AI tool comparison
CodeBurn vs MarkItDown
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
CodeBurn
Token cost analytics and waste finder for AI coding tools
75%
Panel ship
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Community
Paid
Entry
CodeBurn is an open-source terminal dashboard that tracks and analyzes your token spend across Claude Code, OpenAI Codex, Cursor, OpenCode, and GitHub Copilot. It classifies coding sessions into 13 activity types — architecture, debugging, refactoring, code review, and more — and shows you exactly where your tokens are going. The standout feature is the optimizer: CodeBurn identifies wasteful patterns in your workflow — like repeatedly re-reading the same files, bloated context files, or MCP servers that are loaded but never used — and suggests concrete changes with estimated savings. It also tracks one-shot success rates per task type, helping you understand where AI is genuinely saving time vs. where you're fighting the tool. A macOS menu bar widget shows live token spend as you work, with a daily budget alert. Built by indie developer AgentSeal and shared as a Show HN, it picked up 80 upvotes and significant interest from developers who didn't realize how much they were spending on context re-reads alone. Open source under MIT license.
Developer Tools
MarkItDown
Convert any file to Markdown — PDFs, Office docs, audio, images
75%
Panel ship
—
Community
Paid
Entry
MarkItDown is Microsoft's open-source Python utility that converts virtually any file format into clean, LLM-friendly Markdown. It handles PDFs, Word documents, PowerPoint presentations, Excel spreadsheets, HTML, CSV, JSON, XML, ZIP archives, images (with optional vision model descriptions), audio files (with transcription), YouTube URLs, and EPub files in one consistent interface. The key design philosophy is LLM-first: rather than trying to reproduce original formatting for human readers, MarkItDown preserves document structure—headings, lists, tables, links—in a format that language models naturally parse efficiently. It integrates with OpenAI-compatible vision clients for image descriptions and supports speech transcription for audio content. With 108k+ GitHub stars and still gaining nearly 2,000 per day, MarkItDown has become the default document ingestion layer for countless AI pipelines. As agents increasingly need to process real-world enterprise documents, this kind of robust conversion utility becomes critical infrastructure—turning messy business files into clean inputs that Claude or GPT-4o can reason about without token-wasting formatting artifacts.
Reviewer scorecard
“I ran this on a week of Claude Code sessions and immediately found I was spending 30% of my tokens re-reading the same five config files. The menu bar widget is the killer feature — seeing the cost counter tick up while you work changes your behavior instantly. Instant install for anyone serious about AI coding.”
“MarkItDown solves the boring-but-critical problem of getting messy enterprise docs into LLM-friendly formats. The breadth of format support—PDF, PowerPoint, Excel, YouTube URLs, audio—means one library covers your whole intake pipeline. 108k stars is the market's verdict.”
“The 13 activity categories feel arbitrary and require calibration. More importantly, this is fundamentally a symptom-treating tool — the real fix is better context management built into the AI tools themselves. And if you're on a flat-rate API plan, cost tracking is largely irrelevant.”
“Output quality varies wildly by format. Complex PDFs with multi-column layouts, tables, and embedded images still produce garbled Markdown. It's great for clean docs but 'any file' is aspirational—you'll spend time post-processing anything messy. Microsoft started this, then moved on; community maintenance is mixed.”
“Observability for AI token usage is an entire category about to explode. As agentic workflows scale from individual developers to teams and enterprises, understanding where tokens go becomes as important as understanding where CPU cycles go. CodeBurn is early but directionally correct.”
“Every enterprise AI pipeline needs a document ingestion layer. MarkItDown becoming a standard here signals we've moved past 'can LLMs reason?' to 'can LLMs process the full enterprise data stack?' That's a meaningful maturation point for production AI.”
“Even for non-coding creative work — writing, research, brainstorming — understanding which prompting patterns are wasteful vs. effective is valuable. The one-shot success rate tracking by task type is a genuinely novel idea I haven't seen anywhere else.”
“Drop in a PDF, a PowerPoint deck, even a YouTube URL and get clean Markdown back for your AI workflows. No more copy-pasting reference materials into prompts. This single utility has quietly made AI-assisted research dramatically less painful.”
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