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22 entries#apple ×

2024

Publishing Apple Notes as a Website

Status: Ideating

Apple Notes ships on all MacOS and iOS devices, and is a quick way to take notes; I have notes that I took 10+ years ago, that have survived many device upgrades.

Notes has many features that makes it a lightweight knowledge management tool like ability to attach pictures directly from the phone, HTML tables, and most recently - Linking to other notes.

This has me wondering if Notes can be used a lightweight authoring tool for a website. However, notes supports only exporting single note to PDF, and nothing much else.

The first step to this is to export the content into a more web friendly format like HTML (or markdown, whence it can be converted to HTML easily).

The first clue down this road is apple-notes-to-sqlite tool written by @simonw.

The exported [notes] table contains HTML in the body column:

CREATE TABLE [notes] (
   [id] TEXT PRIMARY KEY,
   [created] TEXT,
   [updated] TEXT,
   [title] TEXT,
   [body] TEXT
);

If one can build a MacOS native app that can directly publish the notes as webpages to a hosting service, then we can see the classic Desktop based web publishing make a come back in a small way.

At the least this NotesPublisher should have the following options:

  1. Select which folders to export
  2. Select "Home page"
  3. Some kind of navigation.

See also: yakshaves, sqlite, publishing

notes#apple#notes

[2403.09611] MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

arxiv.org

From Apple

In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, consisting of both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.

2023

macOS

MacOS Applications

zero-native | Desktop Apps with Zig + WebView "Build native desktop apps with web UI. Tiny binaries. Minimal memory. Instant rebuilds."

looks interesting

but haven't bought/installed it.

TinyWeb | The Tiniest, Bezeless HTTP Client

New Mac Setup

notes#apple#operating-system#unix

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