Didn't make it to AWS re:Invent this year? Here are all the highlights. (Hint: Agents. So. Many. Agents. 😃)

Durable execution frameworks are becoming an important infra pattern for agents. Traditional services run in milliseconds. If they crash, you retry. But agents run longer, retries burn tokens, and crash recovery actually matters. DBOS is a great example of this emerging

We've loved working with our friends at Tiger Global. They are a better and smarter fund than people realize. Excited to see this.

✨Timescale (now TigerData) 2.24 feature teardown: Direct Compress just got smarter (and faster) When we introduced Direct Compress in 2.21, the idea was straightforward: Instead of writing data into the row store and later converting it to compressed columnar format, perform that

It's that time of year when we pause to reflect on the future. How we want to grow, improve, and make better long-term decisions. I'm talking about your tech stack, obviously. What else? And if you’re looking for guidance, the most powerful ChatGPT model has real clarity. Your

✨Timescale (now TigerData) 2.24 feature teardown: Continuous Aggregates now speak UUIDv7 UUIDv7 is a new time-based identifier format (RFC 9562): globally unique, but with an embedded timestamp that preserves ordering. That detail matters architecturally: the ID itself can serve as a stable

Huh. The models are starting to hallucinate that it's 2024. Anybody else? An internal agent using Claude Sonnet 4.5 started doing this yesterday, and ChatGPT just did as well.

Unifying database and lakehouse data. We announced our baselake architecture the day before Databricks announced lakebase. Names aside, the underlying idea resonates with many larger customers. Databases and lakehouses serve different use cases, different teams, and different