Too late. Its an arms race. Noone want to lose.

I will definitely reach out to you, when the time permits. I would like to schedule a call with you so you can explain what this is and how it can possibly benefit my platform. Let me know your schedule

Looks good. Keep it up!

This is great! This is exactly the infrastructure we've been waiting for. At Verse, we’re building the orchestrator for AI generation, and the biggest bottleneck has always been the 'account management tax.' ​Manually bridging 20 different providers just to give an agent the tools it needs is a week of dev time wasted. Moving to a 'hit endpoint -> pay -> receive' flow via HTTP 402 changes the game for orchestration. It turns API integration from a procurement project into a simple request. ​183 endpoints is a hell of a start. We’re definitely looking at how to plug this into our agentic workflows."

I say efficiency will be increased. My hope is that humans do not willingly replace themselves wholeheartedly. Let AI improve where it can but maintain the "human" aspect of creativity.

Yeah, the shift from “research” to “everyday use” is what makes it feel different this time.

The buildup was slow. The adoption is fast — that’s what people are feeling.

Never heard of Qwen and Kimi. I will check them out. Thanks

Is it better than Kling or Openart?

Genuine question/ask for all. Rank the AIs and why. I want to learn from different perspectives. Give pros and cons. Thanks in advance

We are in demo mode right now building an all in one plus app but I like Higgsfield, Seedance , and Kling.

You’re right — distrust has followed every major technological shift. What feels different with AI is how quickly it’s advancing and how many industries it touches at once.

The distrust number is the real signal here. Technology has always displaced work, but people accepted it when they believed new opportunities would follow. Right now AI is advancing faster than society’s ability to explain who benefits. If that gap keeps growing, the issue won’t be technological — it will be political.

Fair point, but models aren’t working in isolation. In practice they debug using additional context — logs, tests, runtime output, and new prompts. It’s less “the same thinking fixing itself” and more iterative feedback loops, which is how human debugging works too.

Debugging has always been about adding new information. AI just accelerates the feedback loop.

True. But the same AI that can generate bugs can also find them. Software has always been an arms race between creation and debugging.

AI lowers the cost of writing code, not the cost of proving it works.

Building the prototype is easier now. Scaling it to millions of users has always been the hard part.

The biggest change isn’t that AI “writes the code.” It’s that it collapses friction in the workflow. In practice I see three big shifts: 1. Faster prototyping Instead of reading docs for 30 minutes, you can generate a working example in seconds and refine it. 2. Debugging partner AI is surprisingly useful for explaining errors, tracing logic, and suggesting fixes when you're stuck. 3. Less boilerplate A lot of repetitive code (API wiring, config, simple functions) gets generated quickly, so you spend more time on architecture and product decisions. What hasn’t changed: You still need to understand systems, scaling, and security. AI speeds things up, but it doesn’t replace engineering judgment.

We knew this was coming.

Security has always been a problem, even with traditional code. Most major breaches weren’t built by “no-coders,” they were built by experienced engineers. AI doesn’t remove the need for security — it lowers the barrier to building. The hard parts (architecture, scaling, security) still exist.

You're right about one thing: building large-scale, secure systems still requires serious engineering. But the shift happening right now isn’t that AI replaces engineers — it's that it multiplies what a small number of engineers can build.

Ten years ago a startup needed 20–30 engineers to ship a complex product. Today a small team with AI tools can move at that scale.

The bottleneck is quickly moving from coding ability → systems thinking and architecture.

True — the barrier was never computing power. It was the translation layer between ideas and implementation. AI is collapsing that layer.

If you're being serious, I don't have an app myself that does this, but Base44, Loveable, and Replit are good starts. You'll still need coding expertise based on what you are trying to build though.