zlacker

>>meetpa+(OP)
It is baffling how these AI companies, with billions of dollars, cannot build native applications, even with the help of AI. From a UI perspective, these are mostly just chat apps, which are not particularly difficult to code from scratch. Before the usual excuses come about how it is impossible to build a custom UI, consider software that is orders of magnitude more complex, such as raddbg, 10x, Superluminal, Blender, Godot, Unity, and UE5, or any video game with a UI. On top of that, programs like Claude Cowork or Codex should, by design, integrate as deeply with the OS as possible. This requires calling native APIs (e.g., Win32), which is not feasible from Electron.

>>Olympi+EJ
It’s just irrelevant for most users. These companies are getting more adoption than they can handle, no matter how clunky their desktop apps are. They’re optimizing for experimentation. Not performance.

>>herval+qE1
More adoption? I don't think so... It feels to me that these models && tools are getting more verbose/consuming more tokens to compensate for a decrease in usage. I know my usage of these tools has fallen off a cliff as it become glaringly obvious they're useful in very limited scopes.

I think most people start off overusing these tools, then they find the few small things that genuinely improve their workflows which tend to be isolated and small tasks.

Moltbot et al, to me, seems like a psyop by these companies to get token consumption back to levels that justify the investments they need. The clock is ticking, they need more money.

I'd put my money on token prices doubling to tripling over the next 12-24 months.

>>IhateA+iH1
> I'd put my money on token prices doubling to tripling over the next 12-24 months.

Chinese open weights models make this completely infeasible.

>>deaux+KP1
What do weights have to do with how much it costs to run inference? Inference is heavily subsidized, the economics of it don't make any sense.

Anthropic and OpenAI could open source their models and it wouldn't make it any cheaper to run those models.. You still need $500k in GPUs and a boatload of electricity to serve like 3 concurrent sessions at a decent tok/ps.

There are no open source models, Chinese or otherwise that are going to be able to be run profitably and give you productivity gains comparable to a foundation model. No matter what, running LLMs is expensive and the capex required per tok/ps is only increasing, and the models are only getting more compute intensive.

The hardware market literally has to crash for this to make any sense from a profitability standpoint, and I don't see that happening, therefor prices have to go up. You can't just lose billions year after year forever. None of this makes sense to me. This is simple math but everyone is literally delusional atm.

>>IhateA+xT1
Open weights means that the current prices for inference of Chinese models are indicative of their cost to run because.

https://openrouter.ai/moonshotai/kimi-k2.5

It's a fantasy to believe that every single one of these 8 providers is serving at incredibly subsidized dumping prices 50% below cost and once that runs out suddenly you'll pay double for 1M of tokens for this model. It's incredibly competitive with Sonnet 4.5 for coding at 20% of the token price.

I encourage you to become more familiar with the market and stop overextrapolating purely based on rumored OpenAI numbers.

>>deaux+UT1
I'm not making any guesses, I happen to know for a fact what it costs. Please go try to sell inference and compete on price. You actually have no clue what you're talking about. I knew when I sent that response I was going to get "but Kimi!"

>>IhateA+TW1
Okay, so you are claiming "every single one of those 8 providers, along with all others who don't serve openrouter but are at similar price points, are subsidizing by more than 50%".

That's an incredibly bold claim that would need quite a bit of evidence, and just waving "$500k in gpus" isn't it. Especially when individuals are reporting more than enough tps at native int4 with <$80k setups, without any of the scaling benefits that commercial inference providers have.

>>deaux+t12
Imagine thinking that $80k setups to run Kimi and serve a single user session is evidence that inference providers are running at cost, or even close to it. Or that this fact is some sort of proof that token pricing will come down. All you one-shotted llm dependents said the same thing about Deepseek.

I know you need to cope because your competency is 1:1 correlated to the quality and quantity of tokens you can afford, so have fun with your Think for me SaaS while you can afford it. You have no clue the amount of engineering that goes into provide inference at scale. I wasn't even including the cost of labor.