for the curious:

https://www.usaspending.gov/search?hash=5ec35bf87ec1fd63d28d...

Thanks for the input

>We won't be developers, we won't be devops, we'll be modelops! /s

I can still see this happening with higher level langs. the thing is the compiler is not replaced in the training data, more likely LLMs will give rise to semideterministic layers on the compilers

I could see nvidia achieving this first with how nice the devex is with CUDA

you can run benchmarks yourself: openssl speed rsa1024 rsa2048

also this (slightly dated) java ex writeup covers this well: https://www.javamex.com/tutorials/cryptography/rsa_key_lengt...

tldr trade off is found between better performance and how many years the data needs to be assumed confidential

it's a little cringe, but arguably the benefit of having agents use rails would be tht when you review and audit the agent produced code, you review something that is, as you put it: "beautiful and simple code" and "making it easy to reason about..."

I loved rails back in 2017. I may be an outlier but the line tempts me to try it again despite having adopted the who cares attitude to langs. Would be nice to hear from someone first hand if they felt it helped.

Digging into the paper, the significant finding (RCE) is achieved via:

A payload was written which installs a reverse shell backdoor for root persistence. The payload was sent from a computer hosting a Wi-Fi to which the watch was connected, to ensure the watch had a reachable IPv4 address. The program ncat was used both to send the payload to the watch's network service, and to catch reverse shell connections.

So if i understand this- it requires the watch being connected to a compromised AP. Anyone get a different read?

Shoutout to OSHpark's prototype service. Something like 5 bucks an inch and you only have to toss out 3 if you find a fault.

Digging into the repo i can see over 50 open issues from the past few days with a lot of requests for refunds.

Are there any "success stories" ? Could go a long way to building trust in the tool.

Would you mind sharing which benchmarks you think are useful measures for multimodal reasoning?