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A TypeScript library that splits LLM-generated markdown into WhatsApp-friendly chat message chunks.

16 starsTypeScript

Markdown to WhatsApp Converter

by daviddom·Feb 13, 2026·1 point·1 comment
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AI Analysis

●●SolidNiche GemSolve My Problem
The Take

Splits LLM Markdown into chat-sized WhatsApp messages while preserving lists, links, emails, tables and even Spanish punctuation. It applies a priority chain of processors — structural splits first, semantic fallbacks — and ships with zero dependencies plus 100% test coverage, which makes it a pragmatic, focused tool for messaging pipelines.

Target Audience

Developers building chatbots or integrations who need to send AI-generated content to WhatsApp (backend/frontend engineers, SaaS/chatbot builders).

Post Description

Hello, HN! If you've been trying to integrate LLMs into WhatsApp, you've probably noticed that the massive blocks of text that the AI often responds to, besides being in Markdown instead of the format WhatsApp supports, don't provide a good user experience.

I just published an open-source repository that solves this problem:

- Converts Markdown to WhatsApp's format - Intelligently splits long texts into shorter fragments (without cutting lists, links, emails, and with syntax understanding) - Supports tables, so they look good - Runs 100% locally in your project, doesn't use external libraries, and has 100% test coverage

You can start using it with a simple 'npm install'.

If you find it useful, I'd appreciate it if you gave the repo a star :

https://github.com/daviddominguezh/llm-markdown-whatsapp

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Developer Tools●Mid

Klovr – Convert any webpage to Markdown (Cloudflare covers only 5%)

Nice, focused product: site-specific extraction rules (CSS selectors/metadata overrides), edge-first delivery (<500ms p99) and SDKs for Node/Python make it quick to drop into an LLM pipeline and claim 40–60% token savings. That said, HTML→Markdown is a crowded niche (Pandoc, Jina, Firecrawl and dozens of scrapers already exist), so Klovr needs clearer differentiation — e.g. demonstrable extraction accuracy, enterprise-grade rule sharing, or unique model-aware trimming — to move beyond 'handy utility'.

Solve My ProblemSlick
vaibhavlodha98
214mo ago