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LazyReader - a TL;DR machine that runs entirely in your browser (no servers, no keys, no excuses)

Disclosure: this project and post were created with AI assistance - the code was written by an AI agent and tested in a real browser. What I Built LazyReader is a tiny web app that summarizes any long article

Disclosure: this project and post were created with AI assistance - the code was written by an AI agent and tested in a real browser.

What I Built

LazyReader is a tiny web app that summarizes any long article right in your browser. You paste a wall of text, pick how lazy you're feeling ("one breath", "a sip of coffee", or "still lazy but thorough"), and get a TL;DR in seconds.

There is no backend. No API key. No account. No tracking. The AI model downloads once into your browser cache and from then on everything, including the inference itself, happens on your machine. Your text never leaves the tab.

This week's theme is "Slow and Steady": build something that rewards laziness. Reading is work. Skimming is also work. LazyReader removes both: it reads the whole thing so you can stay horizontal.

Demo

Live demo: https://lazyreader.surge.sh

Paste in any long article (I tested it with a bicycle history essay) and hit "Summarize for me". The first run downloads the model (~100 MB); after that it works offline.

The Code

The whole project is three small static files - index.html, style.css and app.js - plus this README. The complete source code is embedded below - all three files, nothing held back.

index.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>LazyReader - TL;DR anything, right in your browser</title>
<meta name="description" content="LazyReader summarizes long articles locally in your browser with open-source AI. No accounts, no API keys, no servers. Paste text, get a TL;DR.">
<link rel="stylesheet" href="style.css">
</head>
<body>
<main class="wrap">
  <header>
    <h1>LazyReader<span class="zzz">💤</span></h1>
    <p class="tagline">Too long; didn't read? Paste it. We'll read it for you.<br>
    <strong>100% local, open-source AI.</strong> Your text never leaves this tab.</p>
  </header>

  <section class="card">
    <label for="input">Drop your wall of text here</label>
    <textarea id="input" rows="10" placeholder="Paste a long article, essay, Terms & Conditions, that 40-page PDF your professor assigned..."></textarea>
    <div class="row">
      <div class="wordcount" id="wordcount">0 words</div>
      <div class="length-picker">
        TL;DR size:
        <select id="length">
          <option value="short">One breath</option>
          <option value="medium" selected>A sip of coffee</option>
          <option value="long">Still lazy, but thorough</option>
        </select>
      </div>
      <button id="go" disabled>Summarize for me 😴</button>
    </div>
  </section>

  <section class="card hidden" id="statuscard">
    <div id="status">Loading the brain (one-time download)...</div>
    <div class="bar"><div id="barfill"></div></div>
  </section>

  <section class="card hidden" id="resultcard">
    <h2>Your TL;DR ☕</h2>
    <div id="output"></div>
    <button id="copy">Copy it and go back to bed</button>
  </section>

  <footer>
    <p>Built with <a href="https://huggingface.co/docs/transformers.js" target="_blank" rel="noopener">Transformers.js</a> and the open-weight
    <a href="https://huggingface.co/sshleifer/distilbart-cnn-12-6" target="_blank" rel="noopener">DistilBART</a> model.
    No accounts. No tracking. No servers..</p>
  </footer>
</main>
<script type="module" src="app.js"></script>
</body>
</html>

app.js
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

env.allowLocalModels = false;

const input = document.getElementById('input');
const go = document.getElementById('go');
const wc = document.getElementById('wordcount');
const statuscard = document.getElementById('statuscard');
const status = document.getElementById('status');
const barfill = document.getElementById('barfill');
const resultcard = document.getElementById('resultcard');
const output = document.getElementById('output');
const copyBtn = document.getElementById('copy');

const LENGTHS = {
  short:  { max: 60,  min: 20 },
  medium: { max: 140, min: 50 },
  long:   { max: 280, min: 110 },
};

let summarizer = null;

input.addEventListener('input', () => {
  const words = input.value.trim().split(/\s+/).filter(Boolean).length;
  wc.textContent = words + ' words';
  go.disabled = words < 30;
  if (words > 0 && words < 30) wc.textContent += ' (need at least 30 - go on, paste something longer)';
});

async function getSummarizer() {
  if (summarizer) return summarizer;
  statuscard.classList.remove('hidden');
  status.textContent = 'Waking up the brain (one-time download, ~100 MB)...';
  summarizer = await pipeline('summarization', 'Xenova/distilbart-cnn-12-6', {
    progress_callback: (p) => {
      if (p.status === 'progress' && p.total) {
        const pct = Math.round((p.loaded / p.total) * 100);
        barfill.style.width = pct + '%';
        status.textContent = 'Downloading the brain... ' + pct + '% (' + p.file + ')';
      } else if (p.status === 'ready') {
        status.textContent = 'Brain loaded. Reading so you don\'t have to...';
      }
    },
  });
  return summarizer;
}

function chunkText(text, maxWords = 550) {
  const paras = text.split(/\n+/).map(s => s.trim()).filter(Boolean);
  const chunks = [];
  let cur = [], curWords = 0;
  for (const p of paras) {
    const w = p.split(/\s+/).length;
    if (curWords + w > maxWords && cur.length) { chunks.push(cur.join(' ')); cur = []; curWords = 0; }
    if (w > maxWords) {
      const words = p.split(/\s+/);
      for (let i = 0; i < words.length; i += maxWords) chunks.push(words.slice(i, i + maxWords).join(' '));
    } else { cur.push(p); curWords += w; }
  }
  if (cur.length) chunks.push(cur.join(' '));
  return chunks;
}

go.addEventListener('click', async () => {
  const text = input.value.trim();
  if (!text) return;
  go.disabled = true;
  resultcard.classList.add('hidden');
  try {
    const sum = await getSummarizer();
    const chunks = chunkText(text);
    const len = LENGTHS[document.getElementById('length').value];
    const partials = [];
    for (let i = 0; i < chunks.length; i++) {
      status.textContent = chunks.length > 1
        ? 'Reading part ' + (i + 1) + ' of ' + chunks.length + '...'
        : 'Reading so you don\'t have to...';
      const out = await sum(chunks[i], { max_new_tokens: len.max, min_new_tokens: len.min });
      partials.push(out[0].summary_text.trim());
      barfill.style.width = Math.round(((i + 1) / chunks.length) * 100) + '%';
    }
    let final = partials.join(' ');
    if (chunks.length > 3 && final.split(/\s+/).length > len.max * 1.5) {
      status.textContent = 'Condensing the condensation...';
      const out = await sum(final, { max_new_tokens: len.max, min_new_tokens: len.min });
      final = out[0].summary_text.trim();
    }
    output.textContent = final;
    resultcard.classList.remove('hidden');
    statuscard.classList.add('hidden');
    barfill.style.width = '0%';
  } catch (e) {
    status.textContent = 'Even the AI got lazy and errored out: ' + e.message;
  } finally {
    go.disabled = false;
  }
});

copyBtn.addEventListener('click', async () => {
  await navigator.clipboard.writeText(output.textContent);
  copyBtn.textContent = 'Copied. Sweet dreams.';
  setTimeout(() => copyBtn.textContent = 'Copy it and go back to bed', 2000);
});

How I Built It

The core is Transformers.js running the open-weight DistilBART CNN 12-6 summarization model through ONNX Runtime Web - WebAssembly by default, WebGPU where available. Because the model runs locally, there is literally nothing to deploy server-side: the demo is static files on surge.sh.

Two details make it practical rather than a toy:

  1. Paragraph-aware chunking. DistilBART handles roughly a thousand tokens. LazyReader splits long texts into ~550-word chunks on paragraph boundaries, summarizes each, and if the joined summary is still too long, condenses the condensation.
  2. Honest laziness UX. The word counter nags you if your "long article" is under 30 words. The copy button congratulates you and tells you to go back to bed.

Why Open Innovation Matters Here

This project is only possible because every layer is open. The model weights are open (DistilBART, distilled from BART, trained on CNN/DailyMail). The runtime is open (Transformers.js, ONNX Runtime). The platform is open (the web itself: no app store, no review board, no 30% cut).

Closed AI would have made this a SaaS with a signup wall and a $9/month tier. Open AI made it a weekend project that respects the reader: no data leaves the device, nobody can be tracked, and the whole thing keeps working even if I disappear. That is what open weights buy you - not just cheaper inference, but software you can actually own.

What's Next

  • Fetch-and-summarize by URL (blocked by CORS today; a tiny optional proxy would fix it)
  • A "lazy mode" browser bookmarklet
  • Multilingual summaries

Built for the Hacktoberfest Open-Source AI Challenge, Week 3 ("Slow and Steady").

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