Recap

recapext.xyz
Productivity Open Source

Recap is a productivity tool designed to help users bypass the context window limitations of ChatGPT by automatically splitting long texts into optimized segments for summarization. Aimed at researchers, students, …

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May 20, 2026
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Best for: Academic Researchers, Business Analysts, …
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Open Source

About Recap

TL;DR

Recap is a productivity tool designed to help users bypass the context window limitations of ChatGPT by automatically splitting long texts into optimized segments for summarization. Aimed at researchers, students, …

Recap is a productivity tool designed to help users bypass the context window limitations of ChatGPT by automatically splitting long texts into optimized segments for summarization. Aimed at researchers, students, and professionals who work with extensive documents, transcripts, or articles, the application automates the process of dividing large volumes of information into logical chunks. It then interfaces with ChatGPT to generate cohesive and structured summaries of each section, ultimately compiling them into a comprehensive overview. By streamlining this workflow, Recap saves users from the tedious manual task of copying and pasting smaller portions of text into AI prompts. It is highly beneficial for anyone looking to quickly digest long-form content, from academic papers to lengthy business reports, without losing key insights in the process.

Use Cases

Real-world scenarios where Recap saves time.

Use Case 1: Chapter-by-Chapter Thesis Reading\nProblem: Uploading a complete 80-page dissertation into ChatGPT often triggers context limits or results in shallow summaries.\nSolution: Recap automatically divides long documents into logical chapters, requesting sequential summaries from ChatGPT.\nExample: A graduate student gets an organized, highly dense synthesis of a long research paper without manual copy-pasting.\n\n

Use Case 2: Long Transcript Condensing\nProblem: Transcripts from 2-hour board meetings contain too much conversational fluff to review quickly.\nSolution: Segment-splits the transcription text and applies Chain of Density summarizing models directly via the browser.\nExample: An operations coordinator runs the extension over a long call transcript to extract high-density action items.\n\n

Use Case 3: Structured Web Research Synthesis\nProblem: Collecting insights from long, scrolling research portals requires manually organizing different web sections.\nSolution: A browser extension interface allows users to highlight specific areas of any web page and generate structured outputs.\nExample: A market analyst highlights section blocks on a competitor's terms of service, outputting structured pricing rules.

Key Features

What you get out of the box.

  • Smart long-text split algorithms\n- Integrated browser extension popup controls\n- Chain of Density high-density summaries\n- Custom OpenAI API host integrations\n- Web-based model selection overlays

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