I have a couple hundred line TUI that just clones an LXD container and I get all my repos and infra isolated in 5s with pi having isolation to that container. Swap between any instance. Port forward to host all automatic.

Funny to me how stuff is described these days. Seems like some people lack the full understanding of all these different systems and really overcomplicate their harness setups or use super bloated all in one tooling.

I tried putting my agent in an LXC / Incus container but then I also needed it to process videos stored on a NAS, and that was not easy at all. I tried a FUSE-based NFS client [1] but it was very flaky and would randomly report files having zero length and fail processing.

[1] https://github.com/facebookarchive/nfusr

Do you have any links that would explain this?

I'm looking at changing my AI harness and am definitely interested in this

love this idea!

I just have a slack bot running on a VM that sees a message and invokes pi.

It would be trivial for every request to clone a full lxd container and have all the tools and repos required if I wanted to allow it to do even more.

Not sure why anyone prefers to choose locked in options

How did you set all that up? We built a flow where you push a slack.yaml to github to arrange this

> Not sure why anyone prefers to choose locked in options

Convenience. And OPEX vs CAPEX something something.

I have been using glm 5.3 flash and it feels as good as opus 5. Put a lot of work into it this week (100m tokens). Now I'm curious to try this one. These smaller models are getting very good imo

Neither flash or regular glm 5.3 are close in my experience. I still prefer Sol though.

What type of setup do you use? I have very small 4-5k initial context and do one task then clear. I rarely go above 150k context for most things.

It DOES still hurt.

For example the eBay app. Does not allow installing from the play store on grapheneos.

No memory. No web search 99% of the time.

Two agent.MD files that are very small. One on each project. One at parent project level.

Did 30M tokens through glm 5.3 flash today for 52c

Using pi and a few extensions my initial context is always 4k max

I ran 30M tokens through for 50c... insanity that this is possible.

and it really is opus 4.8 level.

Any plans to offer a way to generate audio books on demand. like if i set kokoro, a book auto imports, it can make the file for me for the whole book after some time of local gpu work?

This is great. Recently got an xteink x3 and jailbroke my kindle, making custom stuff for it.

I couldn't find it exactly in the docs. Sorry if I missed it.

If you align these and then pull onto koreader via opds or other way, will audio work over Bluetooth there too? Along with the highlighting.

You can use a KOReader plugin, https://github.com/stradichenko/audiobook.koplugin, which has work-in-progress support for Media Overlays (the EPUB spec that Storyteller uses for readaloud)!

Wow I didn't even think about just using on device AI.

I'm going to set this one up and try it with kokoro which has the most natural for small size that I've seen. Wonder if the paperwhite 12th Gen can handle it.

And also setup the main repo for when I have the real audio book. Thanks!

I tried running Kokoro on my iPhone XS (several years old), and it was slower than real-time. So, i wouldn’t expect it to be usable on a Kindle. But if you find a solution, please let me know.

Kokoro can be hacked a bit (see script https://github.com/DavidVentura/translator-rs/blob/master/sc... ) which makes it about 3x faster.

My phone went from slightly slower than realtime to very comfortable (also lower latency on first utterance!)

Thanks. Will try!