AI Productivity

## Context

I have a recurring [FREQUENCY] task: [BRIEF DESCRIPTION].

Currently I do it manually by [CURRENT PROCESS - 1-2 sentences].

Here’s an example of this task in action:

<example>

Input I provided: [PASTE ACTUAL INPUT]

Output I needed: [PASTE ACTUAL OUTPUT OR DESCRIBE]

</example>

## What I Need

Turn this into a reusable system with:

1.... See more

Leverage . LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely... See more

The explosion of content raises the bar for everyone to be more conscious in your media consumption

Optimizes for upgrading, not just watching: recommendations aren’t “what will you click fastest?” but “what’s the next rung you can handle?

You give the model a workspace and rules:

1. Inspect the corpus


2. Decompose the problem


3. Solve sub-parts independently


4. Verify logic and assumptions


5. Commit only when confidence is high

This feels less like chatting and more like working with a junior analyst who shows their work.