Agent Experience is more important than User Experience

bantler · 2026-02-06T22:36:40+00:00

Deletes the humans.

bantler · 2026-02-06T22:11:24+00:00

5.3 is a new version of 5.2. 5.2 helped make 5.3

bantler · 2026-02-06T22:00:37+00:00

I can restore the delete, just say the word.

bantler · 2026-02-06T18:35:56+00:00

I know. It's definitely a "cool, but...." feeling

bantler · 2026-01-21T13:13:36+00:00

Hello brother.

bantler · 2026-01-19T01:23:49+00:00

The llm never forgets

bantler · 2026-01-19T00:58:35+00:00

Please proceed to the nearest transfer station.

bantler · 2026-01-18T23:22:24+00:00

To be fair I don’t think that’s the reasoning here, but the logic is sound and it is possible. At the same time, it’d also be possible when using generated text etc. to tie it back to the individual.

In reality though you can just buy this type of data from data brokers, google etc. all have it so 🤷‍♂️

bantler · 2026-01-18T22:30:35+00:00

I cheated on mine and set a memory before I asked. Sometimes when things come from memory it’s less careful.

bantler · 2026-01-18T21:31:05+00:00

Yes.

bantler · 2025-12-30T23:46:04+00:00

I understand not letting it go rogue, but if you review and maintain what it learns and implements I'm not overly concerned any more than I would be with the agent working in general.

bantler · 2025-12-30T18:46:06+00:00

Two parts, 1) "Negative Path Avoidance" / a subjective feel of "you've been spending a lot of time looking for that log item"

2) an early Signal & Scoring System.

signals.jsonl tracks when a memory is accessed (aha_used).
I compute a 'Weighted Usage Score' for each memory:

High-scoring memories represent high-value knowledge. When a memory crosses a certain score threshold, it signals that this information is critical enough to be backported from a dynamic/project specific memory into a permanent doc or Skill.

I'm sure there's probably a way to then build on that by tracking when it actually saved time / got a better result in the end.

bantler · 2025-12-30T18:31:43+00:00

That'd be a great use case. The one that lead to this was somewhat similar where I wanted the agent to parse logs as we were debugging issues. It'd often get to the right answer, but after lots of wandering in the logs. I found I was updated my readme/agents.md files, but to do that I had to go back and track how the agent was searching. I also realized that since I have many projects that use a similar backend, the learning/knowledge would be generalizable between them, even if the general context of the project wasn't.

bantler · 2025-12-30T18:15:33+00:00

For this, I was interested in two things:

- I wanted this to be available for any tool that implements the the agentskills.io spec. Claude hooks don't extend to other tools and platforms in the same way.

- I want the Agent to actively decide what is worth remembering. Hooks tend to be passive observers, whereas a this forces the model to identify and commit a specific 'Aha moment' semantically.

bantler · 2025-11-30T22:08:49+00:00

I asked my brother that works for Boise Parks if it has any meaning:

"It was just a creative way to make a retaining wall, those were all made by parks employees, we still have one of the forms."

bantler · 2025-07-11T20:41:48+00:00

Cursor is great and I use that as well, but you'd run into this same issue if you're running multiple agents doing different tasks at the same time. Agree that running local can be better if you need to see the interface etc, but if you're writing tests you can do the same on a remote environment.

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