Caleb Gross

ai for security

[Caleb Gross @noperator]

1/ Agentic LLMs can automate vuln detection. Very exciting, but doesn't address the hardest part (imo) of vuln research: prioritization. Can we reliably explore the search space and separate signal from noise? I wrote a paper (and OSS tool) to solve this. https://t.co/HDzm4SvSX1

[Caleb Gross @noperator]

@kolemannix @julien_c fitting, as you were the first person to show me tmux back in college :)

[Caleb Gross @noperator]

@b1ack0wl my greatest piece of advice here is to read top daily posts in r/LocalLLaMA

[Caleb Gross @noperator]

@msftsecresponse too long to be wrong

[Caleb Gross @noperator]

Is that all a professor is—someone who "knows the most"? Is that all education is—information acquisition?

[Caleb Gross @noperator]

This concern makes sense if the medium doesn't matter. Perhaps we should question our desire for raw, unmediated information.

[Steve McGuire @sfmcguire79]

“AI is demoralizing.”

A Princeton Professor says he kept wondering this semester (while lecturing) if his students would be better off learning from Claude:

[Caleb Gross @noperator]

thanks for the signal boost @antirez :) first time occupying the top spot on hacker news front page.

[Caleb Gross @noperator]

https://t.co/gOSvgSg8kv

[Caleb Gross @noperator]

@pietrozullo @antirez kudos @TomNomNom

[Caleb Gross @noperator]

https://t.co/gOSvgSg8kv

[Caleb Gross @noperator]

marked safe from the earthquake in Hawaii 🤙

[Caleb Gross @noperator]

@kusonooyasumi need specs on your inference rig

[Goni Zahavy @gonizahavy]

My MLX Vulkan backend just passed both CPP AND Python test suites!!

[Caleb Gross @noperator]

I opted for the framework desktop because:
- it's cheaper than the mac/spark options
- I want to support framework as a company
- I want to support AMD ecosystem as an alternative to being locked into NVIDIA (and I think support for that platform is rapidly improving)

https://t.co/mrwrl2J7XC

[Caleb Gross @noperator]

@AlizTheHax0r the tldr is that you can run small-ish models fast on a GPU (3090, etc.) or large-ish models slow on unified memory (mac ultra, dgx spark, framework desktop, etc.). biggest difference between those two is the amount of (V)RAM and the bandwidth of that memory.

[Caleb Gross @noperator]

@antirez @AMD amazing. my framework desktop arrives today 🙌

[Caleb Gross @noperator]

betting that GitHub's "Download ZIP" button gets _way_ more clicks since the advent of LLMs. I very frequently drop entire zipped codebases in front of ChatGPT.

[Caleb Gross @noperator]

@AlizTheHax0r also depends on what you're trying to accomplish. if you're already ready to spend a lot ("small fortune"), a 128GB unified-memory inference machine could be appropriate if you want to run frontier-ish models locally.

[Caleb Gross @noperator]

@AlizTheHax0r 3090 is great too :)

[Simone Margaritelli @evilsocket]

Earlier today Cloudflare's CSO shared how they tested Anthropic Mythos using an unreleased 8-stage vulnerability-discovery agent. So I asked Opus to implement the agent for me, it works via Claude SDK with a Pro or Max subscription, no API.

Enjoy https://t.co/McoZbTvTLL

[Caleb Gross @noperator]

@__tinygrad__ @halvarflake Agree with your notion of AI-as-search. Here's how I'm thinking about it: https://t.co/8lGm747JN1

[Caleb Gross @noperator]

1/ Agentic LLMs can automate vuln detection. Very exciting, but doesn't address the hardest part (imo) of vuln research: prioritization. Can we reliably explore the search space and separate signal from noise? I wrote a paper (and OSS tool) to solve this. https://t.co/HDzm4SvSX1