Tools for Thought

Tools for Thought (TfT) is focused on exploring and reviewing productivity frameworks, augmented sensemaking, automation, and the evolution of cognitive enhancement tools. It covers strategies, software, and methodologies designed to enhance thinking, reasoning, and knowledge management for individuals and teams, with a strong emphasis on leveraging AI and structured methods.

Productivity Frameworks Augmented Sensemaking Automation for Knowledge Workers Evolution of Cognitive Tools AI and Learning Enhancement Information Management Reading and Comprehension Tools Note-taking and Organization Software and App Reviews Digital Interfaces and Collaboration Large Language Models

The hottest Substack posts of Tools for Thought

And their main takeaways
6 implied HN points 19 Jan 23
  1. Sensemaking is crucial in today's information overload and complex problems.
  2. Commonsense AI can enhance sensemaking by providing vast knowledge and explanations.
  3. Tools like synoptical reading, AI research assistants, and brainstorming setups can significantly improve sensemaking processes.
7 implied HN points 23 Nov 22
  1. The FAST productivity framework is designed to help knowledge workers be more productive.
  2. FAST stands for Filter, Automate, Structure, and Templatize as the four main components.
  3. Filtering is crucial for managing information overload and maintaining focus in knowledge work.
5 implied HN points 28 Dec 22
  1. Automations can help eliminate tedious and repetitive tasks for knowledge workers.
  2. Identifying manual tasks and building automation workflows can save time and increase productivity.
  3. Tools like Hazel, Alfred + Zapier, and Readwise are useful for automating tasks and improving workflow efficiency.
3 HN points 16 Feb 23
  1. Chaos and disorganization harm productivity by draining energy, focus, and causing cognitive taxes
  2. Meaningful structures are essential for productivity and should simplify choice, perception, and computation
  3. To create an effective structure, start with first principles, maintain universality, build a Minimal Viable Structure, simplify, keep it lean and antifragile, and automate
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4 implied HN points 03 Aug 22
  1. The Tools for Thought category has evolved from basic writing and note-taking apps to enhancing our cognitive activities like reasoning and sense-making.
  2. The future of Tools for Thought involves moving towards assisted thinking and reasoning with the use of language models like GPT-3.
  3. A major trend in Tools for Thought is the transition from individual apps to marketplaces/ecosystems, similar to how the Apple AppStore evolved.
2 implied HN points 06 Oct 22
  1. Tana offers a structured approach to note-taking and organization by allowing users to create and maintain structures by design.
  2. Tana provides advanced workflow features built into the core, enabling users to build sophisticated workflows without additional plugins.
  3. Tana offers a modern UI/UX designed with love, including features like automatic tagging and separate backlinks.
2 implied HN points 12 Aug 22
  1. Reading has been around for 5400 years and it's time to upgrade it with tools for better comprehension and focus.
  2. Different reading modes include inspectional, analytical, and synoptical reading, each requiring specific tools for efficiency.
  3. Tools like Glasp and Shortform aid in skimming and summarizing, while Endel and Bionic reading enhance focus and comprehension, GPT-3 and Fermat help in understanding broader subjects.
1 implied HN point 14 Sep 22
  1. Tools for Thought encompass cultural practices, techniques, and methods throughout history.
  2. Tools for Thought should be classified as a software category with clear boundaries.
  3. A suitable metaphor for Tools for Thought is 'Augmented Thinking', enhancing our cognitive abilities.
1 implied HN point 19 Aug 22
  1. Designing new digital-native interfaces for innovative thinking is crucial.
  2. Developing collaborative tools and protocols is essential for collective knowledge management.
  3. Leveraging large language models (LLMs) can revolutionize tasks like summarizing text and organizing knowledge, but challenges like fine-tuning and interoperability remain.