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Google Fonts

Sébastien Merour
publié le 27/03/2020 à 17h47 | modifé le 25/04/2020 à 16h54
Catégorie : Front-End | Commentaires (64632)
Google Fonts
Description

Google Fonts est une service d'hébergement de polices de caractères créé en 2010. Ce service, créé par Google, permet d'afficher des polices de caractères spécifiques sur un site web, grâce à un fichier CSS disponible sur le site de Google Fonts. Les développeurs peuvent faire appel à ce fichier CSS en mode "embed", ou bien en téléchargeant le fichier CSS pour l'intégrer ensuite en local sur le site. Il est préférable de l'utiliser en mode "embed" pour ne pas ralentir l'affichage du site.

Fonctionnalités
Police d'écriture
Année de création
2010
Licence
SIL Open Font License 1.1 / Apache License
Langage
CSS
Liens
Aux dernières nouvelles
960 polices de caractères gratuites. Polices populaires : Open Sans, Roboto, Lato, Slabo, Oswald and Lobster.
64632 Commentaire(s)
  1. user
    Markwaive
    14/10/2026 à 00h40  commentaire modifé
    Will recommend this to a couple of friends who have been asking about this exact topic, and after wildorchardmerchantstudio I have even more reason to do so, the kind of site that earns word of mouth rather than chasing it through aggressive marketing or paid placements is always a treat to find online.
  2. user
    DevinPaM
    14/10/2026 à 00h36  commentaire modifé
    Worth pointing out the careful word choice in this post, no buzzwords and no jargon, and a look at linencovecraftcollective continued that disciplined vocabulary, sites that resist the pull of trendy language are sites that will read well in five years and this one is clearly built for that kind of long durability.
  3. user
    ML_Systems_jary
    14/10/2026 à 00h36  commentaire modifé
    An AI feature inside a SaaS product inherits the application's security model. Model access must not bypass tenant boundaries or expose records the current user cannot open. The service context at AI integration services is relevant, but authorization should remain in application code rather than prompts. Use AI SaaS integration planning for the HTML reference. The plaintext project URL, https://ai-software-development.net, can be retained where formatting is removed. Map rate limits, timeouts and provider errors to product behavior users already understand. A fallback should preserve the transaction or draft rather than discard it. Logging also needs redaction rules because model inputs can contain customer data. These boundaries belong in the integration design before prompt tuning begins.
  4. user
    AI_Engineer_jary
    14/10/2026 à 00h34  commentaire modifé
    Red teaming should cover the application around the model, not only adversarial prompts, because retrieved documents can contain instructions, tool outputs may carry untrusted text and authorization can fail between services. AI red team planning should trace how each input reaches a privileged action. Test whether the system follows content from an untrusted source, exposes hidden context or retries a blocked action through another tool. https://ai-software-development.net An AI security evaluation should record the attempted path and the control that stopped it. That evidence distinguishes a resilient workflow from a model that merely refused one wording. Retest the path after changes to prompts, retrieval rules or tool permissions.
  5. user
    AI_Engineer_jary
    14/10/2026 à 00h33  commentaire modifé
    A single quality score can hide the failure that matters most in a financial workflow. Tests should separate retrieval errors from calculation errors. Policy violations and unsupported statements need their own labels. This FinTech AI evaluation approach helps define failures by their operational consequence. Test ordinary and ambiguous requests. Then add stale records plus attempts to bypass permissions. https://ai-software-development.net Release gates need a documented response for each failure class. Some outputs can be corrected automatically, while others should stop the workflow and request review. A financial AI development plan should keep model updates reversible and preserve enough context to reproduce a failed decision without retaining unnecessary personal data.
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