Tootfinder

Simple, an AI-powered health coaching app to help users lose weight, raised a $35M Series B led by Kevin Hart's HartBeat Ventures (Dominic-Madori Davis/TechCrunch)
https://techcrunch.com/2025/10/01/kevin-harts-vc-firm-leads-35m-series-…

Kevin Hart’s VC firm leads $35M Series B for weight-loss app Simple | TechCrunch
Simple is an AI-powered health coaching app that helps people lose weight.

Zufallsfund: im @… liegen alle 49 Jahrgänge der Satire-Zeitschrift "Simplicissimus" (1896-1944). Darin zu blättern ist gar großartig: https://

Simplicissimus magazine : Langen, Albert, 1869-1909 : Free Download, Borrow, and Streaming : Internet Archive
Simplicissimus was a literary, illustrated satirical German weekly magazine started by Albert Langen in April 1896. The last issue appeared on September 13,...

from my link log —
VBARE / versioned BARE: A simple alternative to Protobuf & Cap'n Proto for schema evolution.
https://www.rivet.dev/blog/2025-09-24-vbare-simple-schema-evolution-with-maximum-performance
saved 2025-…

VBARE: A simple alternative to Protobuf & Cap'n Proto for schema evolution
At Rivet, we're building an open-source alternative to Cloudflare Durable Objects — a tool for running stateful compute workloads. VBARE is a small but crucial component in meeting the demanding performance requirements of Rivet Actors.

my solution to the nightmare that is "SPI modes"

Bio-essential sugars in samples from asteroid Bennu: #Bennu Samples: https://www.nasa.gov/missions/osiris-rex/sugars-gum-stardust-found-in-nasas-asteroid-bennu-samples/

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
Yanbo Xu, Yu Wu, Sungjae Park, Zhizhuo Zhou, Shubham Tulsiani
https://arxiv.org/abs/2510.01184 htt…

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or broader distribution than the training distribution. We build on the observation that these models leverage (learned) score functions of noisy data distributions for sampling and show that rescaling these allows one to effectively control a `local' sampling temperature. Notably, this approach does not require any finetuning or alterations to training…

Two new NERDS papers: Algorithmic bias, street network simplification
https://nerds.itu.dk/2025/11/03/two-new-nerds-papers-algorithmic-bias-street-network-simplification/

Unified boson sampling
Luca Bianchi, Carlo Marconi, Laura Ares, Davide Bacco, Jan Sperling
https://arxiv.org/abs/2509.02058 https://arxiv.org/pdf/2509.0205…

Unified boson sampling
Boson sampling is a key candidate for demonstrating quantum advantage, and has already yielded significant advances in quantum simulation, machine learning, and graph theory. In this work, a unification and extension of distinct forms of boson sampling is developed. The devised protocol merges discrete-variable scattershot boson sampling with continuous-variable Gaussian boson sampling. Thereby, it is rendered possible to harness the complexity of more interesting states, such as squeezed photo…

Stable first order theories as simplicial profinite sets
Misha Gavrilovich
https://arxiv.org/abs/2510.00854 https://arxiv.org/pdf/2510.00854

Stable first order theories as simplicial profinite sets
We rewrite simplicially the standard definitions of a complete first order theory, a model of it, and various characterisations of stability of a complete first order theory. In our reformulations the simplicial language replaces the standard definitions based on syntax, making them formally unnecessary. We view a complete first-order theory as a symmetric simplicial object in the category of profinite sets and open continuous maps, defined by the functor sending a finite set of variables int…

Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities
Takuo Matsubara, Andrew Duncan, Simon Cotter, Konstantinos Zygalakis
https://arxiv.org/abs/2509.01437 ht…

Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities
We introduce bandit importance sampling (BIS), a new class of importance sampling methods designed for settings where the target density is expensive to evaluate. In contrast to adaptive importance sampling, which optimises a proposal distribution, BIS directly designs the samples through a sequential strategy that combines space-filling designs with multi-armed bandits. Our method leverages Gaussian process surrogates to guide sample selection, enabling efficient exploration of the parameter s…