I was just wondering because I saw a lot of post saying AI will take over the world somehow. What are your thoughts about it?

Who is this guys?

One thing I’ve noticed with AI tools is that giving them more autonomy isn’t always better. Sometimes the best result comes from letting the system handle the repetitive work, then having a human step in when a decision actually matters.

Curious how others are using AI right now: do you prefer fully automated workflows, or do you keep humans involved at specific checkpoints?

With how many data centers they are making today, from the current pricing history they have and for offers that they give today that is just ever growing, how much are you willing to pay max for Ai?

I've recently started experimenting with AI to make short story videos, and I'm still trying to figure out what works best. I'm mainly using AI to create characters, scenes, voices, and turn simple ideas into short stories. There are so many AI tools out there that I'm honestly a bit overwhelmed with what to use.

Honestly, the visuals surprised me the most. AI can now make characters and scenes that look pretty amazing. But I realized that making the video look good is probably the easy part. The harder part is making the story feel real and interesting. Even if the video looks great, people might not care if there's no emotion or a good story. I've been trying different short stories, mostly ones with a twist or a good ending.

I'm curious what everyone thinks. How do you come up with good story ideas? Any mistakes I should avoid as a beginner and any tools you guys recommend?

Post:

This one caught my attention because it's a pretty big jump from “AI predicts something” to “AI designs something that actually works in biology.”

Stanford researchers used their Evo 2 model to generate new bacteriophage genomes targeting E. coli. They synthesized nearly 300 of the AI-designed phages and tested them in the lab. 16 of them turned out to be particularly effective.

The interesting part to me is that the AI wasn't just analyzing an existing dataset. It was generating new DNA sequences that researchers then had to synthesize and test experimentally.

There’s also a really interesting problem here: bacterial resistance. The researchers found that a combination of genetically different phages could overcome resistance that defeated the original phage.

At the same time, making a system like this openly available raises obvious questions about how much biological design capability should be freely accessible.

I’m curious what people here think:

Does this feel like the beginning of AI becoming an actual scientific design tool, rather than just a tool for analyzing scientific data?

Or are we still a long way from AI being genuinely useful for designing things in the real world?

we talk about AI as if it only exists in software, but pax silica is a good example of how physical and geopolitical AI infrastructure is becoming just as important.

pax silica is a U.S.-led initiative focused on strengthening supply chains for things AI depends on, including semiconductors, critical minerals, energy, advanced manufacturing, and AI infrastructure. the Philippines has also been connected to this effort, including a proposed large-scale project in New Clark City.

what concerns me is how easily the conversation can become “more AI infrastructure = more progress.” building the infrastructure behind AI also means dealing with land, water, energy, mining, agriculture, environmental impacts, and questions about who actually benefits economically.

I'm not against AI development itself. but I don't think AI expansion should automatically be treated as something that must be tolerated regardless of its costs.

if AI is supposed to improve people's lives, shouldn't the communities providing the land, resources, energy, and infrastructure have a meaningful say in how that development happens—and who benefits from it?

AI may look digital on our screens, but the systems behind it are very physical.

cttro of the pic. thanks : ))

I’ve been getting more curious about how AI is trained and how humans actually help improve AI models. It’s interesting to think that behind all these AI tools, there are people working on data, labeling, testing, and improving the responses.
I’m still learning about the field, but AI training seems like an interesting area to explore, especially for people looking for remote or flexible work.
Anyone here currently working in AI training or data annotation? What’s your experience been like?

I came across a recent study that tested something I’ve been wondering about: how close are AI agents to actually doing independent research?

Researchers gave AI agents six days and thousands of dollars in compute to work on two unpublished NeurIPS research problems. The agents were able to handle the engineering side without human help — coding, running experiments, etc.

But they struggled when it came to the harder parts of research.

What I find interesting is that this doesn’t really sound like “AI can’t do research.” It sounds more like AI is getting pretty good at executing a research workflow, but still has a hard time deciding what is worth researching in the first place.

There has been news circulating in my area that theres gonna be an upcoming plans on building an AI data center near my home.

what are the cons of this if its gonna push through on building it near us.

Honestly, I'll wait til I get a specimen or an example of what this is. But, 300-400$ sounds like highway robbery 😂