Hayden Bleasel, an knowledgeable developer from OpenAI, launched Blume, an open-source documentation framework. Blume shipped to npm as model 1.0.3 the identical day. It is so simple as Drop Markdown into a folder and ship a docs web site. No app boilerplate is written or maintained afterward. The challenge is MIT-licensed and open sourced.
What is Blume?
Blume is a command-line instrument paired with a part library for docs. It reads a folder of Markdown or MDX information. From that folder, it produces a production-grade documentation web site. That output ships navigation, search, theming, and Open Graph pictures. Configuration stays optionally available and is added one file at a time. The code is a TypeScript monorepo; the revealed bundle sits at packages/blume. Blume’s personal documentation, beneath apps/docs, is constructed with Blume itself. It requires Node.js 22.12 or newer. It runs with Bun, pnpm, npm, or yarn.
How Blume Works?
Under the floor, Blume generates and drives a hidden Astro challenge. First, the CLI masses blume.config.ts and scans your content material into a graph. Next, it writes an Astro challenge into a .blume/ listing. Astro then renders each web page by a single catch-all route. That route imports Blume’s shipped elements, the generated knowledge, and your overrides. On every run, .blume/ regenerates, and solely modified information are rewritten. As a end result, sizzling reload stays quick throughout modifying. The core theme ships no consumer framework JavaScript. Consequently, pages rating effectively on Core Web Vitals by default. When you want full management, blume eject promotes the runtime into a standalone Astro app. That ejected challenge nonetheless relies on the blume bundle.
SkillOpt is a text-space optimizer developed by a staff of researchers from Microsoft, Shanghai Jiao Tong University, Tongji University, and Fudan University. SkillChoose trains a single natural-language talent doc whereas the goal mannequin stays frozen. An optimizer mannequin reads scored rollouts and proposes bounded add/delete/substitute edits. A held-out choice cut up accepts an edit solely…
In the rapidly evolving field of agentic AI and AI Agents, staying informed is essential. Here’s a comprehensive, up-to-date list of the top blogs and websites—from industry leaders to academic voices—offering insights, tutorials, and reviews focused on AI agents and Agentic AI in 2025. 1. OpenAI Blog The official blog of OpenAI, creators of landmark…
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Osmosis AI has open-sourced Osmosis-Apply-1.7B, a fine-tuned variant of Qwen3-1.7B, designed to perform highly accurate and structured code merge tasks. Drawing inspiration from IDE agents like Cursor’s “instant apply,” Osmosis-Apply-1.7B is optimized for context-sensitive, function-level code edits. The model achieves strong performance with fewer parameters compared to much larger foundation models by leveraging code-specific formatting…
In this tutorial, we implement a complicated agentic AI system utilizing the CAMEL framework, orchestrating a number of specialised brokers to collaboratively remedy a advanced job. We design a structured multi-agent pipeline consisting of a planner, researcher, author, critic, and rewriter, every with clearly outlined duties and schema-constrained outputs. We combine device utilization, self-consistency sampling,…