
StreamMD
Streaming MD for LLMs. 300x fewer chars parsed per token
What it does
Every AI chat app re-parses the entire markdown on each token—O(n²) slowdown. After 500 tokens, that’s 500 full re-renders. StreamMD fixes this with incremental block parsing: only new text is processed, completed blocks are memoized (no re-render), and only the active block updates. Result: ~300x less work. Built-in syntax highlighting (15 langs, ~3kB, zero deps). Drop-in:
Does the same job
all alternatives →
Geekflare Scraping API v2Apr 2026 · geekflare.com · ▲84RAG-ready web scraping that cuts your LLM token costs


- IJIncremental JSON parser for streaming LLM tool calls in RubyOct 2025 · aha.io · ▲13
Built this for streaming AI tool calls. LLMs stream function arguments as JSON character-by-character. Most parsers reparse from scratch each time - O(n²) behavior that causes UI lag. This maintains parsing state, processing only new characters. True O(n) performance that stays imperceptible throughout the entire response. Ruby gem, MIT licensed. Would love feedback.
- RCReduce ChatGPT costs 10x with distributed cache for LLMs2024 · edgematic.dev · ▲22
Hello HN! We're building a caching solution for LLMs (ChatGPT, Claude). By combining cutting-edge approaches, such as edge computing, prompt compression, vectorization, and others - it can reduce your AI bills by up to 10x and significantly lower response times. Key Features: - cost efficiency: our system stores frequent queries, reducing the number of upstream (paid) API calls - fast responses: with various nodes globally, we reduce latency by serving data from the nearest location - scalability: designed to handle increasing loads and data sizes without degrading performance. The cache…

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