Agents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.

Learn how a natively multimodal database like ApertureDB helps healthcare ads stay compliant by flagging missing facts and improving transparency by supporting true multimodality alongside vector search. Longer abstract: Technologies like RAG (retrieval-augmented generation), semantic search systems, and generative applications wouldn’t be possible without vector databases. A very few of these databases, such as ApertureDB, are truly capable of natively handling more than just text. They now work with images, audio, and other data types, which opens up new possibilities across industries like healthcare, retail, and finance. For building this example, we pick healthcare advertising because it shows a great blend of multimodality. With strict rules around accuracy, disclosure, and patient privacy, it’s critical to include all Material Facts in marketing content. These are details that could influence a patient’s understanding or choices. In this blog, we will discuss how a combination of ApertureDB, Unstructured, and OpenAI can help detect and flag missing material facts in healthcare advertisements.

What if the computer itself can think and take actions for you? You just give it a goal, and it performs every click, type, drag, and gets work done across the desktop and web. In this talk, Zengyi reveals the breakthrough technology that his company OpenAGI is developing: AI that can use computers like humans do. He talks about how his team developed the model, why it outperforms similar models from OpenAI and Google, and its wide use cases across different domains.

Feature stores might be the wrong abstraction. Varant Zanoyan and Nikhil Simha Raprolu explain why Cronon ditched “store-first” thinking and focused on compute, orchestration, and real-time correctness—born at Airbnb, battle-tested with Stripe. If embeddings, agents, and real-time ML feel painful, this episode explains why.

Agents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.

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.