lxdlam

If you run the `dsh`, you can go to the Settings -> Plugins, and you can find that they just write all UI components as plugins(maybe not all, I don't check). Also, you may ask the harness to write a UI plugin for you, I just read some neat examples somewhere.

I have read the underlying paper, and found it may be useful, but not that useful.

For those who want to know what it achieves: it adds hot-reload and dynamic enable/dispose capabilities to a plugin system, like the one in Pi agents, though they push the boundaries further, to the UI components and so on.

For those who want to know what it does: if you have some PLT knowledge, ask your agent to explain the algebra to you better; for those who aren't familiar, the framework requires each plugin to provide how it initializes and how it destructs (like C++'s RAII, Rust's Drop trait and so on), and the runtime will then properly handle the lifecycle events and the common pitfalls. In addition, it provides a clean way to declare the dependencies between plugins, and the runtime will also properly process the lifecycle changes on a broader plane.

I think it's worth reading if you are not familiar with OSGi, iPOJO, React's useEffect and so on (which the paper itself mentions); for others, a skim is enough: it does point out the gotchas for some common problems, but the algebra may not help you further.

Disclaimer: I'm not working for them, just FYI.

Seems to be an raft.build competitor but open source.

BTW, from my very own opinion, IM is a great place for agents to chat, but not a good place for get work done. Something more comprehensive like Linear should be work better. Communication is a key stone but not all for the agents.

Durable execution was one of my most worth investing techniques 2024, and glad to see it's blooming in 2026. The idea behind it is pretty simple: persistent state machines, auto or semi-auto context capture, and a run engine, but it actually solves many common headaches like exactly once execution with retry, signal based workflow and so on.

I still suspect the effectiveness of plugging in a type system patch to a complete system, like typescript to javascript. We still observe so many `as any` or `as unknown as` at every corner.

Despite of the suspicion, Gleam provides a better and elegant syntax for those who are not familiar with Erlang or functional programming languages, which I loved most.

We self hosted Kratos only as our IdP: three million total users, about 200k login/logout/session/jwt queries a day, using only four 1C 2G k8s pods with one extra for courier, a standard proxied 4c8g Postgres, everything works fine. Really easy to maintain with simple configuration and fully featured API.

But their documentation is really bad, especially in OSS suites. I generally use Claude Code to read their code, find the matching implementation, and try to figure out how to properly configure.

Anyway, if you need self host your IdP, just go for it, you cannot go wrong.

Really happy to see some company that plays a key role that publish such a statement. Creativity comes from humanity, from our experience, our work, our connection.

AI may be not a theft, but it just sophisticated combinations from our wisdom. Until it can really create, the human will always win.

It really sucks. BTW, how did you find the statement? I cannot find it in any place.

I have a serious question about the term "AI TOPS". I find many conflicting definitions while others say nothing. A meaningful metric should at least be well defined on its own term, like in "TOPS" or expanded "Tera Operations Per Second", what operation it will measure?

Seemingly NVIDIA is just playing number games, like wow 3352 is a huge leap compared to 1321 right? But how does it really help us in LLMs, diffusion models and so on?

From my very own perspective, to compare OpenAI with any others is meaningless: AI is far different in terms of resources and business models, maybe similar to some others, but it may collapse or have the rug pulled out by another significant technology evolution, e.g., quantum computer which may ridiculously speed up the training, or a more self-update-friendly model architecture.

To the rest of us, training a usable model these days is relatively affordable, and it seems to make no difference to use a "most intelligent" model against a subtly small model. The current business blocker is to find the application fields that work for models, which is not an area where OpenAI has an advantage.

I must say this is tremendous. There are many different AIGC explorations in 3D topics, with such high quality dataset, it will greatly assist current workflow and accelerate the 3D creative evolution.