The answer is context. But context is expensive. I created a framework for the individual or small teams. Taking this framework to scale is a very interesting and valuable problem set.
Would be curious to know what you've seen out in the wild.
context-loops.vercel.app/
A lot of people using AI right now aren't becoming more productive.
They're just spending tokens.
They open a new chat, re-explain their project, get some output, and start fresh the next time. It feels like progress. It isn't.
I know because I did this for months.
Most people miss two fundamental constraints of LLMs: they don’t remember anything between sessions, and even within a session their memory is limited by the context window.
Every new chat starts from zero. No memory of your decisions. No awareness of what you already tried. No understanding of what your goals are.
The result is chaos.
The AI contradicts itself.
It re-suggests ideas you rejected last week.
It feels like onboarding a new hire every morning who never read the docs.
After eight months building a production app with AI and getting this wrong more times than I'd like to admit, I stopped blaming the tools and started designing a solution.
The result is Context Loops, an open-source framework that structures your project context so AI agents can actually work across sessions.
Tickets. Session logs. Sprint records. Decision logs. All living in your repo, all feeding into every future session.
Your work carries over instead of disappearing.
The shift it creates is simple: you stop being a passive user of AI and start orchestrating it.
If you're building with ChatGPT, Cursor, Claude, or Copilot and your sessions feel like they reset every day, this is the fix I wish I had eight months ago.
The framework is free and open source. Full docs in the comments if you want to try it.
Every AI influencer on this app is posting productivity porn about 10x-ing their output.
Meanwhile an actual study by Anthropic measured what happens.
Researchers at Anthropic had professional developers use AI to learn a new Python library.
They scored 17% lower on follow-up knowledge tests.
And they weren't meaningfully faster.
Less learning, no productivity gain.
The “AI makes everyone a superhero” thesis is being sold by people who are over-allocated without any understanding about how the technology works.
If you want to know how to build with AI agents, you need to see this.
This guy literally built with Claude Code for 6 months in production and turned every failure mode into an interactive simulation.
Token burn. Hallucinated data. Cost spirals. Context collapse.
This is what every AI builder needs right now.
Bookmark it.
The President of a $300B company that powers most of the internet's infrastructure just said there are 3 things that could stall AI entirely.
Act accordingly.
Everyone's celebrating how fast AI lets you ship.
Nobody's talking about what happens when you can't explain what you shipped.
I spent 6 months building a production AI system. The hardest part wasn't building it. It was understanding what I had built.
open.substack.com/pub/earlys…
Everyone's celebrating how fast AI lets you ship.
Nobody's talking about what happens when you can't explain what you shipped.
I spent 6 months building a production system using AI Agents.
The hardest part wasn't shipping code. It was understanding what I've built.
I wrote everything I learned so you don't have to learn it the hard way 🔽
If you want to learn about AI
You're better off spending a few weeks on @3blue1brown videos from 8 years ago than listening to some rando talk about how they are "shipping" with OpenClaw.
Backpropagation, the core algorithm of all modern AI systems.
