How to host a personal website for free using GitHub Pages and static site generators

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Developers looking to publish a website without server costs can use static site hosting, which delivers pre-built files directly to users without backend processing. GitHub Pages is a free platform that serves the contents of a GitHub repository as a fully functional website, making it a practical option for static sites. Static sites display the same fixed content to every visitor and are typically built with HTML alone, requiring no database or complex server-side logic. To simplify managing multiple pages, static site generators like Jekyll can automate the build process, letting developers write in Markdown and compile ready-to-serve HTML files. GitHub Pages natively supports Jekyll, meaning the platform can interpret Markdown files and render them as HTML pages without a separate build step.

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Developer Ships Python Logging Package to PyPI After Two Years of Repeated Friction

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ProgrammingDEV Community ·

Sci-Fi Literature May Have Already Shaped How AI Systems Think and Behave

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ProgrammingDEV Community ·

Structured prompts beat rule lists for stopping AI agents from inventing fake code

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ProgrammingDEV Community ·

Why AI assistants lose track of time and act on stale information

AI language models receive only a rough sense of the current date at the start of a conversation and have no built-in way to assess how fresh the information they retrieve actually is. This becomes a practical problem when models cite outdated prices, availability, compliance policies, or job titles without flagging that the data may have changed. Simply providing the current time to a model does not solve the issue, since knowing the date differs from knowing how old a specific piece of retrieved information is. The proposed fix draws on a GPS-like approach: stamping each piece of retrieved context with when it was accurate, attaching a decay model suited to that type of fact, and re-evaluating freshness every time the context is used. The author is developing a context integrity layer designed to give AI pipelines, including RAG systems, a reliable signal about information age and trustworthiness.

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