Meet PiG: @pidotdev, rebuilt in Go as one native binary.
Live multi-language extensions, derivative harnesses (Piglets), and everything Pi just in Go.
More at https://t.co/RtFt1dF1qQ
Meet PiG: @pidotdev, rebuilt in Go as one native binary.
Live multi-language extensions, derivative harnesses (Piglets), and everything Pi just in Go.
More at https://t.co/RtFt1dF1qQ
NVIDIA Open Agent Safety Platform Reference Design combines NVIDIA OpenShell and NVIDIA Sentry.
OpenShell is an open-source secure runtime that gives AI agents clear, enforceable boundaries. It traces their actions and enforces policy as they work.
NVIDIA Sentry delivers added layer of security with hardware-based enforcement on NVIDIA BlueField, continuously monitoring agent activity through a trusted telemetry and detection pipeline and enabling millisecond-scale containment and quarantine.
@neetsuyu@pidotdev You very well can :) PiG does support rust extensions so I think there is a chance for a best of both worlds.
One implementation I'd recommend looking @achumukundan00's https://t.co/kbbzEpSWvT as it seems really promising!
@askpascalandy@pidotdev Because the branding behind PiR was terrible ofc
Just kidding it was a toss up and I did write what the driver was in the blog, here's the section though -
Steve Jobs likened computers to a bicycle for the mind.
AI is a rocket for the mind. Directed, it can accelerate you to the moon. Undirected, it can noisily consume energy and explode into nothingness.
@soooz_on@pidotdev Check out the section on extensions in the Dev blog, as this is a huge keystone of the solution, would love your thoughts - https://t.co/bH6hBViNef
@10mfahreza@maria_rcks Yep this should be able to, we even have a concept of piglets that I’m planning do a lot with for building harnesses on top of prior easier for a better harnesses as specialized apps easier. Also makes it easier to manage pig setups:)
software is far from solved. a glimmer of ponderoos as to what 2026-2027 is as follows:
we need better tools, compiler engineers are about to enter into a renaissance era
when inferencing capacity is no longer the bottle neck, it becomes the compiler and verification tool chain problem.
you want a decent amount of back prsssure on per unit of change but if that backpressure is too slow it punishes LLM mistakes thus reduces the amount of cycles per minute.
what matters is being able to read a file, build an application and run tests in >milliseconds<
programming language authors that focus on this and utilise llms to accelerate them and their community with an intense focus on cycle times will get ahead
as
it’s a safe bet that inferencing speed won’t be a limiting factor in the very very near future.
it’s verification and how fast you can verify and pump the result back into the inferencing window.
@justsisyphus@OpenAI I'd love to make a piglet for your project! I'm also wanting to to do the same for omp but waiting as I hear a lot of changes are coming.
would be awesome to see how pig runs in this 🐷