ai

Introducing Shortcuts Playground: Create Apple Shortcuts with Claude Code or Codex

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Shortcuts Playground is a free, open-source plugin for Claude Code and Codex that enables users to create Apple Shortcuts using natural language prompts, producing ready-to-import shortcut files on Mac. Developed over six months by Federico Viticci, it uses extensive documentation, validation loops, and Apple's own command-line tools to generate mostly accurate shortcuts, allowing users to automate tasks without advanced Shortcuts knowledge. The project aims to democratize and simplify automation on Apple platforms while anticipating future native AI-based solutions from Apple itself.

https://www.macstories.net/stories/introducing-shortcuts-playground/

Atomic — Everything You Know, Connected

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Atomic is an open-source, self-hosted knowledge graph app that organizes notes, articles, and web clips by automatically embedding, tagging, and linking them into a connected knowledge base. It features semantic search, AI-generated wiki synthesis with inline citations, agentic chat for AI interaction scoped to user notes, and a visual spatial canvas to explore the relationships between ideas. Available across desktop, server, iOS, and browser extensions, Atomic helps users build an AI-augmented knowledge system that grows and organizes itself over time.

https://atomicapp.ai/

Tolaria

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Tolaria is an open-source, free note-taking app designed for the AI era that organizes notes as Markdown files with YAML frontmatter, supporting native relationships, Git integration, and Claude Code features. Created by Luca, an experienced content creator and author, Tolaria offers block-based editing, rich Markdown output, version control, and seamless AI tool integration, catering to users who want a powerful, transparent, and flexible knowledge management system.

https://tolaria.md/

OpenAI’s New Codex App Has the Best ‘Computer Use’ Feature I’ve Ever Tested

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OpenAI’s updated Codex app for Mac introduces a native computer-use feature that interacts with multiple Mac apps in the background via advanced accessibility APIs, enabling precise control without bringing them to the foreground. This technology, evolved from the acquired Sky app, allows Codex to automate complex tasks more efficiently and accurately than other models by reading app interface hierarchies rather than relying on screen recording or AppleScript, marking a significant advancement in AI-driven Mac automation.

https://www.macstories.net/notes/openais-new-codex-app-has-the-best-computer-use-feature-ive-ever-tested/

I Tried an Abliterated Local LLM and It Feels Nothing Like the Others

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The article explores “abliterated” local large language models (LLMs) that have their safety guardrails—implemented via reinforced learning from human feedback—removed through a mathematical process called orthogonalization. Unlike traditional uncensored models, abliterated LLMs bypass refusal behaviors and respond directly to any prompt, offering a more raw, unfiltered conversational experience, though sometimes at the cost of stability and reasoning performance. These models appeal to users seeking unrestricted AI interactions on local machines without editorial constraints, highlighting a trade-off between openness and reliability in AI usage.

https://www.makeuseof.com/tried-abliterated-local-llm-nothing-like-others/

How the “AI Loser” May End up Winning

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While other companies race to build the best AI models, Apple is benefiting from the commoditization of intelligence. By leveraging its existing user base and vast data collected through its devices, Apple can create a unique moat around personalized AI experiences. Additionally, Apple’s efficient chip architecture, particularly its unified memory, provides a significant advantage for running AI models locally.

https://adlrocha.substack.com/p/adlrocha-how-the-ai-loser-may-end

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