How do I convert a PDF to Markdown for free?

Upload your PDF to mdisbetter.com, click Convert, and get clean structured Markdown in seconds. No signup, no installation — it works directly in your browser.

Why is Markdown better than PDF for AI?

Markdown reduces token usage by up to 95% compared to PDF when feeding documents to AI models like ChatGPT or Claude. PDF contains layout metadata, fonts, and binary data that waste tokens. Markdown preserves only the content structure that AI actually needs.

What file types can MDisBetter convert to Markdown?

MDisBetter converts PDF, Word (.docx), plain text, YouTube videos (transcript extraction), audio files (MP3, WAV, M4A, OGG, FLAC, WEBM), and any web page URL to clean Markdown.

Is MDisBetter free to use?

Yes, MDisBetter is completely free. You get 10 conversions per day with no signup required. All tools work directly in your browser.

How do I extract a YouTube transcript as Markdown?

Paste the YouTube video URL into the YouTube to Markdown tool on mdisbetter.com and click Convert. The tool extracts the transcript and structures it as clean, formatted Markdown with headings and timestamps.

Why can't Cursor read Word documents directly?

Cursor's @ file reference reads workspace text files. Word documents are binary ZIP-of-XML blobs — the chat surface can't parse them inline the way it parses .md, .py, .ts, etc. Pre-converting to Markdown brings the spec into the AI's reach without changing how Cursor itself works.

Where in the repo should converted specs live?

A dedicated subfolder of docs/ : docs/specs/ for product specs, docs/architecture/ for design docs, docs/contracts/ for API contracts, docs/prds/ for product requirement documents. Use slug-style filenames ( auth-flow-spec.md ) so @ references stay readable. For specs you don't want in the public repo, use a parallel local-only folder added to .gitignore.

Can the AI cite specific sections of the spec when implementing?

Yes — provided the spec had heading structure that survived conversion. Reference a section explicitly in your prompt ("per section 4.2") and the AI scopes its implementation to that section. For specs with weak heading structure, spend 5 minutes adding heading styles in Word before converting; payoff in implementation accuracy is large.

How do I keep specs current as they evolve?

When the source Word document is updated, re-convert and replace the .md in the repo. Treat the .md as the canonical workspace artefact (the AI reads only this); the Word file is the editing surface. For specs that change frequently, establish a "convert and commit" cadence after each round of edits.

What about confidential specs that shouldn't live in the public repo?

Use a local-only docs/ folder added to .gitignore — Cursor reads the .md from your local filesystem, the file never gets pushed. For team-shared private specs, use a private repo or a synced folder outside the public repo with the parallel-folder pattern. The point is bringing the spec content into Cursor's context, not committing it publicly.

Word to Markdown for Cursor — AI-Assisted Implementation from Docs

Why Word specs are invisible to Cursor

Cursor's context system reads workspace files. Word documents — even if they live in a synced folder — are binary blobs the chat surface can't @-reference cleanly. The PRD that defined the feature, the security spec that constrains the implementation, the API contract the team agreed to last sprint: all of it lives outside the AI's reach unless converted to plain text.

Convert each spec to Markdown, commit (or symlink) into the repo at docs/specs/, and Cursor treats it like any other source file. @feature-x-spec.md in the chat pulls the document into context; the AI implements features against the documented requirements rather than guessing from code shape alone.

The implementation workflow

Open Word to Markdown, upload the spec, download the .md, save into docs/specs/. In Cursor's chat, prompt: "@auth-flow-spec.md implement the password-reset endpoint per section 4.2, write tests covering the edge cases listed in section 5". The AI now writes code constrained by your spec rather than by its training distribution.

For multi-source coding workflows: pair with PDF API references (PDF for Cursor), web docs (URL for Cursor), and recorded design discussions (Audio for Cursor, Video for Cursor). Every documentation modality lands in the workspace as @-referenceable Markdown.

Tool	Cost	Unit
Text to MD, EPUB to MD, MD to PDF, MD Cleaner, Merger, Chunker, Token Counter, Context Builder	Free	—
Word to MD	0.5 credit	per page
Excel to MD	0.5 credit	per conversion
Single URL Scrape	0.5 credit	per call
Site Crawl	1 credit	per page
Translate	1 credit	per 10 000 chars (min 1, free re-translation on cache hit)
Prompt Optimizer	1 credit	per call
System Prompt Generator	1 credit	per call
Audio to MD	2 credits	per minute
Video to MD	2 credits	per minute
YouTube to MD	2 credits	per minute
Image OCR	4 credits	per image (0 on cache hit)
PDF to MD	4 credits	per page
PPTX to MD	4 credits	per slide

Word to Markdown for Cursor — Specs and PRDs as Code Context

Why Word specs are invisible to Cursor

The implementation workflow

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