Tootfinder

SEC filing: Snowflake says an executive shared unauthorized financial guidance with an Instagram influencer; an Oct. 26 video interview features CRO Mike Gannon (Alice Tecotzky/Business Insider)
https://www.businessinsider.com/influencer-inte…

An influencer's interview with Snowflake's CRO triggered an 8K filing
Mike Gannon said that Snowflake will end the year with just over $4.5 billion in revenue and that "we're getting to $10 billion in a couple of years."

Simplify Your #Code: Functional Core, Imperative Shell
https://testing.googleblog.com/2025/10/simplify-your-code-functional-core.html

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Korbinian Moller, Roland Stroop, Mattia Piccinini, Alexander Langmann, Johannes Betz
https://arxiv.org/abs/2509.24313

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uniform or heuristic sampling often produces many infeasible or irrelevant trajectories. We address this limitation with a hybrid framework that learns where to sample while keeping trajectory generation and evaluation fully analytical and verifiable. A reinforcement learning (RL) agent guides the sampling process toward…

Den starter sikkert til samme tid også om du ikke vil se på. #partilederdebatten #Norsktut

GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
Peter Holderrieth, Uriel Singer, Tommi Jaakkola, Ricky T. Q. Chen, Yaron Lipman, Brian Karrer
https://arxiv.org/abs/2509.25170

GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
The performance of flow matching and diffusion models can be greatly improved at inference time using reward alignment algorithms, yet efficiency remains a major limitation. While several algorithms were proposed, we demonstrate that a common bottleneck is the sampling method these algorithms rely on: many algorithms require to sample Markov transitions via SDE sampling, which is significantly less efficient and often less performant than ODE sampling. To remove this bottleneck, we introduce GL…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Sampology:
🎵 Samba Sampo
#Sampology
https://sampology.bandcamp.com/track/samba-sampo
https://open.spotify.com/track/0If1FGdygAT945E4byANCY

SymBoltz.jl: a symbolic-numeric, approximation-free and differentiable linear Einstein-Boltzmann solver
Herman Sletmoen
https://arxiv.org/abs/2509.24740 https://

SymBoltz.jl: a symbolic-numeric, approximation-free and differentiable linear Einstein-Boltzmann solver
SymBoltz is a new Julia package that solves the linear Einstein-Boltzmann equations. It features a symbolic-numeric interface for specifying equations, is free of approximation switching schemes and is compatible with automatic differentiation. Cosmological models are built from replaceable physical components in a way that scales well in model space. The modeler should simply write down their equations, and SymBoltz solves them and eliminates much of the friction in the process. SymBoltz enabl…

Sous les parkings, la ville !
https://www.novethic.fr/environnement/transition-energetique/la-disparition-des-parkings-symbole-de-la-transition-des-villes

La disparition des parkings, symbole de la transition des villes - Novethic
À Rennes, un immense parking va être détruit pour laisser place au fleuve. Un cas qui illustre un mouvement de fond des villes françaises.

La oss snakke om dŸden. En mikrofestival om dŸden i kulturhistorien, i livet og i musikken. Allehelgensdag, sŸndag 2. november, kl 13–17:30, #KulturkirkenJakob. Billetter kr 150 på jakob.no. #oslo

Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler
Liding Zhang, Kuanqi Cai, Yu Zhang, Zhenshan Bing, Chaoqun Wang, Fan Wu, Sami Haddadin, Alois Knoll
https://arxiv.org/abs/2508.21549

Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler
Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-based methods, such as Informed RRT*, which utilize informed set and anytime strategies to expedite path optimization incrementally. Informed sampling-based planners define informed sets as subsets of the problem domain based on the current best solution cost. However, when no solution is found, these planners re-samp…