As AI applications move beyond rows and columns into images, video, embeddings, and graphs, traditional query languages like SQL and Cypher start to crack. This post explains why ApertureDB chose to design a JSON-based query language from scratch—one built for multimodal search, data processing, and scale. By aligning with how modern AI systems already communicate (JSON, agents, workflows, and natural language), ApertureDB avoids brittle joins, performance tradeoffs, and DIY pipelines, while still offering SQL and SPARQL wrappers for familiarity. The result is a layered, future-proof way to query, process, and explore multimodal data without forcing old abstractions onto new problems.

Everyone’s arguing about agents. Jonathan Wall says the real fight is about sandboxes, isolation, and why most “agent platforms” are doing it wrong.

MCP lets your agents connect to Slack, GitHub, your database, and whatever else you throw at it. Great for productivity. Terrible for security. When an agent can call any tool through any protocol, you've got a problem: who's actually making the request? What can they access? And when something breaks—or gets exploited—how do you even trace it back? This talk covers what breaks when agents go multi-protocol: authentication that doesn't account for agent delegation, permission models designed for humans not bots, and audit trails that disappear when Agent A spawns Agent B to call Tool C. I'll walk through real attack scenarios—prompt injection leading to unauthorized API calls, credential leakage across protocol boundaries, and privilege escalation through tool chaining. Then we'll dig into what actually works: identity verification at protocol boundaries, granular permissions that follow context, not just credentials, and audit systems built for non-human actors. You'll leave knowing how to implement MCP without turning your agent system into an attack surface, and what to build (or demand from vendors) to keep agent-to-tool communication secure.

Feature stores aren’t dead — they were just misunderstood. Simba Khadder argues the real bottleneck in agents isn’t models, it’s context, and why Redis is quietly turning into an AI data platform. Context engineering matters more than clever prompt hacks.

Satish Bhambri is a Sr Data Scientist at Walmart Labs, working on large-scale recommendation systems and conversational AI, including RAG-powered GroceryBot agents, vector-search personalization, and transformer-based ad relevance models.

Modern AI agents depend on vast amounts of context, data, features, and intermediate states, to make correct decisions. In practice, this context is often tied to specific datasets or infrastructure, leading to brittle pipelines and unpredictable behaviour when agents move from prototypes to production. This talk introduces mloda, an open‑source Python framework that makes data, feature, and context engineering shareable. By separating what you compute from how you compute it, mloda provides the missing abstraction layer for AI pipelines, allowing teams to build deterministic context layers that agents can rely on. Attendees will learn how mloda's plugin‑based architecture (minimal dependencies, BYOB design) enables clean separation of transformation logic from execution environments. We'll explore how built‑in input/output validation and test‑driven development will help you build strong contexts. The session will demonstrate how mloda can generate production‑ready data flows. Real‑world examples will show how mloda enables deterministic context layers from laptop prototypes to cloud deployments.

Sierra’s Zack Reneau-Wedeen claims we’re building AI all wrong and that “context engineering,” not bigger models, is where the real breakthroughs will come from. In this episode, he and Demetrios Brinkmann unpack why AI behaves more like a moody coworker than traditional software, why testing it with real-world chaos (noise, accents, abuse, even bad mics) matters, and how Sierra’s simulations and model “constellations” aim to fix the industry’s reliability problems. They even argue that decision trees are dead replaced by goals, guardrails, and speculative execution tricks that make voice AI actually usable. Plus: how Sierra trains grads to become product-engineering hybrids, and why obsessing over customers might be the only way AI agents stop disappointing everyone.

Everyone obsesses over models, but NVIDIA’s stack makes it obvious: the real power move is owning everything around the model. NeMo trains it, RAPIDS cleans it, TensorRT speeds it up, Triton serves it, Operators manage it — and the hardware seals the deal. It’s less a toolkit and more a gravity well for your entire GenAI pipeline. Once you’re in, good luck escaping.