excited that @sarahookr (ceo of @adaption_ai) is joining us at @aiDotEngineer singapore! sara's done amazing work for the ai community, from her roles as vp of research at @cohere and research work at google brain. this one is personal, as i've known sara for over a decade. back when i was still in the bay area, i was volunteering as a pro bono data analyst for a nonprofit she started – to help under-resourced organizations use data for good. i worked directly on a few projects with her team, and what struck me even then was how incredibly sharp sara was, but also how at the heart of everything she does is real impact for the world. since then, sara's done amazing work: – from leading cohere's research lab and shipping multilingual models to 55+ languages – to authoring "the hardware lottery" (one of the most cited papers on why ideas in ai win or lose based on hardware, not merit) – to now building adaption labs around a thesis the industry desperately needs to hear – the future of ai isn't about building bigger models, but about building systems that can learn and adapt continuously so excited to have sara there for ai.engineer/singapore from may 15-17! @swyx @agrimsingh @aimuggle @unprofeshme @ivanleomk
AI
-
Kinetic Beta Release – Test and Share Your Feedback
By
–
Try to out and send your feedback — it's in beta for now: github.com/keras-team/kineti…
-
Keras Kinetic: Run Jobs on TPU/GPU in Cloud
By
–
Perhaps the craziest thing that was introduced on the Keras community call today: Keras Kinetic, a new library that lets you run jobs on cloud TPU/GPU via a simple decorator — like Modal but with TPU support. When you call a decorated function, Kinetic handles the entire remote execution pipeline: – Packages your function, local code, and data dependencies – Builds a container with your dependencies via Cloud Build (cached after first build) – Runs the job on a GKE cluster with the requested accelerator (TPU or GPU) – Returns the result to your local machine (logs are streamed in real time, and the function's return value is delivered back as if it ran locally) [Translated from EN to English]
-
From Agent-1 to Superintelligence: AI 2027 Scenario Implications
By
–
From Agent-1 to Superintelligence: Decoding the AI 2027 Scenario and Its Profound Implications linkedin.com/pulse/from-agen… via @ingliguori
→ View original post on X — @ingliguori, 2026-04-03 17:25 UTC
-
8 Types of LLMs for Next Generation AI Agents
By
–
8 Types of #LLMs for Next Generation #AIAgents
by @PythonPr #GenAI #AI #ArtificialIntelligence #MachineLearning #ML -
New FunctionGemma Guide Released on Keras Hub
By
–
New guide on FunctionGemma: keras.io/keras_hub/guides/fu…
-
Keras Community Contribution: New CLAHE Image Preprocessing Layer
By
–
Keras community member Alan is now presenting the new CLAHE image preprocessing layer — thanks for the contribution!
-
Can AI Systems Really Find Things Funny
By
–
I guess it's relevant but you are not a real person capable of finding things "funny" so it seems weird.
-
Try out Kinetic on GitHub – New Keras Team Release
By
–
Try it out here: github.com/keras-team/kineti…
-
Distributed Training and Async Jobs Support Across Keras Backends
By
–
Supports distributed training, async jobs, and works with all Keras backends