The AI Productivity Engine: Tracking Habits with Data and LLM Summaries

Описание: Can AI bridge the gap between effort and actual results? In Episode 3 of Exit Code 1, I’m diving into the development of my AI-driven productivity tool. I discuss why standard to-do lists often fail to provide real insights and how I’m using LLMs to analyze the "metadata" of daily tasks to track long-term progress.

In this episode:

The "Recurring todo" Concept: Why the focus shifted from one-off tasks to a robust system for tracking recurring habits and consistency.
AI-Generated Weekly Summaries: A look at how the app aggregates task data to provide an objective retrospective of your week, including achievements and setbacks.
The 75 Hard Framework: How this platform serves as the perfect engine for high-intensity challenges like the 75 Hard.

About Exit Code 1:
A developer's journey through building AI-driven projects, from UFC statistical models to productivity tools and mobile apps. I’m documenting the transition from manual coding to leveraging LLMs to ship real-world products.

Support the Project:
If you are interested in the intersection of AI, productivity, and software development, hit Subscribe to follow the build. I am documenting the process of taking this application from a terminal prototype to a live product.

If you’re interested in joining the internal testing track for the Android or iOS versions, let me know in the comments!

#Productivity #AI #HabitTracker #75Hard #ExitCode1 #BuildInPublic #SoftwareDevelopment #saas

00:00 - Introduction: The To-Do App on Steroids

00:21 - Yet another Todo app

01:56 - AI Driven Productivity App

04:27 - Overview of the current version: Tracking my routines

07:34 - Evaluating performance with weekly summaries

09:54 - Roadmap: New features and ideas

10:27 - 75 Hard Challenges tracking

11:51 - The PIVO Method & Naming the App