Chenruishuo

Chenruishuo / posterly

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Build academic conference posters as a single HTML/CSS file, rendered to print-ready PDF via headless Chromium. A Claude Code skill.

16
0
89% credibility
Found May 31, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

posterly is a free, open-source tool that helps researchers create academic conference posters using familiar web technology instead of complex LaTeX templates. Instead of spending hours fighting with formatting systems, you build your poster as a simple HTML file with modern layout tools, then the tool converts it to a print-ready PDF at exact conference dimensions. The tool includes automatic quality checks that catch alignment issues, image problems, and awkward text wrapping before you print. It works alongside AI coding assistants, so you can describe your research and have the AI help fill in the content. Everything runs locally on your computer with no cloud services or sign-ups required.

How It Works

1
📚 You discover posterly

You hear about a tool that makes conference posters easier to create using web technology instead of complex formatting systems.

2
🤖 Your AI assistant sets everything up

You share a simple link with your AI coding assistant, and it automatically installs the poster-making tool and all necessary components on your computer.

3
🎨 You pick a poster layout

You choose from ready-made templates designed for your specific conference, whether it's a four-column landscape for ICML or a two-column portrait for CVPR.

4
✏️ You create your poster content

You fill in your research title, add your figures and equations, and arrange everything in neat columns - editing is as simple as refreshing a webpage.

5
🔍 The tool checks your work

Automated checks scan your poster to catch common problems like misaligned columns, oversized images, or text that might wrap awkwardly.

6
📄 Your poster becomes a PDF

With one click, your HTML poster transforms into a perfectly-sized PDF ready for printing at your conference dimensions.

🎉 You have a print-ready conference poster

Your research is now displayed in a beautiful, professional poster that matches exact conference specifications - no more wrestling with formatting.

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AI-Generated Review

What is posterly?

Posterly is a Python tool for building academic conference posters using HTML and CSS instead of LaTeX. You write a single HTML file with your content, run it through a headless Chromium pipeline, and get a print-ready PDF at exact conference dimensions. It ships with four CLI sanity-check tools that validate alignment, catch LaTeX residue, verify figure sizing, and confirm the final PDF matches your canvas. The whole thing is designed as a skill for AI coding agents like Claude Code.

Why is it gaining traction?

The pitch is simple: edit CSS, refresh, done. No recompiling LaTeX, no scanning error logs, no reopening PDFs. Posterly leans on modern CSS layout primitives like flexbox and grid that LaTeX poster classes either lack or require layers of packages to approximate. The four validation gates give you confidence that columns align and figures don't overflow before you ever open the PDF. Since it renders via Chromium, you get exact print dimensions through CSS `@page` rules rather than guessing about A0 scaling.

Who should use this?

Academic researchers presenting at ML conferences like ICML, NeurIPS, or ICLR who want poster-quality output without wrestling LaTeX. If you already live in HTML/CSS and find LaTeX's compile-debug cycle frustrating, this is built for you. Researchers with heavy math notation benefit from MathJax integration, though going fully offline requires downloading MathJax locally. Teams using AI coding agents can invoke `/posterly` to walk through venue lookup, template selection, content filling, and rendering end-to-end.

Verdict

Posterly solves a real pain point for ML researchers, but at 16 stars and version 0.1.0, the credibility score of 0.9% reflects a very early-stage project with a small user base. The documentation is solid and the validation tooling is thoughtful, but clone-only distribution and no PyPI package mean manual setup. Worth trying if you want faster poster iteration, but expect to dig in and contribute fixes if you hit edge cases.

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