Ship papers withless frictionand a calmer flow.

Draft and revise LaTeX through a clean chat loop, attach context from PDFs and links, and keep the source editable at every step.

Get startedTry the demo
No setup. Just write.
Preview
paper • sections • math

Untitled

Preview
Abstract

We sketch a compact intuition for contrastive representation learning and provide a clean scaffold for quick iteration.

Equation
L=logexp(sim(zi,zj)/τ)kexp(sim(zi,zk)/τ)\mathcal{L}=-\log\frac{\exp(\mathrm{sim}(z_i,z_j)/\tau)}{\sum_k\exp(\mathrm{sim}(z_i,z_k)/\tau)}
Idea

Pull positive views together and push negatives apart.

Objective

\[ =-(z_i,z_j)/)}{_k((z_i,z_k)/)} \]

latex-ai • demo
Interactive mini workspace
Local simulation — no external calls. Designed to feel like the real product.
Prompt
From outline → clean LaTeX
mode: Draft
Generate to preview the flow
Output
Chat-style summary + copy-ready LaTeX
Summary
Generate to render the preview and LaTeX.
Draft a 1-page research note on contrastive learning with a short abstract, 2 sections, and one equation.
Preview
29 lines
Generate to render a document preview.
**Local demo** (no AI calls) Drafted a clean scaffold from your intent. **What this simulates** - Chat-driven drafting and editing - Reference scaffolding (citations / structure) - Real-time LaTeX output you can copy into the editor **Your prompt** > Draft a 1-page research note on contrastive learning with a short abstract, 2 sections, and one equation. ```latex \documentclass[11pt]{article} \usepackage{amsmath,amssymb} \title{A Minimal Research Note on Contrastive Learning} \author{Anonymous} \date{} \begin{document} \maketitle \begin{abstract} Contrastive learning trains representations by pulling related views together while pushing unrelated samples apart. This note sketches a compact intuition and a clean LaTeX scaffold for quick iteration. \end{abstract} \section{Idea} Given two augmented views of the same sample, we maximize agreement in embedding space. \section{Objective} Let $z_i$ and $z_j$ be embeddings of a positive pair and $\tau$ a temperature. The InfoNCE loss is: \[ \mathcal{L} = -\log\frac{\exp(\mathrm{sim}(z_i,z_j)/\tau)}{\sum\limits_{k}\exp(\mathrm{sim}(z_i,z_k)/\tau)} \] \end{document} % Prompt (local demo): % Draft a 1-page research note on contrastive learning with a short abstract, 2 sections, and one equation. ```

Features

A LaTeX workflow that stays readable

Draft, edit, and keep structure stable — with context from files and links when you need it.

Chat-native drafting
Turn a prompt into a compilable scaffold (title, abstract, sections) — fast and clean.
Example
“Write the abstract + 2 sections.”
→ structured LaTeX scaffold
→ iterate with small edits
Editor-first iteration
Keep the source visible. Make a local edit. Preserve the rest of the document.
Rapid revision loops
Ask for a change in one place (tone, math, layout) without rewriting everything.
PDF + file context
Drop a PDF and pull definitions or snippets into your draft without losing structure.
Web context ingestion
Bring a link, extract what matters, and write with fewer tabs open.
Ready-to-export output
Copy LaTeX, compile when you’re ready, and keep the output predictable.

Workflow

Small steps, stable structure

The goal isn’t magic — it’s fewer rewrites, cleaner diffs, and predictable LaTeX.

01
Draft
Start from intent. Get a clean scaffold you can compile and extend.
02
Ground
Attach PDFs or links only when needed — keep the core draft simple.
03
Refine
Make local changes that preserve structure: one paragraph, one section, one equation.

Open the workspace

Start a session, try the editor, and iterate on structure without the noise.