What Is

A serverless database is a cloud database that decouples compute from storage, scales capacity automatically as demand changes, and bills for what a workload actually consumes. Servers still exist. The difference is that you never size them, patch them, or pay for idle ones, and capacity planning stops being a launch-day decision. That difference matters […]

On July 24, Jensen Huang made the first post of his life on X. It wasn’t a product launch or a victory lap. It was a policy letter signed by 25 companies, doubling to 50 within a day, asking Washington not to restrict open weight AI models. Microsoft, Meta, IBM, a16z, and Hugging Face. The […]

As a database veteran, I found traditional monolithic databases (such as Oracle) to have bottlenecks for mission critical applications in the cloud, which called for a next generation cloud native database to solve these challenges. This is what excited me about joining the PingCAP team, where we took the core concept of Distributed SQL’s multi-node auto-sharding architecture and extended it to allow for greater flexibility and functionality beyond Spanner and other Distributed SQL databases. It is also open source and has helped to change the landscape for developers.

Agentic AI architecture is the system design that lets an AI agent perceive context, reason over it, call tools, maintain memory, and take actions across multiple steps. It spans the model, the orchestration layer, and the data infrastructure that makes an agent’s work durable rather than disposable. Most teams get the model and framework right […]

A single user action in an agentic application no longer maps to a single database query. It spawns agent instances that branch context in milliseconds, hold memory across sessions, and provision their own tenants without waiting for a human. At Manus, agents create more than 90% of new database clusters. The stacks underneath them were […]

For most of the cloud era, infrastructure decisions followed a familiar pattern: choose the fastest path to production, accept some proprietary dependencies, and optimize later. AI breaks that pattern. Coding agents now reshape applications in hours, while models, frameworks, and workload shapes change underneath them. That leaves the data layer as the one place a […]

I’ve spent the last year helping enterprise teams put AI agents into real workflows: Fraud detection, infrastructure monitoring, customer intelligence. The conversations follow a predictable pattern. The first question is always about the model. Which LLM? Which orchestrator? The second question, about six weeks after go-live, is always about the data. Why is the agent […]

If you have ever watched an ORM-heavy application meet a sharded MySQL backend, you know the moment I mean. The code looks right. The tests pass. Then production traffic arrives, and you start seeing the impossible: An order without its line items, a user whose profile says one thing and whose billing record says another, […]

When a table crosses one terabyte, MySQL does not fail. It just starts charging for everything. A single index change takes over a week, blocks every other change queued behind it on the cluster, and demands a full shadow copy of the table just to run. At Bolt, whose ride-hailing, micromobility, delivery, and car rental […]

When enterprises evaluate a distributed SQL database for production workloads, security isn’t a checkbox. It’s a prerequisite. Teams running financial transactions, customer data, and AI agent infrastructure need to know that the database they depend on holds up under independent scrutiny, not just internal testing. That’s why TiDB engaged NCC Group, a global cybersecurity consultancy, […]

If you have ever run Amazon Aurora at scale, you know the moment I mean. Write latency on the primary starts creeping up during peak hours. You bump the writer to the next instance class, and the relief buys you a quarter. You bump it again. Then one day you open the AWS console to […]

A few months ago I watched a fintech engineering team walk me through their AI agent platform. They had a transactional database for balances and ledgers, a separate vector database for semantic retrieval, a cache for session state, a document store for agent memory, and a stream processor stitching it together. Five systems. Each one […]