As Large Language Models evolve, the real challenge isn't just generating text—it's remembering context, maintaining state, and managing historical data effectively. How do you provide LLMs with persistent, long-term memory without overwhelming context limits or skyrocketing latency and costs? ​Whether you're building stateful AI agents, implementing advanced Retrieval-Augmented Generation (RAG), or managing enterprise-grade vector and relational data, this meetup covers the practical architectures and trade-offs behind modern LLM memory systems.

Preparing an MLOps repository for AI coding agents requires eliminating friction points that cause AI hallucinations, such as fragmented toolchains and conflicting CI/local scripts. To create an "agent-ready" environment, teams should adopt a "single-gate pattern" that unifies execution commands across all environments, while ensuring that standard AGENTS.md instructions are strictly enforced by automated, machine-readable rules. Additionally, optimizing test performance for isolated MLflow environments—such as using a SQLite template database pattern—and managing dependency vulnerabilities through explicit, tested overrides are crucial. Ultimately, building a deterministic, rigorously checked repository allows AI assistants to bypass tooling drift and efficiently produce correct, production-ready code.

An analysis of the upcoming 2026-07-28 MCP specification updates. It covers how the protocol is moving from stateful handshakes to a stateless core, allowing remote servers to run behind standard round-robin load balancers. The piece also details the new enterprise-managed token exchange (ID-JAG via RFC 8693) and the release of beta SDKs with structured 12-month deprecation cycles.