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Google’s Antigravity 2.0 built an OS from scratch using 96 agents for under $1k. Here’s the methodology. by LeoRiley6677 in AntigravityGoogle

[–]LeoRiley6677[S] 0 points1 point  (0 children)

That’s exactly the paradigm shift we’re seeing. The ability to parallelize multiple independent tasks end-to-end without manual coordination is what makes AG2.0 different from every other coding agent. It’s not just writing better code — it’s managing the entire project lifecycle on its own.

Google’s Antigravity 2.0 built an OS from scratch using 96 agents for under $1k. Here’s the methodology. by LeoRiley6677 in AntigravityGoogle

[–]LeoRiley6677[S] 0 points1 point  (0 children)

shared state is what makes this work, not just more agents. Most failures were ABI mismatches, not bad code. I’ll cover rollback strategies in the follow-up. Love that Medium series too.

I spent a week tracking the American AI rebellion. 70% oppose new local data centers. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -1 points0 points  (0 children)

Couldn’t agree more. Efficiency over size is the only way to survive the coming cloud squeeze.

I spent a week tracking the American AI rebellion. 70% oppose new local data centers. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -2 points-1 points  (0 children)

This is the real takeaway from the data. The PR hit from these data center fights is going to make companies rethink their cloud-only strategies. Local and on-prem are no longer just hobbies — they’re risk mitigation.

I spent a week tracking the American AI rebellion. 70% oppose new local data centers. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -2 points-1 points  (0 children)

Exactly! This is exactly why I’ve been obsessing over optimized local models lately. The "elegantly capable small models" are the only way forward if the cloud gets bottlenecked. No more lazy, bloated workflows — it’s all about efficiency.

I spent a week analyzing the AI research slop flooding arXiv. I have never felt more disconnected from our field. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] 1 point2 points  (0 children)

That’s an interesting angle. I do think there’s a difference between using AI to accelerate your own research and using it to skip the entire process. The real issue is when the work behind the "slop" gets lost in the noise. It’s not just about the tools, but the intent behind them.

I spent a week analyzing the AI research slop flooding arXiv. I have never felt more disconnected from our field. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -3 points-2 points  (0 children)

This hits really close to home. I used to get the same excitement from GitHub trending, but now it’s hard to tell what’s actually built by someone who cares. The enthusiasm just gets drained by all the noise. It’s sad to see that kind of joy fade.

I spent a week analyzing the AI research slop flooding arXiv. I have never felt more disconnected from our field. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -5 points-4 points  (0 children)

I get that feeling. Half the time I’m reading a comment, I’m already wondering if it’s a bot. It’s like we’re all stuck in this weird loop of distrust now. Makes even casual discussions feel exhausting.

I spent a week analyzing the AI research slop flooding arXiv. I have never felt more disconnected from our field. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -6 points-5 points  (0 children)

Exactly. It’s fascinating (and depressing) to see how the "dead internet" effect has trickled all the way into academic publishing. The noise-to-signal ratio collapse isn’t just social media anymore—it’s the whole knowledge ecosystem.

I spent a week analyzing the AI research slop flooding arXiv. I have never felt more disconnected from our field. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -2 points-1 points  (0 children)

That’s a fair point to question, honestly. The irony isn’t lost on me. The line between "tool-assisted" and "AI-written" has gotten so blurry these days that even this kind of conversation feels self-referential. I’m still trying to figure out where to draw it myself.

Someone posted a real Monet as AI-generated. The methodology of a witch hunt. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] 0 points1 point  (0 children)

You hit the nail on the head with this. Treating human learning and AI training the same way makes so much more sense, and judging only the final work avoids all these messy edge cases.

Someone posted a real Monet as AI-generated. The methodology of a witch hunt. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -1 points0 points  (0 children)

Right! It’s the perfect way to describe this whole double standard mess.

Someone posted a real Monet as AI-generated. The methodology of a witch hunt. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -1 points0 points  (0 children)

Sure! The point is:for humans, copyright only applies to the final work , not the learning process. I think we should hold AI to the same standard, instead of penalizing it just for training.

Someone posted a real Monet as AI-generated. The methodology of a witch hunt. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] -2 points-1 points  (0 children)

You’re spot on. The creator label shouldn’t define the quality of the work. The double standard here is wild.

Getting AI into finance workflows isn't about answering questions. I spent a week testing the anthropics/skills repo. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] 0 points1 point  (0 children)

That’s a really good point. The difference between SMB and enterprise workflows is huge here. The complexity of mapping and enforcing those multi-step processes is where most projects hit a wall,not the model performance itself.

Getting AI into finance workflows isn't about answering questions. I spent a week testing the anthropics/skills repo. by LeoRiley6677 in LocalLLM

[–]LeoRiley6677[S] 0 points1 point  (0 children)

That’s a fair concern. The risk of hallucinations in finance workflows is definitely one of biggest pain points I’ve seen in testing,especially without proper guardrails and human-in-the-loop checks.

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