Search Results for "code::blocks"

Planning With Files is a Claude Code skill — essentially a plugin for AI agent workflows — that adapts the “Manus-style” persistent markdown planning methodology into developer workflows, enabling structured project planning, progress tracking, and knowledge storage using plain text files. Inspired by high-profile agent workflows and context engineering patterns, it uses persistent markdown files (like task_plan.md, progress.md, and findings.md) as the “working memory” for AI agents, overcoming the limitations of ephemeral memory and large context windows that often lead to drift or information loss. ...

...This has a simple, easy-to-learn syntax with features like checklists, highlighting, links, images and more. Remarkable allows you to export your files to PDF and HTML from within the app. The HTML code is even prettified and PDFs have a TOC. You can style your markdown documents however you like. If you don't like the default styles you can use your own. The code you write is highlighted in the Live Preview. This makes Remarkable great for writing software documentation or even taking lecture notes. You can set up Remarkable whatever way you like. ...

Have you always wished Jupyter notebooks were plain text documents? Wished you could edit them in your favorite IDE? And get clear and meaningful diffs when doing version control? Then, Jupytext may well be the tool you’re looking for. Only the notebook inputs (and optionally, the metadata) are included. Text notebooks are well suited for version control. You can also edit or refactor them in an IDE - the .py notebook above is a regular Python file. Text notebooks with a .py or .md extension...

...The system is designed to run iterative cycles of research, coding, testing, and refinement, effectively simulating a “sleep mode” where productive work continues in the background. It integrates with AI tools such as Claude Code to generate solutions, analyze results, and improve outputs over time. The project emphasizes long-running workflows that can explore problem spaces more deeply than manual intervention would typically allow. It also highlights the potential of autonomous agents to handle repetitive or exploratory tasks that would otherwise require significant human effort. ...