markdown vs @supermemory in markdown only memory systems, the agent may have to look through and traverse files to answer things. This is wasteful and slow! The files also get stale really fast, and there's no forgetfulness, updates, etc. The agent has to just look through things. supermemory is kinda like a dense interconnected markdown system, except that we automatically construct almost an 'instant' file that will directly one shot what the agent needs. That context has the whole history of the thing, removes forgotten info, and even related things that the model should probably know. This information is dense, fast, and dynamically constructed, so you have a living breathing improving system. So yes, you should use supermemory instead of markdown

TanStack Markdown 0.0.14 is out! ⚡ Faster parsing (-72% prose, -38% streaming) ✅ +45 CommonMark cases passing 📦 Much better compatibility And all without extra bundle size... actually a little smaller! Great for blogs, docs, and streaming AI

The fastest PDF-to-Markdown parser just got faster. ⚡️ With LiteParse v2.14.6, text-based PDFs parse about 25% faster. Across realistic documents, LiteParse processed pages at 2.8ms/page and 1.5× faster than the next-fastest local parser. LiteParse is open source and runs locally in Python, Node.js, Rust, or directly in the browser. Grab v2.14.6 → Bench Docs →

I have a simple markdown file and a /tastemaker skill that I use to add ratings for movies, TV shows, and books I've seen. Then every week it looks up new releases and stuff I haven't seen and makes incredibly relevant suggestions. Should I open source this?

A free pdf-to-markdown tool brought us 339k visitors last year Strategy is simple: double down on what works! So two new tools ✌️