Sireesh Gururaja

@siree.sh

PhD student @ltiatcmu.bsky.social. Working on NLP that centers worker agency. Otherwise: coffee, fly fishing, and keeping peach pits around, for...some reason https://siree.sh

Please join us at Carnegie Mellon University and online on October 2nd, 2026 for the Community-Centered AI Infrastructure Summit, a statewide convening to consider the opportunities, challenges, and governance of AI infrastructure development in Pennsylvania. RSVP here: communityaisummit.org

Flyer for the Community-Centered AI Infrastructure Summit on Friday October 2, 2026 from 9-5pm at Mellon Institute Auditorium, Carnegie Mellon University, Pittsburgh, PA. Join residents, community organizations, policymakers, researchers, technologists, energy experts, and advocates from across Pennslyvania for a public convening. Organized by CMU, Data & Society, PA Climate Equity Table, and Center for Coalfield Justice, with support from the Block Center.

If you want people to engage with a technology you care about, you have to find ways to frame it around providing them with some agency. You also have to be ready for people to just say "fuck you, no" and be OK with that. You have to learn to center people and not the technology itself.

My impression is that AI coding is on a "pick two of three" triangle: scope, ship speed, and correctness. Small tools are (speed + correctness) fast prototypes are (speed + scope) and big projects are (scope + correctness). Every project that tries to be all three degrades to "fast prototype."

It's finally out! At a time when basic research funding through the NSF and NIH in the US is under threat, it's important to understand what "basic" means in the context of more available DoD funding. For a lot of academics, the DoD's "basic" might not be be as basic as you think!

🌶 David Gray Widder@davidthewid.bsky.social · 3mo ago

🚨Finally published: How does the US Military enlist AI researchers in war? @siree.sh, Lucy Suchman and I examine 7k US Military grant solicitations for AI research, and show how even "Basic Research" enlists researchers in a warfighting agenda. journals.sagepub.com/doi/10.1177/...

good ML: * carefully designed for narrow contexts (or with awareness of context limits) * designed by ML and topical experts, with expert curated data and expert reviewed outputs * designed for specific needs and consumers, and kept up to date for both * clear training / comms on use and limits

Truly wonderful conversation with my lyft driver this morning, which started by discussing how he wanted to go on vacation in Florida, to see alligators in the wild. Normal, relatable, etc - until he reveals that he in fact raises alligators as pets - but only until they're eight (8) feet long!

> looking at tech sector layoffs > ask the hr manager if the layoffs are from ai or from the end of zirp > she doesn't understand > pull out illustrated diagram explaining what is ai replacement and what is zirp > she laughs and says "they’re good layoffs sir” > check the data > its zirp

As someone that came of age in a time where the linux distros I used had easy access to a *huge* variety of loyal clients (xmpp, so many web things), this paper and its framing are a lovely way to describe the thing I'd love for computing to return to ❤️

Ethan Zuckerman@ethanz.bsky.social · 5mo ago

"The loyal client" is a concept that's been important to our lab over the past five years as we've tried to make social media better: it's the idea that the tools you use to access social media should meet your needs, not those of the platform owner. Spencer Lane wrote a great paper on this as part

Great quote from a great article, and it drives me *nuts* how big the gap is between the real affordances of AI, which can be fun and adaptive and agency affirming the political project it is inextricable from, software-brained imperialism :/

Hypervisible @hypervisible.blacksky.app · 6mo ago

“…all I can tell you is that it is a failure when you ask people to adapt to computers. Computers should adapt to people. Asking people to make themselves more legible to software —to turn themselves into a database — is a doomed idea.”

This is a great thread, and reminds me of interviewing someone at one of the big labs for a study, and they made the claim that "classification was solved in 2022" That's an insane quote, until you account for the money, time, and labor that will be poured into any problem that's deemed worth it

Vincent Carchidi@vcarchidi.bsky.social · 6mo ago

I have my beef with his recent commentary. This beef is substantial. However, the problems at the heart of his DL critique - (un)reliability and (robust) out-of-distribution generalization - were never solved by LLMs or LLM-based systems. They were circumvented.