Tootfinder

Good explanation what Mythos is and what it's not, it did impressive things like finding a 27 year old bug in Open BSD. So please don't say that AI/LLMs can only reproduce their training data....A myth that won't go away. On the other hand Mythos still has many of the characteristics that make AI/LLMs problematic, it still makes mistakes, is not AGI or self-improving.

Like global search and replace but don’t like surprises?
Check out serpl – a handy little command-line app that gives you a visual preview of the changes you are about to make. You can even go in and remove the replacements you don’t want from the source previews. The regex support appears to be basic, however (I couldn’t get a negative lookbehind to work).

Perfect Network Resilience in Polynomial Time
Matthias Bentert, Stefan Schmid
https://arxiv.org/abs/2602.03827 https://arxiv.org/pdf/2602.03827 https://arxiv.org/html/2602.03827
arXiv:2602.03827v1 Announce Type: new
Abstract: Modern communication networks support local fast rerouting mechanisms to quickly react to link failures: nodes store a set of conditional rerouting rules which define how to forward an incoming packet in case of incident link failures. The rerouting decisions at any node $v$ must rely solely on local information available at $v$: the link from which a packet arrived at $v$, the target of the packet, and the incident link failures at $v$. Ideally, such rerouting mechanisms provide perfect resilience: any packet is routed from its source to its target as long as the two are connected in the underlying graph after the link failures. Already in their seminal paper at ACM PODC '12, Feigenbaum, Godfrey, Panda, Schapira, Shenker, and Singla showed that perfect resilience cannot always be achieved. While the design of local rerouting algorithms has received much attention since then, we still lack a detailed understanding of when perfect resilience is achievable.
This paper closes this gap and presents a complete characterization of when perfect resilience can be achieved. This characterization also allows us to design an $O(n)$-time algorithm to decide whether a given instance is perfectly resilient and an $O(nm)$-time algorithm to compute perfectly resilient rerouting rules whenever it is. Our algorithm is also attractive for the simple structure of the rerouting rules it uses, known as skipping in the literature: alternative links are chosen according to an ordered priority list (per in-port), where failed links are simply skipped. Intriguingly, our result also implies that in the context of perfect resilience, skipping rerouting rules are as powerful as more general rerouting rules. This partially answers a long-standing open question by Chiesa, Nikolaevskiy, Mitrovic, Gurtov, Madry, Schapira, and Shenker [IEEE/ACM Transactions on Networking, 2017] in the affirmative.
toXiv_bot_toot

Auch wenn's heute nur das #mdRzA war, das ich normalerweise nicht als #Rausgeschafft ansehe, so war's nach 'ner knappen Woche Bindehautentzündung (mittlerweile wieder gut) doch das 1. Mal wieder #Radfahren

🇺🇦 #NowPlaying on #KEXP's #MiddayShow
The Style Council:
🎵 Long Hot Summer (Club Mix)
#TheStyleCouncil
https://xtopher.bandcamp.com/track/the-style-council-long-hot-summer-xtophers-poolside-rework
https://open.spotify.com/track/26Fk1YatHThrKaBVpEWfKw