Browse Comments — Clean (de-noised)

Pascaline Amuzu

hilarious!

Sheshadri Bhattacharyya I’m rooting for Google too. The vision is right, AI orchestration is clearly the future. But execution still matters. Right now, a lot of developers care less about “93 agents in parallel” and more about reliability, output quality, and cost efficiency. That’s where Claude and ChatGPT still feel ahead for serious coding workflows and deeper reasoning tasks. That said, Google has something almost nobody else has: world-class talent, infrastructure, distribution, and control across the entire AI stack. If they can align product quality with that advantage, they could become extremely hard to beat. Competition here is good for everyone building with AI.

Milind Gune That’s the bigger shift here. The real breakthrough isn’t just coding faster, it’s compressing enterprise-grade capability into consumer hardware. Once powerful local AI models can run efficiently on standard laptops, the barrier to innovation drops dramatically. A single person with a laptop could soon access capabilities that previously required an entire engineering department and massive cloud infrastructure. That changes who gets to build.

Shiza Akif That’s the real paradigm shift. We’re moving from “writing every line yourself” to architecting systems where multiple agents collaborate effectively. In many ways, prompt design, context management, task decomposition, and workflow orchestration are becoming the new software engineering fundamentals. The developers who thrive won’t necessarily be the fastest coders, but the best coordinators of intelligence.

Oomkar S. This has been a common sentiment from many early users. The vision was exciting, but the developer experience felt fragmented. In AI tooling, raw model capability alone isn’t enough anymore. Developers care deeply about onboarding, reliability, observability, pricing transparency, documentation, and workflow integration. If those pieces break, even strong models become frustrating to use. Google absolutely has the infrastructure and talent to fix this though. If they can combine their model scale with a truly polished developer experience, they’ll become a very serious force in AI engineering workflows.

Keanu Dedenbach The abstraction layer keeps moving higher. First we managed hardware, then software frameworks, then cloud infrastructure. Now we’re managing intelligence itself, deciding goals, context, constraints, and coordination between agents. The speed of the shift is what surprised everyone. What felt experimental 12 months ago is rapidly becoming a new operating model for building products.

Joe Allen That’s why developers are becoming far more pragmatic now. The winning platforms won’t just have the best demos, they’ll offer the best developer economics and reliability. If smaller players can provide generous limits, transparent pricing, and smoother workflows, developers will naturally gravitate there regardless of who owns the biggest infrastructure. In AI tooling, trust is built through consistency and usability as much as raw capability.

Daniel Velasquez There’s definitely a valid concern here. A lot of “agentic” products today are still probabilistic systems wrapped in impressive demos, and without strong deterministic tooling underneath, reliability becomes a real issue for production workflows. And yes, the economics matter. Running large multi-agent systems is expensive, so eventually pricing has to reflect compute usage somehow. That said, I still think the broader direction is real. The companies that win will likely be the ones that combine agentic flexibility with deterministic guardrails, predictable workflows, and pricing developers can actually sustain.

Wish Bakshi That’s the challenge with AI products right now, expectations are incredibly high because the demos look futuristic, but developers judge based on day-to-day workflow quality. Throttle limits, model access, consistency, and reasoning quality matter far more than flashy benchmarks once you’re actually building production systems. Google still has enormous potential here, but the gap between capability demos and developer experience is something they’ll need to close quickly.

Alvin Foo 💯

The coding part is solved. 93 agents building an OS in 12 hours is impressive. But the next frontier isn't speed — it's whether the software actually understands the person using it. That's the gap nobody's closing yet.

Alvin Foo yes I also believe the future is agentic, but right now it is unsustainable. Compute costs are really high, and this is more a structural problem. Having 93+ agents running in parallel without a concern of how many tokens they are going to use is not an efficient approach at all. Ang Google really messed things up forcing its developer community to use antigravity 2.0, without any previous notice and developers just need the tools to build their agentic workflows, not a final product with a huge ticket price.

Jump in the wheel Gerbil.

Glad to see Google empowering Antigravity, tbf one can find repos in GitHub that can do this with whatever frontier model you choose but this is good for novices.

Is there someone who can personally present the Doctrine of the #SystemOfConsciousness to Pope Leo and tell him to stop? There are still plenty of "PROBLEMS" in the world without him. #PopeStopNonsense #SymbolicAI #CognitiveArchitecture #AIandHumanity #PhilosophyOfAI #ArtificialIntelligence, #AIAlignment.

Csaba Zsolnai Its already there. For example, Qwen-3.6-35B-A3B can run on Ollama with a standard mid-level gaming GPU and mimicking quality found at the GTP-3.5-Turbo level while pushing out over 20 tokens a second. Qwen-3.5-397B-A17B, a bigger and much stronger AI engine is better than GPT-4 @ FP8, in my opinion and even better at FP16 I would imagine, but that would take over 1TB to run it at FP16 while leaving enough room for a sufficient context window. The costs to run this would require 100s of thousands of dollars, 240V hookups, and NVIDIA's industrial grade GPUs that are hard to come by today. ⠀ This is achievable on the LibreChat platform which is designed to integrate with Ollama, vLLM, open-source LLM providers, and proprietary models. The challenge is that the systems are moving fast, constantly changing and faster than anything I've seen in my 20+ years with open-source software. A new Wild West is underway. Those that begin to team up with AI open-source engineers and rig developers will own their destiny. ⠀

But can it run Doom?

I have mixed feelings about this. I want to say more, but it’s all been said.

What is new? Neoliberals often quote Adam Smith, but few have actually read Wealth of Nations, much less Moral Sentiments. So-called Marxists never read Capital. Many refer to the Bible for moral justifications, but struggle to find the passages they believe are written. To read or not to read has always been a choice. Nothing new under the sun - Ecclesiastes 1:9.