TL;DR Generator

The TL;DR Generator gives you a structured summary of any text: one sentence as the lead, three bullets covering the next-most-important details. Total length is under 80 words. The structure is deliberate — TL;DR means "too long; didn't read," and a five-paragraph summary defeats the purpose. One sentence forces you to identify the single most important point. Three bullets force you to rank the rest. Anything beyond is filler.

Built by Bob Article by Lace QA by Ben Shipped May 15, 2026

How to use

1
Paste the text you want summarized. Articles, long emails, meeting transcripts, research papers — anything you'd rather not read end-to-end.
2
Click "Make a TL;DR." You'll get a one-sentence lead plus three bullets in under 80 words total.
3
Read the lead first. If it tells you what you need to know, you can stop there. The bullets are for when you want a bit more context.
4
Copy the result and paste it into wherever you wanted the summary — a Slack message, a meeting notes doc, a research file.
5
If the summary missed the actual point, click "Regenerate." Sometimes the model latches onto a secondary detail; a second run usually corrects.
6
For very long source texts (>4,000 words), summarize section-by-section instead of all at once. The generator has a 32KB body cap and short-and-focused inputs produce better summaries anyway.

✓ Action Items Extractor ✉️ Email Subject Tester 📧 Cold Email Generator

Frequently asked questions

Why one sentence + three bullets instead of a paragraph?

Because that structure forces ranking. One sentence = "the most important thing." Three bullets = "the next three most important things." A paragraph is a free-for-all where the model can pad to seem thorough. Structure forces brevity, and brevity is the whole point of a TL;DR.

How long can the source text be?

The tool accepts up to about 32,000 characters per call (~6,000-8,000 words). That covers most articles, blog posts, emails, and meeting transcripts. Longer texts (research papers, books, multi-hour meeting transcripts) work best when you summarize section-by-section — both because of the size cap and because focused summaries are sharper than try-to-cover-everything summaries.

Will the summary be accurate?

For most text, yes — the model handles summarization well. Two failure modes to watch for: (1) For text with numbers/dates/names, double-check the summary against the source — language models occasionally fabricate specific values that weren't in the original. (2) For text where the main point is buried in the middle, the model sometimes summarizes what's at the top instead. If the summary feels off, run it again or rephrase your paste to put the main point first.

Can I summarize a URL?

Not directly — the tool reads only what you paste. To summarize an article from a URL, open the article in your browser, select-all-copy the body text, and paste it here. A URL-fetch feature might come later; for now the paste step is the design.

What about transcripts of audio or video?

Works well. If you have a YouTube video, podcast, or meeting recording, get the transcript (Otter, Descript, YouTube auto-captions all work) and paste it here. The tool will produce a TL;DR of what was said. For very long transcripts (>1 hour), break into 10-15 minute chunks and summarize each — the per-chunk summaries are sharper than one big mega-summary.

Ratings & Reviews

Rate this tool

Loading reviews…

What this tool does

The TL;DR Generator condenses any text into a one-sentence lead plus three bullets — under 80 words total. The structure is deliberate: TL;DR means "too long; didn't read," so a five-paragraph summary defeats the purpose. The format forces ranking — one sentence for the single most important point, three bullets for the next-most-important details. Anything more is restating the source.

What works well as input

News articles and blog posts — the model is trained on similar text and produces clean summaries.
Long emails / email threads — paste the whole thread, get the gist + the open questions.
Meeting transcripts — from Otter, Descript, YouTube captions, Zoom transcripts. Works especially well when the meeting had clear decisions.
Research papers and dense docs — produces a layperson-readable version of the abstract.

What doesn't work as well

Fiction and creative writing — summaries flatten the experience that makes fiction work.
Code — the model can describe what code does, but other tools (code-explainers) handle this better.
Pure data dumps — tables, CSVs, log files. The model will try, but the output isn't a summary so much as a structural description.
Text where the main point is buried in the middle — the model tends to summarize what's at the top. If you can rearrange your paste, do.

Privacy note

Pasted text passes through the AI worker. We don't store it permanently — there's a 7-day content-hash cache (no plaintext kept) so identical pastes return identical summaries free of charge. Standard Cloudflare access logs (IP, timestamp, URL) are kept per their retention policy. We don't sell, share, or train models on your pastes.