However it definitly isn't _just_ Kimi. The weight will be different after that 85% of extra training on top of the base model.
If those different weights are better are worse doesn't change that it's in most meaningful ways not the same as the base one.
I would encourage you to lookup their blog posts about their post training process if you want a bit more faith that they aren't running an extra 85% of compute and burning money with no-ops.
I can understand words, but having more diverse medias for communication lets a person express strictly more than before.
Sometimes words are better, sometimes a visual demo is better.
Is your solution to the problem you presented that you should artificially restrict what a person can express just to keep your own personal moat?
I prefer the alternative, let a person express themselves and grow thanks to AI, while keeping the necessary culture and boundaries to where it's also accepted for _me_ to cross boundaries and express my ideas to them in the same way. Or suggest other ways to express those ideas.
We then become a marketplace of ideas in a much deeper sense than before, where product managers would already expect you to implement what they want, but without them being able to convey it properly.
If I didn't have original ideas and didn't think I could compete in that marketplace of ideas, I would be scared like you convey in your message. But I'm confident that my value is not about translating things into code, it's because I have original thoughts I can convey to other people (and to AIs). (and about understanding architecture and systems to a degree that keeps me valuable even if all the code itself is written by AI without my direct involvement)
I honestly much prefer this to the old way where the only mode of communication was speech or text. I now often understand a lot more holistically what the person coming with their product wants with just a demo + a conversation.
Of course you need the person making that vibe product to understand it's just a mockup of their idea and that it'll change. But I would argue this has always been a necessary quality for a product person.
I'm guessing the parent is wondering why this is noteworthy enough to be posted and discussed in this thread, and so if there's context they are missing
Going on an old legacy website, downloading reports, summarizing them, and then doing things based on those
Or basically any app without MCP capabilities
I ask the AI daily to summarize information across surfaces, and it's painful when I have to go screenshot things myself in a bunch of places because those apps were not made to extract information out of them, and are complete black boxes with a UI on top
I enabled the computer use plugin yesterday. Today I asked it to summarize a slack thread, along with a spreadsheet without thinking about it
I was expecting it to use MCPs I have for them, but they happened to not be authenticated for some reason
I got _really_ freaked out when a glowing cursor popped up while I was doing something else and started looking at slack and then navigating on chrome to the sheet to get the data it needs
Like on one hand it's really cool that it just "did the thing" but I was also freaked out during the experience
One could argue a smaller number of employees that are more motivated and feel connected to their coworkersis better than a more employees that are all isolated and "meh".
> I get it that in 10 years all of this might peak and we're gonna be content using old models
I would personally be happy using gpt 5.3 codex for the foreseeable future, with just improvements in harnesses
IMO we're already at the point where even if these company collapse and the models end up being sold at the cost of inference (no new training), we would be massively ahead
It's hard to explain, but I've found LLMs to be significantly better in the "review" stage than the implementation stage.
So the LLM will do something and not catch at all that it did it badly. But the same LLM asked to review against the same starting requirement will catch the problem almost always
The missing thing in these tools is that automatic feedback loop between the two LLMs: one in review mode, one in implementation mode.
My reaction in that case is that most other readers of the codebase would probably also assume this, and so it should be either made clearer that it's stateful, or it should be refactored to not be stateful
However it definitly isn't _just_ Kimi. The weight will be different after that 85% of extra training on top of the base model.
If those different weights are better are worse doesn't change that it's in most meaningful ways not the same as the base one.
I would encourage you to lookup their blog posts about their post training process if you want a bit more faith that they aren't running an extra 85% of compute and burning money with no-ops.