Monday night, 10 p.m.: waiting for a webpage to finish generating

Last Monday at 10 p.m., I was still fixing a client’s landing page. The code was halfway through generation, the page was just sitting there, and the progress bar moved like a snail. I stared at the screen and thought, “If this could generate several screens of code per second, I could get two extra hours of sleep tonight.” Later, I realized the problem wasn’t only the model’s speed. Waiting itself was wearing me down.

I used to get this wrong too: I assumed a long wait meant the model was bad, so I tried three tools and thought they were all the same. Then I learned that different models have genuinely different rhythms. Some “think longer but get it right in one pass”; others “answer immediately but send you back for repeated rework.”

h2>Who built this model, and who is using it

NaiveAI is a Chinese company that built an open-weight model called Naive-N0.5-Flash. Open-weight means anyone can download the weights and deploy it themselves, rather than being locked into one company’s platform. It was further trained from Xiaomi’s MiMo-V2.5 and paired with a dedicated acceleration engine. The goal is straightforward: maximize how fast the AI produces work. The official claim is up to 2,100 words per second.

The editors at Latent.space actually tested it. They compared it with DeepSeek V4.1 Flash and GLM 5.3 Flash, then gave all three models the same batch of “build an app from scratch” tasks. My favorite part was this: editor Aleksandr, sitting in San Francisco—or at least their editorial team—sent the same prompt to each model and recorded the time from pressing Enter to getting a running app. This wasn’t reading a spec sheet; they were timing it with a stopwatch.

What it costs to replicate today

Money: If I’m only trying the official API, it’s billed by token and costs a little more per chat than DeepSeek; the exact price depends on that day’s official pricing. If I don’t want to pay, I can run the open weights myself, but then I’m paying for electricity and hardware.

Time: An afternoon. In about three hours, I could register on the official site, get an API key, paste in a requirement, and see the speed firsthand—assuming I know what an “API key” is. In plain terms, it’s a password-like string the site gives you so a program can act on your behalf; even without that background, I could copy a tutorial.

Technical barrier: Moderately high. The acceleration engine comes built in, so I don’t need to tune it myself. Local deployment on my own machine, though, requires some command-line knowledge and environment-variable fiddling. Honestly, I messed this up too: my first local attempt kept me stuck on environment variables for two full days. I gave up and switched to the web version.

First step: Open the official site, create an account, get an API key, start a new chat, paste in the requirement I’m most tired of handling, press Enter, and start the timer.

Where I’d start, based on team size

  • Just starting (0–1 clients): I’d probably skip a speed-focused model for now. While I’m still finding the right needs, model speed doesn’t determine whether I can win work. If I wanted to test it, I’d use a low-stakes little page rather than experimenting with a real client’s budget.
  • Already have 1–2 clients: I’d try it. At this stage, waiting for generation eats into my day, and speed turns directly into an extra hour in the evening. I’d run two models together: the fast one for the structure, the slower one for the tricky parts.
  • Scaling up (3+ people): I’d get two answers before deciding: how stable it is on long, high-volume tasks, and what the total bill looks like. This model is still new and slightly more expensive, so I’d calculate the cost carefully as the team grows.

My last thought: this isn’t a tool everyone needs. If I don’t write code at all, or I’m only writing a few dozen lines at a time, I probably won’t feel much difference. There’s no urgency to test it now, either. I might revisit it after another few months of iteration, once pricing and stability improve.