After 4 months of focused work, I'm launching what is, to my knowledge, the first truly serverless vector database: turbopuffer.com/ $1/month per million vectors (10-70x cheaper than others) $4 per million queries

The US government is now spending a record 18.5% of its total revenue just to cover interest on its debt, per YF

If you are building a software factory, and want to just feed in a perfect spec and get out a working system - are you just reinventing the waterfall method with agents? I can’t wait for history to repeat and we have an agentic agile manifesto

I keep reflecting about the economics of this. Everyone can make their dream games now? Ok. Then what? For offline games: in most genres, playing one you made yourself isn't so fun because you're, by definition, spoiled. For online games: if everyone can build it, getting people to play it (which was always the hardest part) will be even harder. So, which game will people play, and why? For one, building my dream MOBA right now should take a week (in Bend so there are no bugs 😃). But if people are stubborn on League of Legends, what good is that for? Hmm...

You can never vibecode and build a ML model I REPEAT NEVER

A painful part of working with your data has always been that your live data and historical data are stuck in separate systems: the order a customer just placed lives in your database, while their last five years of orders sit in a data lake in S3. And answering a real question usually needs both at once (is this a normal purchase for them, or should we flag it?), and to do that you had to move the data together first, copying history out of the data lake into your database (or the other way around), because the database couldn’t read it where it lived. That meant guessing ahead of time which data you’d want, keeping a second copy of it all, building pipelines to move it, and constantly syncing so the two didn’t drift apart. A lot of plumbing, and slow going, all before you could answer one question. And even then, the answers were only as fresh as your last sync. That now changes with Aurora PostgreSQL, which can call your live data and historical data in S3 together, in a single query. No copying, no pipelines to keep in sync. And it’s fast, because we’ve built in DuckDB, a popular open source engine that’s really good at reading and analyzing data right where it’s stored. DuckDB reads the open formats like Parquet and Iceberg already sitting in your data lake, so there’s nothing to convert or move. As folks build AI agents into their apps, the data their agent needs will depend on the task in front of it. Being able to query that specific data live, instead of copying it over just in case, is gonna be a big help for builders. aws.amazon.com/blogs/aws/ama…

"Even if they’re empty half the time, and energy and maintenance double the cost of the car itself, the equilibrium price is about $2 an hour." betonit.ai/p/the-rideshare-r…

Two weeks ago, @PlanetScale launched TIN. They benchmarked it against us, and it was very fast. So we spent those two weeks making ParadeDB faster without their tradeoffs. Today, we are faster than TIN. paradedb.com/blog/opening-a-…

Gemini 4 Argon is our next era of frontier intelligence. It shows significant improvements across benchmarks, setting a new state of the art for real-world long-horizon software engineering tasks.