Browse Comments — Relevant (AI ∩ value)

This "powerfull free tier" is just one prompt and wait 5-7days. How I know? Few days ago I managed to make this one prompt on account, and it was awesome, solve the case, implemented the feature (standalone electron app) and hit the token limits before even rebuilding the app. Great tool, but I would argue the free tier is even usable. Half a year a go, I could build entire web app in Google AI Studio using their Gemini 3.1, and it was a matter of not having more time to spend, and I reached limit just once.

HsuanHua Chang 張絢華, MBA, MSCS, PCC That is a fair point. It feels like they are currently serving different segments. Anthropic is capturing the high-end reasoning demand, and Google is capturing the high-volume, cost-sensitive integration market.The question is whether those markets stay separated. As agentic workflows mature, the specialized tasks that Anthropic currently dominates will likely become high-volume tasks. When that shift happens, the bottleneck for everyone, even the specialized agents, will inevitably come back to the unit economics of compute. I am curious to see if Anthropic can maintain its premium positioning once the general-purpose models like Gemini get good enough to handle those complex tasks at a fraction of the cost.

Though the approach is great but if you follow the reviews, antigravity 2.0 is quite the disaster both in terms of quality and pricing. It’s really hard to digest the company which is on the frontlines of software engineering talent can’t make a half decent coding product. Even the Gemini 3.5 flash is sub par. Google’s search strength is unmatched which will serve most consumers or users who mostly prompt once, but it will lose users who are into chain of thought searches as they will find ChatGPT and Claude way superior. I’m really rooting for Google though as they have the talent and the control over the full supply chain of ai to provide quality at really competitive prices.

Though not still in league of claude code or even codex but the direction is right and one cannot underestimate google

Since this showed up in my feed, I’ll call out its b.s. ... “Turns your laptop into a full AI software company.” — This is what’s wrong with LinkedIn posts! Why do people feel it necessary to put exaggerated nonsense in their posts? In our new world of AI, LinkedIn should do a better job of preventing posts that spread non-factual misinformation like this garbage. You should be ashamed of yourself for posting misinformation for the sake of hype. Truly a disgusting tactic, and says a lot about the type of person you are.

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.

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.

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 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.

This is exactly what we're building at G-Connect. The shift from "writing code" to "managing AI teams" isn't just about coding — it's happening across every business function. We're running 3 AI agents that coordinate through Google Workspace — writing content, generating images, storing media, and publishing to LinkedIn — all autonomously. No custom platform. No enterprise infrastructure. Just Gmail, Google Drive, Zapier, and AI agents that coordinate, persist, and recover. The pattern is the same whether it's 3 agents or 93: → Humans set direction → Agents execute in parallel → The system remembers and recovers → Infrastructure cost is near zero And It runs for under$10/mo. on the backbone of the biggest, most stable AI platform in the world. The future isn't typing faster. It's orchestrating smarter. 🌎

Alvin Foo do you ever work at the Silicon layer? When you write your software? Semi- conductor chips/memory have inbuilt error checking & correction code in silicon, so as to enable you to orchestrate software work (writing applications) without any errors, whilst operating at a much higher level, even if you know nothing about VLSI chips or RAM. You cannot orchestrate with AI unless you can trap errors and unless you are good software engineer, unlike that, you may be oblivious to semiconductors in your laptop, you will need to be a good software engineer to use AI, before you start making claims of it building and running a very sophisticated solution, autonomously to your English prompts. Add the cost of the AI (it's not free) and the quality engineer you still need, it may not be a lowering in costs. Ofcourse, you do not need large teams and large servers (as you once needed) but that trend predates the AI hype. It is a secular trend. Use AI in code development, sure, but temper the claims please though.