MarkFlow: AI Format Engine
AI Markdown to IMG/PDF/DOCX, Supports iOS Shortcuts
What it does
Convert Markdown from AI outputs to DOCX, PDF, PPTX, images, XLSX, TXT with one tap. Offline processing, customizable image styles. Now you can even do it without opening the app by using iOS Shortcuts!
Does a similar job
all alternatives →- SASosumi.ai – Convert Apple Developer docs to AI-readable Markdown2025 · sosumi.ai · ▲133
I got tired of Claude hallucinating Swift APIs. It does a good job at Python and TypeScript, but ask it about SwiftUI and it's basically guessing. The problem? Apple's docs are JavaScript-rendered, so when you paste URLs into AI tools, they just see a blank page. Copy-pasting works but... c'mon. So I built something that converts Apple Developer docs to clean markdown. Just swap developer.apple.com with sosumi.ai in any Apple docs URL and you get AI-readable content. For example: - Before: https://developer.apple.com/documentation/swift/double - After:…
- DADoctly AI – Accurate AI-Powered PDF to Markdown Parser2024 · ▲7
I’m one of the co-founders of Doctly AI. I wanted to share our story. We didn’t originally set out to build a PDF-to-Markdown parser. It all started when we were building a RAG solution for a company that deals with regulatory agencies. All of their data was in PDFs, and as it is apparently with lawyers, they like to print and scan documents to make it hard on their counterparts. These documents contained complex tables that barely make sense, are rotated, and handwriting is mixed in between. Many pages are number ruled and potentially rotated. We spent a lot of time trying to get clean data…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 28d ago · cactuscompute.com
Source: Product Hunt launch ↗
Launched alongside, April 2025
the whole month →- IB
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
Dev tools · 2025 · runpyxl.com
- UC
Life & fun · 2025 · filiph.github.io
- IB
https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
AI · 2025 · github.com
