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‎src/config/last_updated.json‎

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‎src/content/en/2025/accessibility.md‎

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Hidde de Vries <a hreflang="en" href="https://hidde.blog/ai-for-accessible-components/">contrasts how humans and language models approach accessible component code</a>. Humans base HTML, CSS, and ARIA decisions on specifications, user needs, assistive technology behavior, and platform quirks, all guided by intentions for the interface. LLMs instead predict likely code from training data, which is problematic because most existing code has accessibility issues, and the models lack intent or understanding of specific users.
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Adrian Roselli acknowledges that recent advances in computer vision and LLMs have brought real benefits, such as better image descriptions and improved captions and summaries. However, he argues <a hreflang="en" href="https://adrianroselli.com/2023/06/no-ai-will-not-fix-accessibility.html">these tools still lack context and authorship</a>. They can't know why content was created, what a joke or meme depends on, or how an interface is meant to work. Their descriptions and code suggestions can easily miss the point or mislead users.
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Adrian Roselli acknowledges that recent advances in computer vision and LLMs can potentially help readers distill complex articles into understandable summaries. However, he argues <a hreflang="en" href="https://adrianroselli.com/2023/06/no-ai-will-not-fix-accessibility.html">these tools still lack context and authorship</a>. They can't know why content was created, what a joke or meme depends on, or how an interface is meant to work. Their descriptions and code suggestions can easily miss the point or mislead users.
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AI raises significant ethical concerns that go beyond accessibility.
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{{ figure_markup(
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caption="Map of accessible countries by Top Level Domain (TLD).",
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description="Displayed visually in a world map, the most accessible countries are Norway with 87%, Finland with 86%, followed by Canada, USA, UK, Sweden, Ireland, Australia, New Zealand, Austria, Belgium, Switzerland, Denmark, and South Africa. China is the least accessible by Top Level Domain, with close to 67%.",
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chart_url="https://docs.google.com/spreadsheets/d/e/2PACX-1vQFD-7C6Jv6q1JyviDsKosRlVwaok7g7nRCQ9NGMw5MaAAohL7EcDejVwgp13Z_T2S_57Zi0YaVb7st/pubchart?oid=1554186781&format=interactive",
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sheets_gid="1037208406",

‎src/content/en/2025/fonts.md‎

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results: https://docs.google.com/spreadsheets/d/1otdu4p_CCI70B4FVzw6k02frStsPMrQoFu7jUim_0Bg/edit
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featured_quote: The promise of variable fonts (fine-tuning typography with continuous variation) is supported by high levels of technical integration, but still in an early phase of creative exploration for most designers—many are using variable fonts as convenient multi-weight files, not yet as fully dynamic resources for typographic expression.
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featured_stat_1: 72%
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featured_stat_label_1: websites self-hosting fonts in some capacity
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featured_stat_label_1: Websites self-hosting fonts in some capacity
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featured_stat_2: 40%
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featured_stat_label_2: websites using variable fonts
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featured_stat_3: 82%
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featured_stat_label_3: font requests served as WOFF2
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featured_stat_label_2: Websites using variable fonts
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featured_stat_3: 65%
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featured_stat_label_3: Font requests served as WOFF2
2020
doi: 10.5281/zenodo.18246295
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---
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‎src/content/en/2025/pwa.md‎

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<figcaption>{{ figure_link(caption="Top 10 used advanced capabilities in PWAs.", sheets_gid="220067822", sql_file="fugu.sql") }}</figcaption>
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</figure>
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There is a complete separate chapter dedicated to [capabilities](./capabilities.md) to dive deeper in the adoption that these sort of APIs have had in 2025.
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## Notifications and PWAs
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Notifications make sense for apps as they allow the user to re-engage with the application. This is a controversial capability as there is considerable bad UX and dark patterns to try to get users to accept them. The data shows that in both desktop and mobile, the most common action a user takes is to ignore these requests.

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