed tracing and a practical demo for showing why observability matters. https://social.ora.cl/6187BDlkXW

OCI Generative AI provides a managed path for building, deploying, and governing AI applications at scale.

LangGraph is useful because it makes agent workflow explicit.

This article looks at how the FastAPI + LangGraph application under code/oci-langgraph-hosted-agent gives teams a concrete starting point: configure OCI Responses API settings, build the container, and deploy it as a hosted application. https://social.ora.cl/6185BDlXzP

👉 Assertions
👉 Foreign keys

Two constraints Oracle AI Database offers to enforce one or more matching child rows for every parent row. Chris Saxon gives us a detailed breakdown: https://social.ora.cl/6184BDlnZo

Good agent skills raises the team baseline: better first pass code, fewer manual corrections, and improved integrations with Oracle AI Database.

This blog covers an agent skill that helps you write Kafka Java for Oracle AI Database Transactional Event Queues’ OKafka library. 📚 https://social.ora.cl/6188BDcmKQ

Semantic caching ≠ vector search

A semantic cache should only reuse an LLM response after policy validation—not just because two prompts are similar.

This architecture shows how to build governed semantic caching with:

✅ Spring AI
✅ Oracle AI Database 26ai
✅ Oracle True Cache

The result: lower latency, fewer LLM calls, lower token costs, and safer AI applications.

If you're building production AI systems with Spring AI, this is a must-read: https://social.ora.cl/6187BDcYV5