Daily Shaarli

All links of one day in a single page.

April 7, 2025

Grab browser links and titles in one click
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A bookmarklet to copy browser tab URLs with titles as rich text and Markdown.

rsync replaced with openrsync on macOS Sequoia | Der Flounder
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On many Unix-based operating systems, rsync is a command line tool for transferring and synchronizing files on a computer, either between storage attached directly to the computer or between another computer located elsewhere on a network. The rsync command line tool has long been included on macOS, but Apple has provided the last version ofโ€ฆ

It's true!

# /usr/bin/rsync --version
openrsync: protocol version 29
rsync version 2.6.9 compatible
Karakeep
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The features list is fantastic.

Karakeep (previously Hoarder) is an open source "Bookmark Everything" app that uses AI for automatically tagging the content you throw at it. The app is built with self-hosting as a first class citizen.

Support for ollama is included.

Mastering diverse control tasks through world models

The full paper is available.

Abstract:

Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement-learning algorithms can be readily applied to tasks similar to what they have been developed for, configuring them for new application domains requires substantial human expertise and experimentation1,2. Here we present the third generation of Dreamer, a general algorithm that outperforms specialized methods across over 150 diverse tasks, with a single configuration. Dreamer learns a model of the environment and improves its behaviour by imagining future scenarios. Robustness techniques based on normalization, balancing and transformations enable stable learning across domains. Applied out of the box, Dreamer is, to our knowledge, the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula. This achievement has been posed as a substantial challenge in artificial intelligence that requires exploring farsighted strategies from pixels and sparse rewards in an open world3. Our work allows solving challenging control problems without extensive experimentation, making reinforcement learning broadly applicable.