Read it
Conversations
One path, captured. A good exchange with an agent is a record of thinking that someone else can pick up later.
Agents can give us richer experiences than a chat window: conversations worth sharing, simulations we can walk through, and searches over the paths we didn't take. Each one is a way to learn faster, work faster, and create more value.
The question, the wrong turns, the moment it clicks. All of it sits in a log that is never read again.
Accurate, but flat. You read about the 2 a.m. incident. You don't feel it.
Experience shows you what happened. It rarely shows what would have happened if you chose differently.
None of these is new. This is my attempt to bucket the kinds of rich experiences we can build with agents.
One path, captured. A good exchange with an agent is a record of thinking that someone else can pick up later.
Walk the path yourself. The agent assists by playing the parts around you. You decide what to try.
See the paths you didn't take. Run the simulation many times, vary the choices, and the shape of the problem appears.
Someone already paid for it. Readers start where the conversation ended instead of rediscovering it.
The chain of thought is laid out in the transcript. Readers build on it instead of starting from scratch.
A summary, a checklist, another language. The reader's agent slices it, and the original stays there to check against.
Each post is authored before it goes out: reviewed, trimmed and scrubbed of anything private. Read them at golden-convo. The process is written down as skills, so anyone can share their own.
Much of what makes an engineer good is experience they can't easily hand over. Postmortems are accurate, but flat. So the incident becomes a playbook, and the playbook becomes something you can play.
Not a quiz. A small, safe version of having been there.
Trying many options and keeping what holds up already powers these fields. Agents bring the pattern to situations that were too messy to simulate before, like an incident. It is only as good as the simulation behind it.
See how a rule plays out before it reaches real people.
Explore settings and new architectures for neural networks, LLMs included. For example, karpathy/autoresearch.
Replay an incident many ways and see which calls hold up.
A place to share the content, and the knowledge in it.
Rich content in many formats. I build the first ones, then open it to early users.
Often a deterministic tool, high quality and built to be extended. How we play with it can be agent-assisted.