I always assumed that was because it's the least common flow. Once you've signed up, you're logged in, so the only time you should ever need the "login" button is on a new computer, or after logging out to switch accounts, or if the service offers a "remember me" checkbox (don't do that) and doesn't check it by default (definitely don't do that), or if the user genuinely wants to not be remembered (e.g. they used private browsing).

If you're having to use "login" on any regular basis, something in the overall flow needs improving.

So often it'll cut off just as it's about to finish.

That said, yes, it is pretty common for me to wait 10, 20 and sometimes even 30 minutes without steering the model or looking at what its doing. I usually write a pretty detailed prompt at the start that describes the issue, the usecases, the testing to do and the definition of success; then I sometimes ask it to draft a plan and give it a read, but after that I just press enter, let it run and come back when it's done.

In the end I do a manual review, both of the code and the functionality, but most of the time I get exactly what I wanted.

If my AI prompt takes more than 5 minutes to execute, I'm almost certain to stop it and break up the task or be more specific about what files it should look at or what subtask it should tackle first. The people who let it run for hours are absolutely not getting anything worth keeping in the end.

But if I were to look in on them they’d be giving feedback during that time, but they wouldn’t need steering.

There is a small link below the tiles for these plans saying something "looking to use it for code? Use Kimi code" and upon clicking you'll get a basically identically looking subscription page with different sub prices and more quota

Non coding plans:

   * Plus (19 "base agent" -- wahtever that might be)
   * Pro (39 x2 )
   * Max (99 x6)
   * Ultra (199 x12)

Coding plans:

    * Starter (19 - "base coding")
    * Explorer (49 - x4)
    * Expert (99 x10)
    * Master (219 x25)

I stumbled on that when i tried Kimi K2.5 back when that was released.

It's also worth mentioning that the $20 get you a 0.25 priority for API usage - because the others get x4 priority. So get ready for a _slow_ experience

mLSTM layers are completely parallel (the update rule for the cell state is C_t = f_t C_{t-1} + i_t x_t where f_t and i_t are gates that can be computed from x_t alone, x_t is the input at time t and this means that you can compute F_s = \prod_{s<t} f_s as fast as a cumsum and an exponential and then compute C_t = \sum_{s<t} F_s i_t x_t, again as a cumsum, so it's as good as if though it were parallel). I think the sLSTM layers that are the other component of the xLSTM have something else like this and presumably there's some trick also to training the Kimi "delta attention" RNN.

I'm not sure whether this is hardware or optimization dependent to some degree, but I get the impression that good custom kernels are an important part of this kind of thing.

I don't think it would even be that expensive. I think with EU chips we could do it for 20 billion EUR + 5 billion EUR per year. After all, an H200 doesn't cost 40k to manufacture, probably one-tenth, and if we are making the chips we can get the money circulating just as the Americans have.

Add in some state effort to create suitable training data-- maybe encourage academics to create some things that are AI-friendly, and let all the people trying have that. There would hardly be anyone in the game other than us if we went for this package.

We already have VSORA. Maybe Euclyd is ready soon too. If enough are ordered we have 85% of the inference need gone, so that whatever accelerators are freed can be repurposed for supercomputing and training? Maybe we could have some organized effort to make many of the VSORA and Euclyd chips so that the chips we need for training are freed up while we wait for our own training chips.

Edit: So in the end I think the thing we need to do is to order enough VSORA Jotunn8 cards and enough Euclyd cards that we flood the inference market so much that the price of H200eds etc. drops and we can buy them second hand and assemble huge supercomputers out of them. This may require some diplomatic effort from the commission, something like 20B EUR almost immediately and some coordination, but it's easy, it's doable and it requires only being a little bit decisive. Since these cards are faster less memory will be tied up in serving the models, so it'd have a positive effect on memory prices too, which in turn would have a positive effect on European supercomputer centers, who are currently of course constrained by high memory prices.

I doubt my workflows match the profile of "daily driver" as you're conceiving of it. I'm mostly in the IDE chat box asking for a hand here and there, but no "long horizon" / big vision / architecture things. Occasionally, I'll solicit a general opinion, quite abstractly, which isn't very token-intensive. My contexts are tiny and I aim to keep them that way.

The only token-intensive thing I do is occasional analyses of open-source codebases along the lines of "what is the actual behaviour when message X comes in and is malformed like this?", or something else similarly far-reaching. For that, I use DeepSeek's Reasonix, which they savvily dropped at the exact moment the sting of Claude API pricing became a sensation. It's a very cache-oriented harness, and delivers on its promise of being very economical. I doubt I've spent more than $10 with DeepSeek over the months I've used it. When I want a harness to inhale a codebase and give me an answer, Reasonix has been the go-to for a while now, and it's great. DeepSeek-V4-Flash is enough for many simple things, though I just run it on DeepSeek-V4-Pro because it's so cheap that I don't care.