I ve been working with Google Docs content recently and kept running into issues when exporting documents especially when I needed clean text or structured output without extra formatting.
Google s default export options work, but for some use cases they felt a bit clunky, especially when used programmatically.
I ended up building a small converter that pulls Google Docs content and outputs it in simpler formats for reuse. I m sharing it here mainly to get feedback and to understand how others are solving this.
Fitness apps are everywhere, yet most of them struggle with the same problem: people start strong, then quietly stop showing up. The issue is rarely lack of workouts or diet plans. It s motivation, personalization, and the feeling that the app actually understands the user s journey.
RBT (Radical Body Transformations) is a digital fitness membership app we helped build in collaboration with Anthony Lolli, whose transformation story later became widely known through media platforms like Amazon Prime. I wanted to break down how this app was designed, what problems mattered most, and which decisions actually helped turn it into a sustainable fitness platform instead of just another content library. If you re building in fitness, subscriptions, habit formation, or community-driven apps, this breakdown should feel familiar.
I m a developer and long-time user of it-tools. Over time, I started experimenting with some changes and improvements that better fit my own workflow, and that gradually grew into a separate project called next-tools.
I m Micha , a solo founder from Poland and a long-time strength & calisthenics enthusiast. I ve been training consistently for almost six years, and during that time I tested a lot of fitness apps from simple workout logs to advanced coaching platforms.
What I noticed is that many apps either feel too generic or try to gamify everything, while people who train seriously often just want clarity, structure, and real progress tracking.
That s why I decided to build my own app GainTrail.
The Problem Most OCR tools fail when documents aren't perfect. If you have crumpled receipts, handwritten notes, or tables with weird formatting, standard OCR often outputs garbage. Even when it reads the text, you still have to manually map that unstructured text into a database.
The Solution aOCR handles the messy inputs that other engines reject. Instead of just giving you a raw text dump, we allow you to define a specific schema or format. Our engine processes the document and transforms the data directly into the structure you requested (JSON, CSV, etc.).
How it works:
Input: Upload any document (PDF, IMG, messy scans).
Define: Tell us the format/schema you need.
Output: Get clean, structured data ready for your DB.
I ve been working on a side project called AxonixTools. It originally started as something I built for myself, but over time I realized it might be useful to others too.
The goal is pretty straightforward: a growing collection of tools for creators and developers, without unnecessary complexity.
I built this text summarizer because I was constantly skimming long articles, docs, and AI outputs just to pull out the main points. Most tools either over-summarize or strip out useful context, which wasn t helping much.
This one focuses on keeping the core idea intact while cutting the noise. It s meant for quick understanding, not perfect summaries. I mostly use it to get a fast sense of long text before deciding whether it s worth a deeper read.
It s simple by design and still evolving based on how it s actually used.
