Every Markdown Conversion

Turn any PDF into clean, structured Markdown your AI can actually read. Headings, lists, and tables are preserved — boilerplate is stripped.

Paste raw text and get clean, well-structured Markdown back. Headings, lists, code blocks, and links are detected automatically.

Convert Microsoft Word documents to pristine Markdown in seconds. Headings, styles, tables, and images are mapped correctly.

Drop a video file or paste a link from any platform and get a clean Markdown transcript. Speakers, timestamps, and chapters are preserved.

Paste a YouTube link and get a clean Markdown transcript with chapters, timestamps, and speaker hints. Free, fast, no API key needed.

Paste a TikTok link and get the full spoken transcript back as clean Markdown. No download, no screen recording, no retyping what the captions show.

Upload any audio file and get a clean, formatted Markdown transcript with H2 sections at topic shifts and optional timestamps.

Paste a URL and get a clean Markdown version of the page — boilerplate stripped, main content preserved. Crawl whole sites in one click.

Drop in a rough prompt and get a polished, structured version that consistently produces better answers from ChatGPT, Claude, and Gemini.

Upload an Excel file and get clean Markdown tables, one per sheet. Headers, formulas, and number formats are handled correctly.

Turn presentations into structured Markdown — slide titles, bullets, speaker notes, and embedded text are all preserved.

Drop an image and get the text inside it as clean Markdown. Works on screenshots, scans, photos of documents, whiteboards, and slides.

Upload an EPUB and get a structured Markdown ebook — chapters, sections, footnotes, and inline formatting preserved.

Paste messy Markdown and get a clean, consistent, well-linted version back. Removes artifacts, fixes spacing, normalizes lists.

Convert Markdown into elegant, professionally typeset PDF documents in one click. Choose a theme, hit export, share.

Drop several Markdown files and merge them into one structured document. Headings are renumbered, links are kept working.

Split large texts into optimal chunks for embeddings and RAG, with configurable size, overlap, and structure-aware boundaries.

Paste text and get an exact token count for every major LLM. See cost, context-window usage, and per-section breakdowns.

Pick documents, pick a model, and we build the highest-signal context window that fits — deduplicated, ordered, and tagged.

Describe your AI assistant in plain English and get back a production-quality system prompt with role, rules, and output format.

Translate Markdown into 50+ languages while preserving every heading, list, link, code block, and table exactly as written.

Simple, transparent pricing. Every account gets 50 free credits per month; upgrade or grab a one-time credit pack when you need more. No hidden fees.

A free Markdown-to-PDF converter that doesn't watermark your output, doesn't cap file size below useful limits, and doesn't demand an email before letting you download. Same engine as our paid styled converter — just without the custom styling layer.

When the default Markdown-to-PDF doesn't cut it: pick a custom font, add a logo header, configure page numbers, set per-section margins, brand the output. The same engine as our free converter, with the styling layer that turns "PDF of my Markdown" into "PDF that looks like it came from a designer".

Convert Markdown to clean, semantic HTML — ready to drop into a CMS, paste into an email, embed in a doc site, or ship as a standalone page. Headings, tables, code blocks, and links survive intact.

A free Markdown to HTML converter with no watermark, no signup, no email harvesting, no "free" tier that secretly truncates your output. Drop your Markdown, copy the HTML, move on with your day.

Coming soon. Text-to-speech (TTS) converts written text into spoken audio — the inverse of transcription. We're building this. For now, see the inverse direction: <a href="/convert/audio-to-markdown">Audio to Markdown</a>.

Coming soon. Convert Markdown documents to audio — listen to your notes, articles, and documents on the go. For now, see the inverse direction: <a href="/convert/audio-to-markdown">Audio to Markdown</a>.

Coming soon. Export Markdown documents (<code>.md</code>) to Microsoft Word (<code>.docx</code>) — share your work with audiences who don't use Markdown. We're building this. For now, see the inverse direction: <a href="/convert/word-to-markdown">Word to Markdown</a>, or use Pandoc locally.

Coming soon. A free online Markdown-to-DOCX converter — drop in a <code>.md</code> file, get a Word document back, no signup. We're building this. For now, see the inverse direction at <a href="/convert/word-to-markdown">Word to Markdown</a>, or use Pandoc locally.

ChatGPT will accept a PDF, but it pays for it: page numbers, footers, repeated headers and broken column layout all count as tokens. Convert the PDF to Markdown first and the model only sees the actual content. Same document, 60–95% fewer tokens, noticeably better answers.

Claude's 200k context window is a luxury you waste when you upload a PDF directly. Claude reads the layout — headers, footers, two-column noise — alongside your text. Convert to Markdown first and Claude can spend that context on long-form reasoning instead of parsing artefacts.

Gemini ships the largest context window on the market — 1 million tokens on 2.5 Pro. That is plenty of room for raw PDFs, which is exactly why people stop bothering with conversion. Big mistake: a 200-page PDF that fills 800k tokens of layout noise will give worse answers than the same document at 200k tokens of clean Markdown.

There is no "correct" format for documents — but there is a correct format for documents you intend to feed to a language model. Markdown is the de facto standard: every major LLM was trained on it, every API accepts it, and every model produces better output from it. PDF is for printing. Markdown is for AI.

RAG is only as good as what you index. Raw PDF text — with its broken column boundaries, repeating headers and orphan page numbers — produces chunks that are simultaneously too noisy to embed cleanly and too disconnected to synthesise from. Convert to Markdown first and you get free chunk boundaries: every <code>##</code> is a natural section break.

LangChain ships PyPDFLoader, UnstructuredPDFLoader, and a half-dozen other PDF loaders. They all wrestle with the same problem: PDF text extraction is unreliable, and you end up writing post-processing every time. Convert to Markdown upstream and you skip the wrestle entirely — feed it to TextLoader and chunk by headers.

LlamaIndex's SimpleDirectoryReader will happily ingest a PDF, but its default node-parsing on PDF text is generic — sentence splitting on whatever the PDF parser hands back. Pre-convert to Markdown and you can use MarkdownNodeParser, which builds a node tree that mirrors the document's real heading hierarchy.

Whatever vector store you use — Pinecone, Chroma, Weaviate, Qdrant, pgvector — its retrieval quality is bounded by the embeddings you feed it, and your embeddings are bounded by your input text. Raw PDF chunks embed layout noise alongside content. Markdown chunks embed only content.

Agents call tools. Tools return data. If the tool returns a PDF blob, the agent has to call another tool to extract text, lose the structure, and reason on noise. If the tool returns Markdown, the agent reads it natively — same way a human reads a README.

Cursor is wonderful when it knows the library. It's embarrassing when it doesn't — inventing methods, hallucinating signatures, citing versions that never existed. The fix is to give it the docs. The fix that actually works is to give it the docs as Markdown, dropped in the workspace where Cursor's context engine will pick them up.

Embedding models compress meaning into vectors. They also compress noise into vectors. Pre-converting PDF to Markdown changes the noise floor: less furniture, more content, denser semantic signal. The same chunk, the same model, a measurably better vector.

Most "clean my PDF for AI" tools strip too much (losing structure) or not enough (keeping page numbers and footers). The right cleaning is opinionated: drop everything an LLM doesn't need, keep everything it does. Markdown conversion does this by design.

Obsidian was built for Markdown, not PDF. A PDF in your vault is a dead-end attachment — no search, no graph, no backlinks. Converted to Markdown, the same document becomes a first-class node: searchable, linkable, and visible on the graph.

Notion can import a PDF, but it dumps it as a single embedded preview — no editable blocks, no search inside the content, no way to link from a database row to a specific section. Convert to Markdown first, paste into a Notion page, and every heading, list and table becomes a native Notion block you can edit, link, and query.

Specs that live as PDF attachments on a Jira ticket might as well not exist for the engineering team. The same document committed to the repo as Markdown is searchable, diffable, reviewable in PRs, and rendered automatically by GitHub. Conversion turns a dead artefact into living documentation.

You already live in VS Code for code. Converting PDFs to Markdown lets you live there for documents too — split-pane preview, find-in-files, Git-diff for revisions, AI assistance via Copilot or Cursor, all the muscle memory you already have.

Hugo turns Markdown into websites. The hard part is going from "PDF in a folder" to "Markdown with the right front matter for Hugo to know what to do with it". Conversion solves the first half; a small front-matter convention solves the second.

Jekyll powers GitHub Pages and a lot of long-running technical blogs. Its conventions for posts (<code>_posts/YYYY-MM-DD-title.md</code>) and front matter are simple but rigid — converting a PDF into a publishable Jekyll post is mostly about getting the filename and front matter right.

MkDocs (especially with the Material theme) is the documentation site of choice for many engineering teams. Migrating PDF documentation into it is mostly about converting to Markdown and editing <code>mkdocs.yml</code> to wire the new pages into the navigation.

Docusaurus is React-flavoured documentation: Markdown content with MDX components, automatic sidebars, version branching, search out of the box. Migrating PDF docs into it gives you all of those features on content that was previously a static download link.

Most enterprise Confluence spaces are graveyards of PDF attachments — read once, never updated, never linked. Converting them to Markdown pages turns dead artefacts into living wiki content: searchable, editable inline, linkable from anywhere, and ready to migrate the day you outgrow Confluence.

Logseq is outline-first — every line is a block, every block can be referenced from anywhere. PDFs uploaded directly stay opaque attachments. Converted to Markdown, the same content becomes a tree of referenceable blocks where every paragraph has its own UUID and backlink graph entry.

ChatGPT's browse tool quietly fails on paywalled sites, JavaScript-rendered apps, geo-blocked domains, and any page that takes longer than a few seconds to load. The fallback — pasting the URL's raw HTML — drowns the model in <code>&lt;div&gt;</code> soup, ad markup, and tracking scripts. Convert to Markdown first and ChatGPT sees the article instead of the page.

Claude doesn't browse the web by default. You either copy-paste, attach a screenshot, or feed it scraped HTML — all of which waste the 200k context window on layout. Converting URLs to Markdown gives Claude exactly what it's best at: long-form, structured prose with real headings.

Gemini 2.5 Pro will fetch a URL on its own — and on a clean, fast-loading site, that's convenient. On anything else (paywalls, JS-heavy pages, sites with anti-bot measures, or content you want to pin to a specific snapshot), pre-conversion is the only way to get the page reliably and cleanly into Gemini's 1M-token window.

HTML is what browsers render. Markdown is what LLMs read. Every major model — GPT, Claude, Gemini, Llama, Mistral — was trained on Markdown documentation and reads it as semantic structure. The same content as raw HTML gets parsed as a wall of div soup. Convert once and every downstream model benefits.

Scraping the web for RAG is mostly scraping HTML. The result is a corpus full of nav menus, footer columns, cookie banners, and ad markers — embedded alongside the actual content. Convert URLs to Markdown first and the embeddings concentrate on what the page is about, not how it was decorated.

LangChain's WebBaseLoader is BeautifulSoup with extra steps — same brittleness, same per-site cleanup, same pollution of every chunk with boilerplate. Pre-convert URLs to Markdown and you can use the much simpler TextLoader pipeline, with deterministic output and header-aware chunking ready out of the box.

LlamaIndex's SimpleWebPageReader is convenient and shallow — it fetches a URL, strips some tags, returns text. The text then flatlines through SentenceSplitter, losing every shred of section structure the page had. Pre-convert to Markdown and you can use MarkdownNodeParser, which builds nodes that mirror the page's real heading hierarchy.

A vector database's retrieval quality is bounded by chunk quality, and chunk quality is bounded by input quality. Embedding raw HTML scraped from the web means embedding navigation menus, cookie banners, and ad slots alongside content — every chunk gets pulled toward boilerplate. Pre-convert URLs to Markdown and embeddings concentrate on what each page is actually about.

An agent that calls a "fetch URL" tool and gets back raw HTML wastes most of its context on layout. The next step is always "extract the article text" — a second tool call, more tokens, more latency. Have the fetch tool return Markdown directly and the agent skips the second call entirely.

Cursor knows the popular libraries it was trained on. It hallucinates everything else — niche libraries, internal SDKs, recently-released APIs, beta features. The fix is to give it the real docs as Markdown files in the workspace, where Cursor's indexer treats them as first-class context.

Windsurf (and the broader Codeium stack) is excellent at the libraries Codeium's indexer has seen and unreliable on everything else: niche packages, internal SDKs, brand-new APIs, beta features. The fix is the same as in any AI editor — give it the real docs as Markdown files in the workspace, where Windsurf's indexer treats them as first-class context for Cascade.

An embedding is the average of everything the embedder sees. Feed it a chunk that contains 200 words of article and 800 words of nav menu, and the resulting vector points toward "this page has a sidebar". Convert the URL to Markdown first and the same chunk becomes 200 words of article — and the vector points toward what the article is actually about. Same embedder, same dimensions, dramatically different retrieval.

Obsidian Web Clipper is convenient — and inconsistent. Clip the same article from three different sites and you get three different mixes of nav junk, ad placeholders, and broken images. Convert via URL-to-Markdown and you get clean, structured Markdown every time, ready for [[wikilinks]], #tags, and YAML front matter.

Notion's Web Clipper drops a URL into your workspace as a half-rendered preview that you can't edit, can't search inside, and can't link to specific sections. Convert the URL to Markdown first, paste into a Notion page, and the same content becomes native blocks: editable headings, toggleable sections, queryable tables.

GitHub speaks Markdown — specifically GitHub-Flavored Markdown (GFM). Pasting a web article into an issue or README as raw HTML produces a rendering disaster; GitHub strips most tags. Convert the URL to Markdown first and the same content drops cleanly into READMEs, wiki pages, issues, and discussions.

Hugo is one Markdown file per page, plus front matter. Migrating web content into a Hugo site usually means manually rewriting articles — slow, error-prone, and full of formatting drift. Convert URLs to Markdown with auto-generated front matter and the migration becomes a directory drop.

Jekyll posts are Markdown files in <code>_posts/</code> with a date-prefixed filename and a YAML front matter block. Converting web URLs into that exact shape — content, metadata, filename — turns "republish this article on my Jekyll site" from a 30-minute manual chore into a one-click web conversion (via <a href="/convert/url-to-markdown">mdisbetter.com/convert/url-to-markdown</a>) plus a tiny front-matter wrapper.

MkDocs eats Markdown and produces a documentation website. The hard part is supplying the Markdown — usually written by hand. Convert existing web content (vendor docs, internal wiki pages, reference articles) to Markdown and you can stand up a documentation site from existing material in an afternoon.

Confluence accepts Markdown imports natively (Cloud) or via plugin (Server/DC). Migrating external web content via URL-to-Markdown produces native Confluence blocks — searchable, editable, linkable — instead of the embedded link previews you get when you just paste a URL.

Logseq is outline-first — every line is a referenceable block. Most web clippers produce flat prose blobs that Logseq treats as one unbroken paragraph. Convert URLs with outline-aware structure and the same content becomes a tree of blocks where every paragraph has its own UUID and backlink graph entry.

Reading docs in a browser while writing code in VS Code is two-window context-switching tax all day long. Convert the doc URL to Markdown, drop the <code>.md</code> file into your repo, hit <code>Ctrl+Shift+V</code>, and the preview opens in a side pane. Same screen, no tab switching, full search inside the doc, version-controlled with the project.

Docusaurus is the modern docs-as-code default — Markdown/MDX in <code>docs/</code>, a sidebars config, built-in versioning, blazing-fast builds. The hard part of any migration into Docusaurus is supplying the Markdown when your existing content lives on a wiki, a legacy CMS, or a scattering of static pages. Convert each source URL on the web tool, save as <code>.md</code>, drop into the right folder — the migration becomes a directory tree.

Hand ChatGPT a flat block of transcript text and it can answer "what was discussed". Hand it a Markdown transcript with <code>##</code> topic headings and inline timestamps and it can answer "what was decided about pricing, and when", because the recording's actual structure is finally visible.

Claude's 200K context window comfortably fits a full day of meeting transcripts, but only if the transcripts are structured. Markdown with <code>## Topic [HH:MM:SS]</code> headings turns long-form audio into the kind of document Claude was trained to reason over: clear sections, timestamped quotes, navigable structure.

Gemini 2.5 will accept audio directly, and on a clean ten-minute clip that is convenient. On a two-hour podcast, an interview series, or a long meeting you want to actually edit and re-use, the right move is to convert first. Markdown gives you a transcript you can hand-correct, chunk by topic section, and cross-reference against PDFs and web pages in the same Gemini conversation.

Audio is for listening. Plain-text transcripts are for searching. Structured Markdown (with H2 headings at topic shifts, timestamps, and clean paragraphing) is the only format that lets a language model do anything beyond keyword-match. Every modern LLM benefits; the gain is largest on long, multi-topic recordings.

RAG over audio is hard because raw transcripts are flat. Chunking by character count breaks mid-thought, embeddings average across unrelated passages, and retrieval surfaces fragments that lose context. Convert to Markdown first and chunking by section becomes one line of code: chunks become coherent topics, embeddings cluster on meaning, retrieval surfaces complete exchanges.

LangChain has loaders for SRT, VTT, and plain-text transcripts, and they all dump flat text into your pipeline, leaving you to write per-format segmentation regex. Pre-convert to Markdown and TextLoader plus MarkdownHeaderTextSplitter handles everything: each topic section becomes its own document, with the section heading and timestamp already in metadata.

LlamaIndex's default node parsing on transcripts flattens the structure: one document, sentence-split into orphan nodes, no section context preserved. Pre-convert audio to structured Markdown and use MarkdownNodeParser, so each topic section becomes a discrete node, the heading and timestamp live in node metadata, and retrieval can finally surface coherent exchanges.

Audio is the largest unsearched corpus most teams own. Hours of meeting recordings, podcast episodes, interview tapes — none of it semantically searchable until it's text in a vector DB. Convert to structured Markdown, chunk by topic section, embed, and the whole archive becomes queryable in the same way your text content already is.

An agent that receives "audio input" usually receives "audio someone hands to a transcription step that returns flat text" — and then has to mentally re-derive everything that flat text threw away. Convert to structured Markdown earlier in the chain and the agent gets a transcript it can actually reason over: explicit topics, explicit timing, explicit section boundaries.

Most architectural decisions are made verbally — in a whiteboard session, a kickoff call, an unrecorded hallway argument that someone later types up badly. Transcribe the audio, convert to structured Markdown, drop into the repo as <code>docs/decisions/</code>. Cursor's indexer then treats the meeting as first-class context, and the AI writes code that reflects what the team actually decided.

Obsidian was built for written notes. Most thinking happens in voice — meetings, walking notes, dictated drafts, podcast reactions you don't want to lose. Transcribe the audio, convert to structured Markdown with topic sections and timestamps, drop into the vault. Voice notes become first-class citizens of the graph: searchable, linkable, taggable, surfaced by backlinks.

Notion's built-in meeting templates assume someone types notes in real time. They never quite work: important moments get missed, details get fuzzy, action items get lost. Transcribe the recording, convert to structured Markdown, paste into a Notion page. Every topic shift becomes a heading block, every quote is timestamped, every action item is grep-able.

Most engineering decisions are made in calls and lost to memory. The dissents disappear. The reasoning evaporates. Six months later, no one remembers why the system is structured the way it is — and the engineer who made the call has long moved on. Transcribe the meeting, convert to GitHub-Flavored Markdown, commit as <code>docs/adr/</code>. Now the decision is part of the repo's permanent record.

Some thinking is faster spoken than typed. A long-form design note, a debug session post-mortem, a code review walkthrough — recording the verbal version takes a quarter the time of writing it from scratch. Convert to Markdown, open in VS Code, edit and refactor with all the editor's tools — and ship the polished result.

Podcast audio is invisible to search engines. The same content as a Hugo blog post — title, date, description, body of structured Markdown — gets indexed by Google and surfaced for every relevant query. Convert each episode's audio to Markdown, drop into <code>content/podcast/</code>, run <code>hugo build</code>. The audio archive becomes a searchable, citable, link-bait-able body of work.

Jekyll posts live in <code>_posts/</code> as Markdown files with date-prefixed names and YAML frontmatter. Podcast episodes naturally fit that shape: each episode has a publish date, a title, a guest list, and a body that wants to be the transcript. Convert audio to Markdown, drop into <code>_posts/</code>, run <code>jekyll build</code> — the audio archive joins your existing blog as searchable text content.

Google Docs has voice typing, which is fine for one person dictating in real time. For recordings (meetings, interviews, podcasts) voice typing fails: it only captures live audio through the mic, gives you no timestamps, and produces a flat block of text. Convert the audio externally to structured Markdown, paste into Google Docs, and you get a properly formatted, navigable document.

Confluence is where most enterprise teams document things they'll need to reference later — and where meeting minutes go to die, written hastily by whoever drew the short straw. Transcribe the actual recording, convert to structured Markdown, import to Confluence. Action items become searchable, decisions get attributed, and the wiki finally reflects what was discussed instead of what someone half-remembered.

Logseq is outline-first — every line is a referenceable block with its own UUID. Most transcription tools produce flat prose blobs that Logseq treats as one long paragraph. Convert audio with structured Markdown output and the same content becomes a proper outline: each topic section is a block you can [[link]] to, ((reference)) from anywhere, and surface in graph view.

Slack voice messages and huddles are great for the speaker and a tax on everyone else. Listening to a colleague's rambling 4-minute voice note is rarely the right use of anyone's time. Transcribe the audio, convert to structured Markdown, drop the summary into the channel. Teammates can skim in 30 seconds what would have taken 4 minutes to listen to.

ChatGPT will summarise a YouTube link, but the summary is shallow — it works from metadata and a few sampled frames, not the full content. Paste the video URL into our converter (or upload an MP4 directly), get back structured Markdown with timestamps, and ChatGPT can finally answer specific questions about a 90-minute talk: what was said, at what minute, with what supporting argument.

Claude's 200K context window can hold an entire conference's worth of talks — a dozen 45-minute sessions, fully transcribed, available to every prompt. The bottleneck has never been Claude; it's been getting video content into a format Claude can read. Convert each video to structured Markdown once, drop into a Project, and the rest is a one-prompt query away.

Gemini 2.5 will accept a video file and answer questions about it — the most impressive demo in the space. It is also a black box: no transcript surfaces, you cannot edit what was extracted, and you pay for the video processing on every query. Convert to Markdown first and you trade the demo for a controllable artefact: edit the text, add your own headings, cross-reference with documents, re-prompt freely.

No language model watches video. They read text — and the question is only what kind of text. A flat auto-generated YouTube caption gives the model 80% of the words and none of the structure. Markdown with timestamps and chapter headings gives it 100% of the words and the navigation cues every modern LLM was trained to recognise.

Video is the largest unsearched corpus most teams own — internal training videos, recorded conferences, podcast back-catalogues, course modules. None of it semantically searchable until it is structured text in a vector DB. Convert to Markdown, chunk by chapter and topic, embed, and the whole archive becomes queryable in the same way your text content already is.

LangChain has YoutubeLoader and various caption parsers — they all return flat text plus a timestamp blob, leaving you to write per-format chapter-detection regex. Pre-convert to Markdown and TextLoader plus MarkdownHeaderTextSplitter handles everything: each chapter becomes its own document with the chapter title and timestamp already in metadata.

LlamaIndex's default node parsing on a video transcript collapses chapter structure: one document, sentence-split into orphan nodes, no chapter context preserved. Pre-convert to structured Markdown and use MarkdownNodeParser, so each chapter or topic section becomes a discrete node, the heading and timestamp live in node metadata, and retrieval surfaces coherent sections instead of fragments.

Hours of conference recordings, podcast episodes, internal training videos, and course modules sit on hard drives doing nothing. Convert to structured Markdown, chunk by chapter, embed, and the whole archive becomes queryable in the same pipelines you already run on text. Video stops being write-only.

An agent that "watches a video" usually receives "auto-captions someone's tool spat out" — and then has to mentally re-derive everything that flat text threw away. Convert to structured Markdown earlier in the chain and the agent gets a transcript it can actually reason over: explicit chapters, explicit topics, explicit timestamps. Less tokens spent re-parsing, more tokens available for actual planning.

You watched a 90-minute tutorial on a new framework. The relevant patterns are in your head, but they're not in Cursor's context. Convert the video to Markdown via <a href="/convert/video-to-markdown">Video to Markdown</a>, drop into <code>docs/learning/</code>, and Cursor's indexer treats the tutorial as first-class context. The AI now writes code that follows the patterns the tutorial actually demonstrated, not generic advice from training data.

You watch hours of conferences, podcasts, and courses every month — and remember almost none of it. Obsidian was built to solve exactly this problem for written content; converting video to Markdown extends the same workflow to spoken content. Transcribe, tag, link, and the talks become first-class citizens of your knowledge graph.

Notion can hold a video embed, but the embed is opaque — your team can't search inside it, can't comment on a specific moment, can't turn a chapter into a database entry. Convert the video to structured Markdown via <a href="/convert/video-to-markdown">Video to Markdown</a>, paste into Notion, and the content becomes native: H2 chapter headings become toggleable sections, timestamps anchor comments, and the whole transcript joins your team's searchable knowledge base.

You record a 12-minute walkthrough of how the new feature works. The video lives on YouTube or an internal video host; six months later, the link is broken, the playback speed is wrong, and a new contributor needs to know what was shown at minute 7. Convert the video to Markdown via <a href="/convert/video-to-markdown">Video to Markdown</a>, commit the <code>.md</code> to <code>docs/walkthroughs/</code>, and the content lives in the repo forever — searchable, diffable, and version-controlled alongside the code it documents.

A video on YouTube earns one set of SEO signals. A transcribed blog post of the same content on your domain earns a completely different set — Google indexes the words, your site ranks on the topics, and you own the traffic. Convert the video to Markdown, drop into <code>content/posts/</code>, ship.

Jekyll runs the world's GitHub Pages sites — and most of them under-publish because their authors record videos that never become text. Transcribe the video, drop the Markdown into <code>_posts/</code>, push, GitHub Pages builds. The video lives on YouTube, the transcript ranks in organic search on your domain.

Confluence holds your team's written wiki. The other 60% of institutional knowledge — recorded all-hands, customer calls, design reviews — lives as opaque video files in some shared drive, indexed by nothing. Convert the recordings to structured Markdown, import into Confluence, and the team's entire decision history becomes searchable from the same wiki everyone already opens.

Logseq treats every line as a node in your knowledge graph. Video content — lectures, conference talks, recorded courses — fits the model perfectly when it arrives as structured Markdown: each chapter heading becomes a top-level outline block, each paragraph a child node, each tag and reference a graph edge. Watch the video, convert, paste, and the talk becomes navigable in the same outline interface as your daily notes.

A Google Doc is the team's default surface for "let's read this together and discuss". Video content has historically been excluded from that surface — you can share a YouTube link, but you can't comment on minute 14:22, can't edit a misquote, can't track changes as the team refines the meaning. Convert the video to Markdown, paste into Google Docs, and the transcript becomes a normal collaborative document.

Drop a Word file into ChatGPT and the model receives more than your prose — it gets the entire OOXML envelope: rsidR markers, paragraph properties, run properties, default style references, theme XML. None of it is content; all of it consumes tokens. Convert to Markdown first via <a href="/convert/word-to-markdown">Word to Markdown</a> and ChatGPT sees the same words with 30-50% fewer tokens and zero formatting noise.

Claude is unusually good at faithful citation when the source is structured. Word documents fed in as .docx force Claude to wade through XML overhead before it can reason; converted to Markdown they read cleanly. With 200K tokens of context, a single Project can hold an entire team's document library: every SOP, every policy, every spec — converted once, queried forever.

Gemini will accept a .docx and answer questions about it — convenient for one-off questions, opaque for everything else. Convert to Markdown first and you trade the demo for a controllable artefact: edit any extraction errors, chunk by section, cross-reference with PDFs and web pages, re-feed the same content to multiple downstream prompts without re-paying processing tokens each time.

Every modern LLM was trained on more Markdown than .docx. Markdown is a first-class citizen of the model's training data; .docx is a foreign envelope the model has to mentally strip before reading content. Converting Word to Markdown before feeding it to AI is not a stylistic preference — it's the difference between the model reading your prose and the model parsing OOXML.

Most enterprise knowledge lives in .docx — policies, specs, SOPs, contracts, training materials, board memos. None of it is semantically searchable until it becomes structured text in a vector database. Convert each Word document to Markdown, chunk by heading hierarchy, embed — and the corpus becomes queryable.

Docx2txtLoader is the standard LangChain loader for Word documents — and it flattens every heading into plain text. Section structure disappears, downstream chunking has nothing to anchor on, and your retrieval surfaces context-less fragments. Pre-convert to Markdown and TextLoader plus MarkdownHeaderTextSplitter handles structure cleanly: each section becomes its own document with the heading path already in metadata.

LlamaIndex's DocxReader extracts text from .docx files; the structure that gave the document meaning gets flattened in the process. Pre-convert to Markdown, parse with <code>MarkdownNodeParser</code>, and each section becomes a discrete node — heading hierarchy preserved, metadata pre-populated, retrieval naturally scoped to sections that actually mean something.

Most enterprises sit on a decade of Word documents — policies, specs, SOPs, training materials, board memos, vendor contracts. None of it is semantically searchable until each file becomes structured chunks in a vector database. Convert to Markdown, chunk by heading hierarchy, embed, upsert — and the corpus your team already maintains becomes the most valuable internal search surface in the company.

Cursor's @ file reference is the killer feature for AI-assisted coding — but it only works on text files in your workspace. Word specs and PRDs sit outside the workspace, locked in a format Cursor cannot reference. Convert them to Markdown via <a href="/convert/word-to-markdown">Word to Markdown</a>, drop the .md into your repo (or a parallel docs/ folder), and Cursor can finally pull spec content into prompts: "@auth-spec.md implement the password reset flow as described in section 3".

Years of Word documents — meeting notes, research, project plans, drafts — locked in a format that Obsidian doesn't natively understand. Convert each .docx to Markdown, drop into the vault, and the content joins your linked-thinking system: backlinks become possible, tags become searchable, the [[wiki-link]] graph absorbs documents that have lived for years as isolated files.

Notion has a "Import from Word" feature. Try it on any non-trivial document and you'll see what it gets wrong: heading levels collapse, lists break apart, tables lose alignment, code formatting vanishes. The Markdown import path, by contrast, is one of Notion's best-supported. Convert your Word file to Markdown first, paste, and the page reads exactly the way the source document was structured.

Word documents committed to Git are write-once artefacts: each save creates a new binary blob, diffs are useless, merge conflicts are unresolvable, code review is impossible. The same content as Markdown is a first-class Git citizen — line-by-line diffs, conflict-free merges, reviewable PRs, grep-able from the command line. Migrate the docs once and documentation joins the repo's real workflow.

Word's editing surface is optimised for visual document creation — toolbars, ribbons, style galleries. Useful when you're writing the document, irrelevant when you're extracting structured information from it or making bulk edits. Convert to Markdown, open in VS Code, and you trade the WYSIWYG canvas for a developer's editor: regex find-and-replace across files, multi-cursor editing, Git history, side-by-side preview.

Years of Word documents — articles, whitepapers, research notes — sitting on a shared drive, indexable only by filename and last-modified date. Convert them to Markdown, drop into a Hugo content directory with auto-generated frontmatter (title from H1, date from file mtime, description from the lead paragraph), and the entire archive becomes a fast, searchable static site.

Years of Word articles — newsletters, briefings, internal write-ups — that never made it onto the blog because the conversion friction was too high. Convert each .docx to Markdown, save into <code>_posts/YYYY-MM-DD-slug.md</code> with Jekyll frontmatter, and the article goes live on the next build. The blog backlog you've been meaning to publish for years is suddenly a 30-minute task per article.

Documentation that lives as a folder of Word files is documentation nobody reads — no search, no navigation, no shareable URL. Convert each .docx to Markdown, drop into a MkDocs <code>docs/</code> folder, run <code>mkdocs serve</code>, and the same content becomes a fast searchable site with the Material theme's polished navigation.

Confluence has a "Insert Word document" feature. Run it on a 30-page spec and you'll spend an hour fixing the result — heading levels shift, lists fragment, tables break alignment, code formatting evaporates. Convert to Markdown first, paste into Confluence, and the import is dramatically cleaner: the structural fidelity Markdown enforces survives the round-trip into Confluence's storage format.

Google Docs imports .docx natively — and brings along every Word artefact: invisible track-changes, leftover style references, awkward column breaks, font fallbacks. The Markdown intermediate path filters those out: convert Word to Markdown (which forces structure into a constrained format), paste into Google Docs (which has excellent Markdown paste support). The resulting Doc reads cleaner than the direct import.

Paste a tiktok.com link into ChatGPT and one of two things happens: it declines, or it invents a plausible summary from the URL and the caption. Neither is the video. The fix takes under a minute. Paste the same link into TikTok Transcript, copy the Markdown that comes back, paste that into your chat. A 45 second clip rounds up to one minute and costs 2 credits, and a free account carries 50 credits every month, so about 25 clips before you spend anything.

Claude has no way to open tiktok.com and no ear for audio, so a pasted link buys you nothing. What Claude does have is a context window large enough to hold an entire creator account at once, and that is the real reason to convert. A 45 second TikTok is roughly 120 spoken words, about 160 tokens, which means a hundred clips still leave most of the 200K window free. Transcribe each one at TikTok Transcript for 2 credits a minute, and analyse the set rather than the clip.

Notion will embed a TikTok, and that embed is a black box. Workspace search never looks inside it, teammates cannot comment on a specific line, and a database of embeds tells you nothing you did not already know. Convert the clip at TikTok Transcript, paste the Markdown into the page body, and the words join Notion search like any other text. A one minute clip costs 2 credits, so a 50 credit free month covers about 25 of them.

Saving TikToks to a bookmark note is how a swipe file dies. Obsidian indexes the URL and nothing else, so the phrase you actually wanted to find again is unreachable and the note shows up as an orphan in graph view. Convert the clip at TikTok Transcript, download the <code>.md</code> straight into a vault folder, and the words are indexed the moment Obsidian sees the file. A sub-minute clip costs 2 credits, and 50 free credits a month covers roughly 25.

Nobody approves an ad script by sending around a TikTok link. You need text a client can comment on, a lawyer can flag and an editor can suggest changes to. Convert the clip at TikTok Transcript, then paste the Markdown into a Doc. One step matters before you paste: open Tools, Preferences and tick Enable Markdown, otherwise the headings land as literal hash characters. A one minute clip costs 2 credits, and the free plan includes 50 a month.

If you post four TikToks a week you have already written roughly 600 spoken words on your topic, which is a full newsletter issue sitting in a format nobody can read. Transcribe each clip at TikTok Transcript, paste the Markdown into a draft, and spend your writing time editing rather than starting from a blank page. Four one-minute clips cost 8 credits, and the free plan includes 50 credits every month, so a month of weekly issues fits inside it.

Everything you have ever said on TikTok is invisible to search. The words live in an audio track on someone else's domain, the captions are pixels in the video frame, and Google indexes neither. Convert the clips at TikTok Transcript and the same sentences become text on your own site, where they can rank. One caution first: a single 45 second clip is about 110 words, which is a thin page. The move is combining a themed set into one real post.

There is no way to export subtitles from TikTok, because TikTok never made a subtitle file. The captions you see are rendered into the video frames as pixels, which is why they cannot be selected, copied or downloaded. Transcribe the clip at TikTok Transcript and the timing data comes back with the text, so the result panel offers <code>.srt</code> and <code>.vtt</code> downloads next to the Markdown. Cost is the same either way: 2 credits per minute of video.

A finished TikTok cannot be reposted anywhere without looking like a repost. The script can. Transcribe the clip at TikTok Transcript for 2 credits a minute and you hold the one asset that travels: the words, in a format you can reshape per platform. A creator posting twelve clips a month spends 24 credits doing this, which fits inside the 50 free credits a month, and gets six destinations out of every video.

The standard RAG recipe says split by heading, then sub-split at 800 tokens. Applied to short-form video it does nothing, because a 45 second TikTok transcribes to about 110 words, or 150 tokens, which is already well under any chunk budget. The correct move is the opposite of splitting: one clip is one chunk, one vector, one row. Transcribe each at 2 credits per minute and spend your engineering effort on metadata, which is what makes a short video corpus retrievable.

Academic papers are the worst-case PDF: two columns, dense citations, equations rendered as glyphs, figures with captions that wander. Generic PDF extractors mangle reading order. Our converter handles arXiv preprints, journal articles, and conference proceedings as a special case — column-aware, citation-aware, equation-aware.

A "scanned" PDF is just images of pages — there's no text underneath. Most PDF tools stare at it blankly. Our converter detects when a PDF has no text layer, runs OCR automatically, and emits Markdown the same way as for digital sources. One upload, one download, no separate OCR step.

Tables are where PDF-to-anything-else converters reveal whether they actually work. Most produce text dumps where each row is concatenated into a sentence. Ours produces real Markdown tables — pipes, alignment, headers — that render correctly in every Markdown viewer.

Equations are images of glyphs, not text. The default conversion of an equation-heavy paper is gibberish — half the symbols missing, the rest garbled. We detect equation regions, run them through a math-aware recogniser, and emit valid LaTeX that any Markdown viewer with MathJax or KaTeX renders correctly.

A two-column PDF read top-to-bottom is gibberish — half a sentence from column 1, then a jump back to the top of column 2. Our converter analyses column geometry on every page and emits text in the order you actually read it.

Slide decks exported as PDF are particularly easy to convert well — the structure is already there: one slide = one section, each bullet is a list item, the title is a heading. Our converter recognises slide-deck PDFs and emits the obvious Markdown.

OCR alone gives you raw text. OCR plus Markdown conversion gives you structure: headings, lists, tables, paragraphs — what the document originally was. We bundle both steps so the output is immediately usable, not just searchable.

Invoices are structured by convention but encoded as PDF. Converting them to Markdown surfaces the structure: a header section with metadata, a line-items table, a totals block. The result feeds straight into AI summarisers, expense trackers, or accounting workflows.

Contracts are PDFs because they're signed on paper. Once executed, they often need to be searched, compared with other contracts, or reviewed by AI for risk. None of those work well on PDF. Markdown conversion preserves the numbered-clause structure that makes contracts easy to navigate.

Textbooks have the strongest structure of any document type — chapter, section, sub-section, definition box, key-terms list, problem set. PDF flattens all of it. Markdown conversion brings it back: each chapter is an H1, each section is an H2, definitions stay in dedicated callouts, problem sets are separable for study-guide tooling.

Docs sites are content wrapped in chrome: a sidebar tree, a top navbar, an "Edit on GitHub" button, a search box, a footer with links. The valuable bit is the prose in the middle. Our converter discards the furniture and emits just the article — same as if the maintainer had written it as a Markdown file in the first place.

A modern blog post is 3KB of writing inside 800KB of page weight: ad slots, sticky share buttons, "you may also like" carousels, autoplaying videos, three newsletter popups. None of it is content. Our converter strips it all and gives you the post — title, byline, body, code blocks if any, images with alt text — as Markdown you can actually read or feed to an LLM.

News sites are the worst offenders for page chrome. A typical article page has the body buried under: a sticky header that hides the headline, a video player that autoplays, two sidebar ad columns, a soft paywall overlay, and "more from this author" cards interleaved with paragraphs. Our converter throws all of that away and gives you the article as it would have appeared in a print edition.

API reference pages are the hardest documentation to scrape: rendered from JSON specs by client-side JavaScript, with collapsible endpoint cards and tabbed code samples. Our converter renders the page properly (headless browser when needed), then converts the resulting structure to a clean Markdown reference — endpoints as headings, parameters as tables, examples as fenced code blocks.

Wikipedia is the largest publicly-readable knowledge base on the planet, but raw HTML scrapes turn into noise: edit links next to every section, citation markers like "[1][2][3]" cluttering paragraphs, navboxes at the bottom that swamp the article. Our converter handles the wiki idioms specifically — citations stay as proper Markdown footnotes, infoboxes become tables, navboxes are dropped.

A useful forum thread is half the value of the original docs — the practical answers, the gotchas, the edge cases. Threads are also the hardest pages to scrape: deeply nested reply trees, vote counts attached to every post, expand-collapsed replies, and (on Reddit) infinite scroll. Our converter handles the major forum platforms and emits a structured Markdown conversation that preserves the discussion shape.

Marketing pages are designed to be looked at, not read. The copy is fragmented across hero sections, feature grids, testimonial sliders, pricing tables, FAQ accordions. For competitive analysis, content audits, or feeding pages to an LLM that compares positioning, you want the words — flat, in order, without the design layer. Our converter strips the visual and gives you the message.

A static fetch of a React or Vue SPA returns an empty <code>&lt;div id="root"&gt;</code> and a script tag. Curl, wget, and most scrapers stop there. Our converter detects JavaScript-required pages, runs them in a real headless browser, waits for the content to settle, then extracts. The result is the same Markdown you'd get from a server-rendered equivalent.

A GitHub repo page is the README wrapped in 80% UI: file tree, branch picker, contributor avatars, language stats, sidebar widgets, "About" panel. The README itself is already Markdown, but copying it from the rendered page brings the chrome along — and wiki/issue/discussion pages aren't even raw Markdown to begin with. Our converter extracts just the content from any GitHub URL: rendered README, wiki page, issue body, discussion thread.

Stack Overflow is the largest practical-knowledge corpus on the planet, and its pages are wrapped in some of the densest UI on the web: related-questions sidebar, "linked" panel, "hot network questions", advert slots, comment threads under every post. Our converter pulls out exactly what you came for — the question, the accepted answer, the top alternatives — as clean Markdown notes you can save, search, or feed to an LLM.

An e-commerce product page is 95% UI: image carousel, sticky add-to-cart bar, review carousel, "frequently bought together", "people also viewed", footer columns, cookie banner. The 5% you actually want is the product description, the spec table, and the feature list. Our converter extracts those three things and drops everything else, giving you a clean product summary you can compare against alternatives or feed to AI for analysis.

Tutorials are the most valuable form of online content per word: someone has done the work of figuring out the right sequence of steps, and you want to follow along while you build. The pages they live on are usually buried under ads, autoplay video, comments, "you might also like" widgets, and newsletter popups. Our converter extracts the step-by-step structure as clean Markdown — code blocks, image alt text, ordered headings — that reads as a portable, offline study material.

MP3 is the everyday audio format: podcasts, lecture recordings, voice memos, ripped interviews, archived radio. It compresses small enough to email, plays on every device, and (until you need to read what was said) is invisible to text search. MDisBetter takes any MP3, transcribes it, and emits Markdown with headings and timestamps you can grep, summarise, or feed to an LLM.

WAV is the lossless cousin of MP3: same audio, but stored uncompressed. It's what professional studios record into, what broadcast equipment outputs, what archival projects use to preserve recordings without generational loss. The downside is file size — a 10-minute stereo WAV is ~100 MB. The upside is that transcription accuracy on WAV is the ceiling for what's possible from any source format.

If you've ever opened the Voice Memos app on an iPhone and recorded something, you have an M4A file. It's the default Apple format — AAC audio in an MP4 container, optimised for the iPhone's mic — and most online transcribers force you to convert it to MP3 first. MDisBetter accepts M4A directly: drop the file from your iCloud or AirDrop, get Markdown back.

FLAC (Free Lossless Audio Codec) is the audiophile and archival favourite: the size advantage of compression, with zero loss of audio data. It's used for music ripping where every detail matters, for archival recordings that need to outlast format generations, and for high-end interview capture where the source recording is the master and any transcription artefact you can avoid is a win.

OGG (the container; usually with Vorbis or Opus inside) is the open-source world's answer to MP3. It's what Linux audio recorders default to, what Wikipedia stores its audio samples as, what some Discord and Telegram exports use, and what you'll receive when someone hands you a file from a non-Apple, non-Microsoft pipeline. MDisBetter accepts OGG directly — no need to transcode to MP3 first.

WebM is what your browser records. The MediaRecorder API in Chrome, Firefox, and Edge defaults to WebM containers when capturing microphone audio in the browser. Google Meet exports use WebM. Any web app that records audio (lots of newer voice-note tools, in-browser meeting recorders) ends up with .webm files. MDisBetter accepts them directly without a transcode step.

AAC (Advanced Audio Coding) is the format Apple, YouTube, and most streaming platforms use under the hood. iTunes purchases come as AAC. YouTube audio streams are AAC. Spoken-word podcasts distributed by Apple Podcasts are typically AAC. The bare <code>.aac</code> extension is less common than M4A (which is AAC inside an MP4 container) but the underlying audio is the same — and MDisBetter handles both.

WMA is the format you'll find on professional dictaphones bought in the 2000s, in archival folders of meeting recordings from the Windows-XP era, and in any institution that standardised on Windows Media tooling before MP3 became the universal default. Most modern transcription tools quietly refuse WMA and demand a transcode to MP3 first. MDisBetter accepts WMA directly.

Opus is the most common voice-message format in 2026. WhatsApp voice notes are Opus inside an OGG container. Telegram voice messages are Opus. Discord voice messages are Opus. If someone has sent you a 90-second voice message in the last week, you've been sent an Opus file. MDisBetter transcribes them directly so you can read instead of listening.

A voice memo is the fastest possible way to capture an idea, an interview, a meeting, or a thought you don't want to lose. The slow part is going back and listening to it later — most people record voice memos and never re-open them. MDisBetter turns voice memos into searchable, scannable Markdown so the recording you made on a walk in May is something you can actually use in June.

A podcast is, from Google's perspective, invisible. Audio is opaque to search engines, opaque to grep, opaque to any LLM-based summariser that doesn't have audio understanding built in. The single highest-leverage move a podcaster can make for organic discovery is publishing a full transcript — and MDisBetter turns the audio into structured Markdown ready to drop into a show-notes page or a static-site blog.

Meeting bots have a real cost: they show up in your calendar with a robot icon, they require IT approval at most companies, they're subscription-priced per seat, and they record by default whether the conversation is one you wanted recorded or not. MDisBetter takes the opposite approach — record locally with whatever tool you already use, upload the file when (and only when) you want a transcript, get structured Markdown back. No bot in the meeting, no per-seat fees, no automatic capture of conversations that should've stayed private.

An interview transcript is the source-of-truth for journalism, qualitative research, HR documentation, oral history, and a dozen other use cases. The standard manual process — listen, type, rewind, type — takes 4 hours per hour of audio at typing speed. MDisBetter compresses that to a few minutes of upload + processing time, returning verbatim Markdown with real punctuation, topic-shift sections and inline timestamps that's ready for search, citation, or LLM-assisted analysis.

Re-listening to a 90-minute lecture for one specific concept you half-remember is the most painful study workflow in education. The lecture exists, the answer is in there somewhere, but the only access pattern is sequential. Transcribing the lecture into structured Markdown turns it into a grep-able, study-able, flashcard-extractable text — the same content, vastly more useful for exam prep and revision.

A church, mosque, temple, or synagogue that records its services has a problem: the recording is the canonical record, but it's opaque audio. Members who missed a service can listen, but only sequentially; researchers and historians can't search; the sermon's teaching can't be quoted, indexed, or cross-referenced with other services. Transcribing into structured Markdown turns the audio archive into a searchable text library while keeping the original recording as the master.

A conference call is the worst-case meeting transcription scenario: many speakers, often poor audio quality (some on weak cell connections), interrupted by the occasional "you're on mute", and important enough that someone needs to remember what was decided. MDisBetter handles multi-participant calls — topic-shift sections, time-stamped breaks, action item extraction — and returns Markdown structured for the way conference-call follow-ups actually work.

Voicemail is one of the worst communication formats ever invented. A 90-second message takes 90 seconds to listen to, can't be skimmed, can't be searched, and forces you to remember "the contractor said the deposit is due Friday — or was it Tuesday?". MDisBetter takes voicemail audio and returns text Markdown so you can read instead of listen, decide if action is needed in 10 seconds, and save the important ones into your notes vault for later reference.

Dictating is faster than typing — most people speak at 150 words per minute and type at 40. Doctors dictating clinical notes, lawyers dictating case summaries, executives dictating memos all use voice as the input modality. The downside has always been that the output is one giant paragraph. MDisBetter recognises the structural cues in dictation ("section: history of present illness", "subsection: medications") and returns properly-formatted Markdown with headings, lists, and paragraphs — not a flat wall of text.

YouTube's built-in "Show transcript" gives you a wall of text without punctuation or structure. Paste a YouTube URL into our converter and you get a properly formatted Markdown transcript: chapter markers from the video become H2 headings, speech is punctuated and paragraphed, and timestamps stay as anchors.

MP4 is the most common video container — what most cameras, screen recorders, and apps export. Drop an MP4 into the converter and you get a clean Markdown transcript back, punctuated and with topics broken into H2 sections.

MOV is Apple's native video container — what your iPhone records, what QuickTime saves, what FaceTime exports. Drop a MOV into the converter and you get the transcript as Markdown, with the same structure as any other video format.

WebM is the open web video format — what most browser-based recording tools default to. Async screen-recording tools, Google Meet exports, screen recorders built into Chrome/Firefox, and MediaRecorder API outputs all produce WebM. Drop yours into the converter for a Markdown transcript.

Recorded lectures sit unwatched in download folders because rewatching is slow and re-listening loses focus. Convert them to Markdown and you get scannable notes you can actually use — Ctrl+F for any concept, copy quotes verbatim, build flashcards.

Video podcasts are the dominant format now — Joe Rogan, Lex Fridman, the All-In Pod, every interview show on YouTube. Convert an episode to Markdown and you have show notes you can publish, quotes you can pull for social, and a searchable archive of everything that was said.

Most meeting recordings sit unwatched. Convert them to Markdown and you get a 5-minute scan instead of a 60-minute rewatch — decisions captured, action items extractable by Ctrl+F or by asking an AI to pull them out.

Programming tutorials are gold but unsearchable in video form. Convert a YouTube coding tutorial or a recorded screencast to Markdown and you have a written reference — every command, every snippet, every "wait, what did they say at 23 minutes?" is there as searchable text.

Webinars get watched live and then sit forgotten. Convert the recording to Markdown and the live event becomes evergreen content — blog posts, lead magnets, email courses, social-media snippets, and a searchable knowledge base for future attendees.

A great talk is hours of insight compressed into 18 minutes. Convert it to Markdown and you have a written reference you can quote, study, share, and search — instead of a video you watch once and forget.

Instagram Reels are short-form vertical video — marketing, tips, tutorials, lifestyle content. Convert a Reel to Markdown and you have text for accessibility captions, repurposing into other formats, and content analysis at scale.

Twitch streams are long — 3, 6, sometimes 12+ hours. Rewatching to find the one moment you want is impractical. Convert the VOD to Markdown and you can Ctrl+F for any topic, quote, or game-section.

Facebook still hosts a huge amount of video — Facebook Lives from creators and businesses, group video discussions, shared content from pages. Convert any Facebook video you have access to into a Markdown transcript for archiving, repurposing, and AI analysis.

LinkedIn video has become a primary channel for thought leadership — execs and creators sharing insights via short-form video. Convert a LinkedIn video to Markdown and you can repurpose the same content as a text post, an article, or a newsletter section.

Google Meet recordings save to your Drive as MP4 — useful, but not searchable, not portable, not AI-ready. Convert to Markdown and the meeting becomes a structured document you can paste into Slack, search via Drive, or feed to Claude for action-item extraction.

Zoom is the meeting platform of record for a huge segment of business. The cloud recordings (MP4) sit in your Zoom account or local download folder, mostly unwatched. Convert to Markdown and they become searchable documents — topic sections, timestamps, action items, and references you can find by Ctrl+F.

Microsoft Teams is dominant in enterprise. Meeting recordings save to OneDrive or SharePoint as MP4 and quickly become an ungovernable archive. Convert to Markdown and they become searchable documents that integrate with whatever knowledge base you use.

Spanish is the second-most-spoken language by native speakers worldwide — Spain, Latin America, and the US Hispanic market. Drop a Spanish video into the converter and you get a Markdown transcript that handles the major regional dialects (Castilian, Mexican, Argentinian, Colombian, Caribbean).

French is spoken across France, Quebec, Belgium, Switzerland, and large parts of Africa. The pipeline handles the major regional accents and produces structured Markdown with proper accents and diacritics preserved.

German is spoken across Germany, Austria, and Switzerland with significant variations. The pipeline handles standard Hochdeutsch and the major regional varieties, preserving compound nouns, capitalisation rules, and umlauts.

Portuguese is the official language of Brazil (215+ million speakers) and Portugal (10+ million). Brazilian Portuguese is the world's sixth-most-spoken language by native speakers. The pipeline handles both varieties with proper diacritics and regional vocabulary.

Hindi is the world's third-most-spoken language and India is a massive market for video content (one of the largest YouTube user bases globally). The pipeline handles Hindi in both pure form and as Hinglish (Hindi-English code-switching), with proper Devanagari script output.

Japanese is one of Whisper's strongest non-English languages. The pipeline handles standard Japanese (hyōjungo) plus regional dialects (Kansai-ben is reasonably handled), with proper kanji, hiragana, and katakana output.

Korean (한국어) is the language of K-content — K-pop interviews, K-drama clips, Korean tech and beauty YouTube, Korean podcasts. The pipeline handles standard Seoul Korean accurately and produces clean Hangul output.

Chinese is the most-spoken language in the world by native speakers. The pipeline handles both Mandarin (the standard) and Cantonese, in either simplified or traditional character output, depending on the source variety.

Arabic spans 22 countries from Morocco to Iraq, with Modern Standard Arabic (MSA, الفصحى) used in formal contexts and many regional dialects in everyday speech. The pipeline handles MSA most accurately and major dialects (Egyptian, Levantine, Gulf) reasonably well.

Russian (русский) is spoken in Russia, Belarus, Kazakhstan, and across the post-Soviet space. The pipeline handles standard Russian accurately and produces clean Cyrillic output preserving cases, gender agreement, and proper noun capitalisation.

Italian is the language of design, fashion, food, and a substantial creator economy. The pipeline transcribes standard Italian accurately and handles the major regional varieties from Milan to Naples to Sicily.

Turkish (Türkçe) is spoken by 80+ million people across Turkey and the Turkish diaspora. The country has a rapidly growing tech ecosystem and a substantial YouTube/podcast culture. The pipeline handles standard Istanbul Turkish accurately with full preservation of Turkish-specific characters.

Indonesia is the world's fourth-largest country by population (270+ million) and one of the fastest-growing digital economies. Bahasa Indonesia is spoken across the archipelago. The pipeline handles standard Indonesian accurately with proper output formatting.

Vietnamese (tiếng Việt) is spoken by 95+ million people in Vietnam plus a substantial diaspora. Vietnam's tech sector is growing rapidly. The pipeline handles Vietnamese with full diacritic preservation, though accuracy is lower than for top-tier languages due to less training data.

Thai (ภาษาไทย) is spoken by 70+ million people in Thailand plus international Thai communities. The pipeline handles standard Thai with proper Thai script output, though accuracy is lower than for top-tier Whisper languages.

The .docx format is a ZIP archive of XML — clean, structured, and well-suited to conversion. But Word's authoring style still leaks formatting noise: zero-width characters, smart-quotes, run-level styling that changes mid-word. Our converter normalises that noise into the kind of Markdown you'd have written by hand.

The legacy .doc format is a binary OLE compound file from a different era. Most modern converters silently fail on it or produce mangled output. Ours decodes the binary structure, recovers the text streams, and emits Markdown that's indistinguishable from a .docx conversion.

Word tables are the trickiest part of any Word-to-Markdown conversion. Word lets you merge cells in arbitrary rectangles, stack header rows, embed paragraphs inside cells, and rotate text 90 degrees. Markdown's table syntax (GFM) supports none of those. Our job is to flatten Word's expressive tables into the cleanest possible GFM, and to be honest about what was simplified.

Word documents routinely contain dozens of embedded images: screenshots, diagrams, photos, logos. The .docx format stores them inside the ZIP archive at fixed paths; the conversion job is to extract them, name them sensibly, and rewrite the Markdown to reference them as proper image links.

Track Changes is essential while a Word document is being collaborated on, but once it's done, the markup is noise. Most Markdown converters either include the markup as-is (producing unreadable output) or silently drop content. Ours resolves Track Changes the way Word does — accept all changes, strip comments — and emits the clean final document as Markdown.

Corporate Word templates are a content-extraction nightmare: branded headers on every page, footers with confidentiality disclaimers, logos in the margin, repeating page numbers, watermarks. None of it is content; all of it pollutes a naive Markdown conversion. Our converter recognises the template chrome and strips it, leaving only the actual document content.

Your resume is in Word. Your portfolio is in Markdown. The mismatch is real — and Word resume templates are particularly hostile to conversion because they rely on tables, columns, and tight typography that don't map cleanly to Markdown. Our converter understands resume structure and emits a Markdown CV that looks intentional, not auto-generated.

Business reports in Word are dense documents: 30–80 pages, heavy use of tables and charts, executive summary up front, appendices in the back. Conversion has to preserve the structural hierarchy so a reader (human or AI) can navigate the result. Our converter is tuned for the report genre — it knows what an executive summary looks like and treats it accordingly.

Contracts in Word are highly structured: defined terms in capitals, multi-level numbering (1.1.2, 2.3.4), recitals, signature blocks, exhibits and schedules. Converting to Markdown makes them searchable and AI-ingestible — invaluable for contract review, due diligence, and clause libraries. But there's an honest limit: AI-converted Markdown is a content extraction, not an authoritative legal record.

A thesis or dissertation is the largest single document most people will ever write — 80 to 400 pages, heavy citations, complex tables, equations, multiple chapters with appendices. Converting to Markdown opens it up to digital-first archiving, version control on GitHub, integration with note-taking tools like Obsidian, and AI-assisted research follow-ups.

RTF (Rich Text Format) is the format that refuses to die. It's still the default in some legal practice management software, US federal government document workflows, and old Windows applications that haven't updated since the 90s. The format is a plain-text markup language under the hood — uglier than HTML, but parseable. We turn it into clean Markdown.

ODT is OpenDocument Text — the native format of LibreOffice, OpenOffice, and the broader open-source office ecosystem. It's widely used in European governments (mandated in some), educational institutions worldwide, and the Linux community. The format is XML-based like .docx, but with cleaner conventions and fewer historical quirks.

Apple Pages is the default word processor on Mac. Most Pages users eventually need their content in a portable format — for blogging, GitHub, Obsidian, or AI tools. The cleanest path is to use Pages' built-in DOCX export, then convert that DOCX to Markdown. Two clicks, one upload, one download.

Google Docs added native Markdown export in 2024, but it's rough: tables are flattened to plain text, footnotes are misplaced, image references break, formatting is inconsistent. The cleaner path: export your Google Doc to DOCX, then convert that. Our DOCX pipeline produces the Markdown Google should have.

Standard Operating Procedures live in Word at most companies and they're a perfect fit for Markdown migration. SOPs are highly structured (numbered steps, responsibilities, checklists, decision points) and they need to be searchable, version-controlled, and accessible across teams. Markdown in a wiki gets you all three. Our converter handles the SOP genre conventions cleanly.

Strip a PDF down to its text and nothing else. Useful for ctrl-F across documents, plugging into a search index, or feeding into a script. We strip layout furniture (page numbers, headers, footers) along the way so the text you get is content, not noise.

PDFs are fixed-layout. HTML is flowing. Converting one to the other means making decisions: do you preserve every pixel of the original layout (yielding HTML that looks good only at one width), or recover the document's logical structure and let it reflow naturally? We do the second.

Markdown is for humans (and LLMs). JSON is for code. When your pipeline needs to programmatically navigate a converted document — pull every H2, list every table, count code blocks — JSON output is the right shape. We emit a structured tree where each PDF maps to a JSON object.

You don't want the whole PDF — you want the tables in it. Bank statements, financial reports, invoice line items, scientific data tables. We detect every table in the PDF and emit each as its own CSV file, ready to drop into a spreadsheet or pandas DataFrame.

You need a Word document — for an editable contract, a collaborative draft, a corporate template. PDF to Word converts the PDF to <code>.docx</code> with structure preserved: headings as Word heading styles, lists as Word lists, tables as Word tables. Ready to edit in Word, Google Docs, or LibreOffice.

You want the words on a page, not the entire page. Strip a URL down to its main-content text and nothing else — no menus, no cookie bars, no "subscribe to our newsletter" pop-ups, no related-articles widgets. Useful for piping web content into a script, a search index, or anywhere that wants flat UTF-8 strings.

You want a frozen, shareable, printable copy of a webpage. URL to PDF renders the page in a headless browser at the viewport size you choose (mobile or desktop), strips the page chrome you don't need, and emits a clean PDF — preserved layout, working links, embedded fonts, ready to email or archive.

A single webpage, converted to clean Markdown. Paste the URL, get a <code>.md</code> file back with the article content properly structured: page title as H1, semantic headings, real lists, working links, image references. Boilerplate (nav, cookie banners, sidebars, related-posts widgets) is stripped before conversion. Same engine as <a href="/convert/url-to-markdown">URL to Markdown</a>, focused on the single-page case.

Convert not just one webpage, but every page on a website, in a single crawl job. Point at a domain root or a sitemap URL, set the depth, and get a folder of Markdown files mirroring the site's URL structure — ready to drop into a docs-as-code repo, build an offline mirror, feed a RAG pipeline, or archive a content library before it's rewritten.

You have HTML in hand — copied from a page, exported from a CMS, generated by an editor — and you need it as Markdown. Paste the HTML, click Convert, get clean Markdown back. Headings stay headings, lists stay lists, tables become GFM tables, links keep their hrefs, code blocks keep their language hints. Skip the npm install / pip install / "where do I host the converter" question entirely.

You have HTML and you want the words. Strip every tag, every style, every script — keep only the readable text content. Useful for plain-text email generation, search indexing, NLP preprocessing, or anywhere a string of words is more useful than a tree of markup.

Browser "Save Page As" gives you 50MB of HTML, broken JavaScript, and missing CSS. "Print to PDF" gives you a frozen 5MB image of a page. Saving as Markdown gives you a 30KB plain-text file that opens in any editor on any device, weighs almost nothing on disk, and stays readable in 20 years when the original page is long gone. The right format for an offline reading list, a personal archive, or a portable knowledge base.

You want a frozen, shareable, printable copy of a web page. Web Page to PDF renders the URL in a headless browser, captures the layout faithfully, and emits a clean PDF — preserved layout, working links, embedded fonts. Useful for archival, for sharing a page that might change, for printing, or for sending the visual record of a page to someone who needs it as a document.

You want the words from a website, not the entire site experience. Strip a URL down to its main-content text — no menus, no cookie bars, no "subscribe to our newsletter" pop-ups, no related-articles widgets, no ad slots. Useful for piping web content into a script, a search index, an NLP pipeline, or anywhere that wants flat UTF-8 strings.

You have audio. You want the words from it as text. Drop an MP3, WAV, M4A or any common audio format into the converter and get clean plain-text transcription back. Useful when the downstream consumer is a script, a search index, or any tool that wants flat UTF-8 strings rather than structured Markdown. For AI input where structure helps, use the Markdown variant.

You have a recording of someone speaking. You want the words written down. Speech-to-text is the simplest possible framing of the transcription job: audio in, text out. Upload an audio file, click convert, get plain UTF-8 text back. For interviews and conversations where you want topic structure and timestamps, the Markdown variant is more useful, but for the basic "give me the words" job, plain text is the cleanest output.

AI transcription is the cheap-and-fast alternative to human transcriptionists. Upload your audio file, get a transcript back in minutes instead of days, at a fraction of the cost. Quality is 92-97% on clean recordings — close enough for most working uses, with verification against the audio for any quote that ships in publication. For interviews and long recordings, the Markdown variant adds H2 topic structure and inline timestamps.

MP3 is the everyone-format for audio: podcasts, voice memos, music, audiobooks, recorded calls. When the MP3 contains spoken word and you want the text out of it, MP3-to-text is the job. Upload your MP3, click convert, download a text file with the transcribed words. Works on any MP3 — bitrate, sample rate, length all auto-handled.

You spoke into a recording app. You want the text. Voice-to-text is the simplest case of the transcription pipeline: single speaker, usually short, usually a recorded note or memo rather than a long-form conversation. Upload your voice recording, get the transcribed text back. Works on voice memo files from phones, voice notes from messaging apps (WhatsApp, Telegram, Discord), and any other single-speaker audio.

You recorded a meeting. You want the text. Upload the recording (Zoom, Teams, Google Meet, or any other platform that lets you save the audio/video), get a transcript back in minutes. NOT a real-time meeting bot like Otter or Fireflies that auto-joins your calls — mdisbetter is a manual post-meeting upload tool. For meetings where you want topic-section structure and inline timestamps, use the Markdown variant.

Subtitles are timestamped chunks of text aligned to audio segments. The standard format is SRT (SubRip) — used by YouTube, video editors, and most playback software. Generating subtitles from audio is a common need: captioning a video, adding closed captions for accessibility, creating a translated subtitle track. mdisbetter currently outputs structured Markdown with inline timestamps; for SRT specifically, see the workflow below for converting our Markdown output to SRT format.

You have a recording — interview, lecture, meeting, voice memo, podcast, call, conference talk. You want the text out of it. Upload the recording (audio or video), get the transcript back in minutes. Works on virtually any audio or video format. For longer recordings where structure matters, the Markdown variant adds H2 topic sections and inline timestamps.

You want the words from a YouTube video as text. Maybe to read at your own pace, paste into a doc, search for one specific moment, or feed to an AI for summarisation. Paste the YouTube URL into the converter, get a clean transcript back in minutes. YouTube's own auto-captions are flat, often inaccurate, and locked into the YouTube interface; mdisbetter outputs portable text you can take anywhere.

You have a YouTube URL. You want the text. The simplest possible framing of the job: video URL in, text file out. Paste the link, click convert, download the transcript. Works on any public YouTube video regardless of whether the creator enabled captions. For structured output with H2 topic sections and inline timestamps (much more useful for AI workflows), use the Markdown variant.

You want the subtitles file from a YouTube video — for translation, for offline reference, for editing into your own video, for accessibility purposes. The standard format is SRT (SubRip), supported by virtually every video player and editor. mdisbetter generates the underlying transcript from any YouTube video; convert to SRT for the subtitles-specific use case, or use the Markdown variant if you need a full structured transcript instead of just timestamped caption chunks.

You have a video file. You want the spoken words from it as text. Drop the MP4, MOV, MKV, WebM or AVI file into the converter and the audio is auto-extracted, transcribed, and returned as a clean text file. Useful when the downstream consumer is a script, a search index, or any tool that wants flat UTF-8 strings rather than structured Markdown. For interviews and longer recordings where structure matters, use the Markdown variant.

MP4 is the everyone-format for video: phone recordings, exported edits, screen captures, Zoom recordings, downloaded YouTube videos. When the MP4 contains spoken word and you want the text out of it, MP4-to-text is the job. Upload your MP4, audio is auto-extracted and transcribed, download the text file. Works on any MP4 — resolution, bitrate, codec, length all auto-handled.

You don't want to watch a 45-minute YouTube video; you want the 200-word summary so you can decide if it's worth watching. The two-step workflow: paste the YouTube URL into mdisbetter to get the structured Markdown transcript, then paste the transcript into ChatGPT or Claude with "summarise in 200 words highlighting the 3 key points". Total elapsed time: 5-10 minutes for a video that would have taken 45 minutes to watch. For the full transcript without summarisation, mdisbetter outputs that too.

YouTube auto-generates captions for most videos, but the quality varies wildly — fine for casual English, terrible on technical content, accents, or noisy audio. You want better captions: more accurate, exportable to SRT/VTT, usable in your own video editor or accessibility workflow. mdisbetter generates a typically more accurate transcript using Whisper-class speech recognition; convert to caption format for the captions-specific use case, or use the structured Markdown output for any other workflow.

You want to transcribe a YouTube video — get the spoken words written down as text for reading, searching, or further processing. Paste the YouTube URL into the converter, click Convert, download the transcript file. No browser extension to install, no manual workflow, no caption-sidebar scraping. Works on any public YouTube video regardless of whether the creator enabled captions.

You have a YouTube video — your own, or one you have rights to repurpose — and you want a blog post version. Old workflow: hire a transcriptionist for $1-2 per minute, then a writer to convert the transcript to a blog post. Time: a week. Cost: hundreds of dollars. New workflow: paste the YouTube URL into mdisbetter for the structured transcript, paste the transcript into Claude/ChatGPT with a blog-post conversion prompt, edit the output. Time: 30-60 minutes. Cost: free if you have a free-tier AI account.

Generate a transcript from any video — uploaded file or YouTube URL. The simplest possible framing of the job: video in, transcript out, generated by AI in minutes. mdisbetter handles both audio extraction (for video files) and YouTube downloading (for URLs), then runs Whisper-class speech recognition to produce a clean text transcript. For structured output with H2 topic sections and inline timestamps, the Markdown variant adds significantly more value for downstream workflows.

TikTok generates billions of views per day across content that's often ephemeral and rarely indexed in any searchable way. For creators wanting to repurpose their own TikToks into blog posts or other content, for researchers analysing TikTok content, for marketers tracking TikTok trends — getting the spoken text from TikTok videos is the unlock. Since 1 August 2026, pasting the TikTok link is enough: no download step, no third-party downloader.

Post-COVID, Zoom recordings have become the dominant artefact of work — every meeting, interview, customer call, and webinar leaves a Zoom Cloud Recording behind. Most never get re-watched because nobody's going to sit through 60 minutes when a written transcript would do. Download the Zoom recording, upload to mdisbetter, get the structured transcript back in minutes. Mdisbetter is a manual post-meeting upload tool — NOT a real-time meeting bot like Otter or Fireflies. For real-time auto-joining bots, see those platforms; for manual transcription of recorded Zooms, this is the cheap path.

You have a Word document and you just want the words out of it as plain UTF-8 text — no formatting, no styles, no XML wrapper. Drop the .docx in, get a flat .txt file back. For most "I just need the text" jobs (search indexing, NLP pipelines, copy-pasting into a script), plain text is exactly the right shape. For AI input where structure matters, the Markdown variant is dramatically more useful.

DOCX is a zipped XML format — what you actually want is the text inside, not the OOXML container around it. Drop the .docx in and get a flat .txt back with formatting stripped. The right shape for search indexing, NLP, or any pipeline that wants strings rather than structured documents. For ChatGPT or Claude input, the Markdown variant preserves structure and is dramatically more useful.

Microsoft Word's built-in "Save as Web Page" produces HTML so polluted with proprietary XML namespaces and inline styles that it's nearly unusable on a real website. mdisbetter's Word-to-HTML strips Word's wrapper and outputs clean semantic HTML — proper <code>&lt;h1&gt;</code>, <code>&lt;p&gt;</code>, <code>&lt;ul&gt;</code>, <code>&lt;table&gt;</code> elements with no MSO junk. Drop in a CMS, paste into a static site, or convert further to <a href="/convert/word-to-markdown">Markdown</a> for AI workflows.

Need to publish a Word document on the web without 50KB of MSO XML junk dragged along? Drop the .docx in and get clean semantic HTML5 out — proper heading tags, real list elements, real tables, no inline styles, no proprietary namespaces. CMS-ready, SEO-friendly, easy to style with your own CSS.

Word to PDF is the classic "lock down a document for sharing" workflow — exporting to PDF freezes the layout, prevents casual edits, and gives recipients a portable file that opens identically everywhere. mdisbetter handles this conversion, but the bigger question is what you're using the PDF for. If it's ending up in an AI prompt, PDF is the wrong target — <a href="/convert/word-to-markdown">Word to Markdown</a> is dramatically more useful for LLM input.

Google Docs has no native "Download as Markdown" option (despite years of feature requests), and the third-party add-ons that try to fill this gap want broad permissions to your entire Drive. mdisbetter is the no-permissions-required path: in Google Docs use File → Download → Microsoft Word (.docx), then drop the .docx into mdisbetter and get clean Markdown back. Three clicks, no add-on, no Drive access granted to anyone.

You have a Word document and you need it in a different format. mdisbetter does the conversion in your browser — no software install, no account creation, no watermarks. The most useful target format depends on what you're doing next: <a href="/convert/word-to-markdown">Markdown</a> for AI input or static sites, HTML for web publishing, plain text for search/NLP, PDF for sharing. For 90% of users today, the answer is Markdown.

mdisbetter is a DOCX converter built for the AI era — primary target format is Markdown (the format ChatGPT, Claude, and modern static sites actually want), with HTML, plain text, and PDF as secondary targets. Also handles related formats: <a href="/convert/rtf-to-markdown">RTF</a>, <a href="/convert/odt-to-markdown">ODT</a> (LibreOffice), and legacy .doc files. Browser-based, no software install, no account required.

mdisbetter is the online, browser-based, no-install version of the Word-to-Markdown conversion you might otherwise do with pandoc on the command line. Drop a .docx in, get clean Markdown back in seconds — works on any device with a modern browser, no Office required, no developer tooling required, no Python or Node install required.

DOCX in, .md out. The single most useful conversion in any AI/dev workflow today: take a Word document and produce a clean Markdown file ready for ChatGPT, Claude, Obsidian, GitHub, Hugo, MkDocs, or any of the dozens of tools that natively consume Markdown. mdisbetter does this in seconds, free, in your browser, with no signup.

Someone sent you a .docx and you don't have Word installed. mdisbetter opens .docx files in your browser — no Office, no LibreOffice, no Google Docs, no signup. The "view" is actually a Markdown preview of the document's content (since rendering pixel-exact Word layout in a browser is overkill for the read-and-quote use case), but every word, table, and image is faithfully preserved.

The standard "convert Word to Google Docs" workflow is just uploading the .docx to Google Drive — Google natively imports .docx into Docs format. mdisbetter's value is the alternative path: convert your Word document to Markdown first (with mdisbetter), then either work with the Markdown directly or import the Markdown into Google Docs. The Markdown intermediate is genuinely better when the document is destined for AI input or static-site publishing.

RTF (Rich Text Format) is the cross-platform document format from the early 90s — still ubiquitous in legal workflows, government document exchange, and anywhere documents need to round-trip between Word, WordPerfect, and obscure word processors. mdisbetter converts .rtf files to Markdown for AI/modern workflows, with HTML, plain text, and DOCX as alternative targets.

ODT is the native document format of LibreOffice and OpenOffice — open standard (ISO/IEC 26300), used by governments, EU institutions, and anyone avoiding Microsoft format lock-in. mdisbetter converts .odt files to Markdown for AI/modern workflows, with HTML, plain text, and DOCX as alternative targets. No software install required, browser-based, free.