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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 (64867)
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.
64867 Commentaire(s)
  1. user
    AI_Dev_jary
    14/10/2026 à 08h09  commentaire modifé
    Changing a prompt can alter tool selection even when ordinary chat examples still look correct. Treat prompts, model settings and retrieval rules as deployable artifacts with review history. https://ai-software-development.net Before wider exposure, compare the candidate against a fixed evaluation set and inspect failures by workflow. An LLMOps release process should block promotion when a protected behavior regresses, even if the average score improves. An AI deployment workflow needs a tested rollback path for application code and model configuration. Compatibility matters during a partial rollback. An older prompt may depend on a tool schema that a newer application no longer provides.
  2. user
    ML_Systems_jary
    14/10/2026 à 08h09  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.
  3. user
    AI_Engineer_jary
    14/10/2026 à 08h06  commentaire modifé
    A document pipeline should distinguish missing data from uncertain data because leaving both as an empty field makes downstream rules unreliable and gives reviewers no clue about the cause. Document AI development can distinguish absent from unreadable content. Conflicting values need their own state. Validation rules should match the document type. An invoice total may be checked against line items, while a contract date needs nearby clause context. The source region should remain visible to the reviewer. https://ai-software-development.net An intelligent document processing approach also needs a clear correction path. Reviewer edits can improve future evaluation data, but they should not silently rewrite the archived source or erase the original extraction.
  4. user
    Yalelonse
    14/10/2026 à 08h05  commentaire modifé
    One of the more thoughtful posts I have read recently on this topic, and a stop at jewelcoasttradecollective added even more weight to that impression, this is genuinely good content that holds its own against far better known sites in the same space without trying to imitate any of them at all which I appreciate.
  5. user
    AI_Builder_jary
    14/10/2026 à 08h04  commentaire modifé
    An evaluation set should represent the decisions the product actually makes. Mixing harmless wording differences with unsafe tool calls in one average can conceal a release blocker. An AI evaluation framework can assign each test case to a named failure mode. Start with expected behavior, allowed variation and the condition that should fail the case, then keep retrieval, reasoning, formatting and tool execution results separate so a team can locate the regression. https://ai-software-development.net A production AI testing strategy also needs fixed comparison data for model or prompt changes. Human review is useful for disputed cases, but reviewers need the same rubric. Otherwise the evaluation measures reviewer preference rather than product behavior.
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