Shadaj Laddad
@ShadajL
Research lead for http://hydro.run @ AWS. PhD from @UCBerkeley. Co-organizer http://sfsystemsclub.com. Views mine.
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I'd go as far as to say that good abstractions are more important than ever. Bad abstractions may be worse than ever.
Final SF Systems Meetup is a go! So excited to hear Alex from @AntithesisHQ dive deep into their deterministic hypervisor!
As always, some really excellent ideas (and even more important, framing). Well worth a read!
We’ve got a surprise! One last SF Systems Club to end the year: luma.com/sdmlmjcp Thanks @AntithesisHQ for hosting! Space is a bit limited so sign up soon!
There we go!
Specifications are much more useful if you can test that your code matches the specification. With Kiro's new property-based testing feature, it can use your specification to generate much more powerful tests, avoiding bugs and improving quality.
Looking around Twitter, I see a number of amazing scientists impacted by the layoffs at Meta. The Agentic AI org at AWS is working on a number of interesting problems and is hiring pre and post training experts to come build with us.
Kicking off Madison Systems with a coffee chat on Sunday, Nov 9th. Come nerd out on systems! Register here: luma.com/v69tvpla
SF Systems Meetup next Wednesday! We will be talking CRDTs and Databases :) luma.com/e7feg2i6
The SF Systems Meetup is back with another night of awesome systems talks and community building! Hosted by the amazing @trychroma, hope you’ll join us! luma.com/e7feg2i6
Reading between the lines, this whole trend is an incredible opportunity for the PL research community. The next generation of systems research will be one level of abstraction higher than it is today. This is what PL research is all about (especially OOPSLA / PLDI crowd).
Barbarians at the Gate is a very interesting new paper, with some exciting results showing the potential for AI in systems research. But I think the authors aren't quite asking the hardest problem about where this takes systems as a field. I wrote a new blog post about it.
Training our advisors was too hard, so we tried to train black-box models like GPT-5 instead. Check out our work: Advisor Models, a training framework that adapts frontier models behind an API to your specific environment, users, or tasks using a smaller, advisor model (1/n)!
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