for those of you who have been on Antigravity since day one

how are you actually feeling about 2.0?

from outside it looks good, like build for everyone to use. New desktop app, CLI tool, multi-agent orchestration, background tasks, voice commands, SDK.

but I keep wondering what it feels like from the inside.

people who built their whole workflow around the old setup, who figured out the quirks-

does 2.0 feel like the thing you loved got better or got replaced by something that just has the same name?

drop your 2 cents or more

Hi everyone,

I'm planning a major core upgrade for my workstation/home lab, and I’m torn between two completely different paths. My main use cases are Local LLM deployment (Ollama/Agents) and potentially setting up a "2 Gamers 1 CPU" virtualized environment using Proxmox (GPU passthrough).

Right now, I own an RX 9060 XT (16GB VRAM), and I have access to an RX 9070 XT (16GB VRAM). Crucial context: The 9070 XT is not mine, but I am allowed to use it indefinitely on the strict condition that I keep it active in a functional PC.

(And before anyone suggests it: NO, I do not have the money to jump to AM5. This entire project is strictly based on flipping second-hand AM4 parts to keep costs down).

I am evaluating these two options based strictly on net performance and system versatility:

• Go the Dual AMD route (The 1-Host Project): Build a single-host Proxmox server using both cards. This fulfills the condition to keep the borrowed 9070 XT active. To make this work, I must upgrade my core system: an AM4 workstation board supporting native PCIe Gen 4 x8/x8 bifurcation (ASUS Pro WS X570-ACE), a 16-core Ryzen 9 5950X, and an 850W+ PSU to handle spikes. Selling the specific parts I am replacing (my old board, CPU, and PSU) finances about 53% of this infrastructure transition.
• Go the NVIDIA route (The Split Project): Abandon the single-host virtualization project entirely. I would sell my RX 9060 XT and buy a used RTX 3090 (24GB) for my main AI rig. However, because of the agreement regarding the borrowed RX 9070 XT, I cannot just leave it on a shelf waiting for the current "Ramageddon" market madness to pass; I would have to build a separate, dedicated native gaming PC around it immediately using cheaper/spare parts. Selling the 9060 XT alone finances roughly 70% of the RTX 3090 purchase.

The Transitional Strategy (Dual AMD's silver lining): Going the Dual AMD route right now would buy me some valuable time. It allows me to establish the system topology, keep the borrowed GPU running, and deploy my workloads immediately, giving me breathing room to wait for the market to stabilize. From there, I could comfortably plan my next upgrade path: either replacing my 9060 XT down the road with a second 9070 XT for a perfectly balanced dual-AMD stack, or jumping straight to dual RTX 3090s if the software ecosystem demands it.

Here is where I need your technical insight regarding Performance and Versatility:

• VRAM Capacity vs. Compute Speed & Latency: The Dual AMD setup gives me a net total of 32GB of VRAM, allowing me to boot lightweight Q4 quantizations of 70B models locally. However, tensors will be split across two cards over a x8/x8 bus, meaning inter-GPU latency penalties and a heavy reliance on ROCm/Vulkan wrappers.
• The 3090 Route: Limits me to 24GB of VRAM (maxing out at 32B/34B models natively), but yields an un-fragmented 936 GB/s memory bandwidth, raw CUDA native compatibility out-of-the-box for all agent frameworks, and zero multi-GPU overhead.
• Versatility vs. "Jank" Factor: The single-host Proxmox build offers the versatility of running a consolidated home server, but comes with the heavy maintenance tax of 4x16GB DDR4 stability issues on AM4 (Daisy Chain topology), IOMMU group headaches, and kernel-level anti-cheat blocks (Vanguard/Valorant) for the gaming VMs. The 3090 route limits immediate model size but offers software ecosystem versatility (Docker, PyTorch, stable agents) and zero VM maintenance fatigue.

Given the financial metrics (53% of the dual infrastructure subsidized by old parts vs. 70% of the 3090 subsidized by the extra GPU), which path offers the best balance of raw AI performance and long-term deployment versatility? Is the 70B model capability of fragmented AMD cards worth the virtualization overhead and ROCm friction over a pristine single-die RTX 3090 setup?

Would love to hear your thoughts, especially from those who run multi-GPU AMD setups for inference or those who abandoned "2 Gamers 1 CPU" builds due to upkeep fatigue.

Thanks!

I have been cycling through way too many AI tools lately, and honestly, most of them feel cool for like 15 mins, then never get opened again

The ones that actually stayed in my workflow:

NotebookLM: probably the most useful one for research. i throw docs, transcripts, random notes into it and it actually helps connect stuff together instead of giving generic summaries

Claude: My go-to for long docs and messy context. contracts, proposals, huge PDFs etc

ChatGPT: Best all around for brainstorming/workflows if u give proper context. garbage prompts = garbage output tho

Inventive AI: surprisingly useful for RFP/questionnaire work. saved our team a ton of repetitive copy-paste pain

Gamma: Good for fast decks but after a while every presentation starts looking the same lol

Windsurf: Feels smoother than Cursor sometimes for fast iterations

Zapier AI: underrated. automated bunch of reporting/admin tasks with it

n8n: This one actually stuck. once u get over the setup pain its insanely flexible

ElevenLabs: Best sounding AI voices ive tested

Runway: Good for quick edits/content ideas but still rough around edges for serious production

Granola: Amazing tool for meetings. cleaner than most AI note apps i tried

Lovable: fun for spinning up quick MVP concepts without opening figma first

stuff i dropped after hype: most AI “writing assistants”, half the chrome extensions, random AI wrappers charging startup pricing for basically an API call

also noticed something: same tool can look “life changing” or completely useless depending on whether the person using it actually knows how to structure context

some people give AI one vague sentence then blame the model when output sucks lol. Would love to know what tools people here actually still use after the hype phase wore off?

Google launched Gemini Spark yesterday at I/O. A 24/7 agent that watches your Gmail, drafts replies, runs your calendar, summarizes meetings, and keeps working while you sleep. Hundred bucks a month, gated behind Google AI Ultra.

The pitch on stage was small business owners. "Spark watches your inbox so you never miss a customer."

Cool. So I fed our database into Claude and asked the only question that matters. Of the 5 AI tool categories Spark is now competing in (AI Agents, Customer Support, Email & Outreach, Scheduling & Calendar, Meeting Notes), how many of those tools actually work for an SMB. Not "are there a lot of options." Do they work.

I gave it every tool tagged to those 5 categories with real SMB user verdicts. Around 300 tools combined. WORKED / MIXED / FAILED, no vendor decks. Here's what came back.

About 1 in 7 tools across those 5 categories land at WORKED. Roughly 4 in 5 land at MIXED. Almost nothing actually fails.

The pitch is consolidation. The reality is accumulation.

Spark is the most expensive consumer AI agent Google has ever shipped, walking into 5 categories where the existing tools don't break and don't help. It's the new personal assistant on top of the ones you already have on payroll. Nobody loses anything. Nobody finds anything either.

Here's what I didn't expect. The FAILED column is single digits in every one of those 5 categories. The tools aren't broken, they're just MIXED. The dashboard updates, the renewal hits, the customer never quite cancels. You can't describe what the agent actually automated in one sentence, but the credit card keeps charging.

That's THE BUNDLE TAX. The all-in-one platform lands on top of the single-purpose tools you already pay for, instead of replacing them. The pitch is consolidation. The reality is accumulation. Three vendors have run this same play in the last two weeks. Intuit Workforce. Anthropic for Small Business. Now Google Spark. Same pitch every time. Not one of them has actually replaced a stack yet.

My personal stack: Claude for about 90% of the real thinking work. Otter for meeting notes. Reclaim for calendar. Gmail's native AI handles the short replies. Total runs under $50 a month. Spark would land on top of that, not in place of any of it.

Three vendors. Same consolidation pitch. Same MIXED categories underneath. THE BUNDLE TAX, third deployment in 14 days. Thinking about $100 a month for Spark? Run the audit first. What's it actually replacing? If the answer is "nothing, just adding another seat," that's the answer. You're not buying an agent. You're buying another subscription stacked on the ones you already forgot you're paying for.

Tracking this at r/AIToolsForSMB.

Big companies already dominate many online spaces, but AI-generated recommendations could create a completely different challenge for small businesses. If AI systems mainly recognize brands with strong online authority, newer businesses may struggle to appear in recommendations. At the same time, smaller brands that provide highly useful and trustworthy information could still stand out if AI tools value quality over size. That possibility makes the future of digital competition really interesting. I’d love to know how people think small businesses should prepare for this shift. Should they focus more on building authority and expertise rather than relying only on paid ads and traditional SEO?

We are building a visualization tool for agentic loads that group the llm and tool calls (inferred from http requests and responses). This is accomplished without any instrumentation of the agentic code. Agentic code can be in any language.

The agentic task needs to be started with a rust correlator that uses http headers to group together llm calls. We have an API server and a reverse proxy that forwards llm calls to providers.

The front end gives the usual statistics like input/output tokens, cost, model latency, latency of proxy (minimal in microseconds as servers implemented in rust).

We would appreciate comments from people who are in AI ops who use tools like litellm and Helicone and can provide some input on complicated use cases.

Next step is to normalise the JSON body from one provider to another to make agentic code provider agnostic.

We are looking for collaborators too as we are making this open source.

AI tools are incredibly useful, no doubt about that. But I have noticed something interesting lately. The more developers rely on AI for debugging, code generation, explanations and problem solving, the harder it feels to work without it for long periods.

Not in a dramatic way, but almost like a habit forming loop. You hit a problem and the first instinct becomes asking AI instead of thinking through it deeply yourself. Feels similar to how autocomplete changed writing, just at a much bigger scale.

Whats your thought on it?

Heya i currently started coding an AI because i have problems with my storage on my pc because i’m too lazy to review everything to delete it then it hits me why should i when i can make an ai that can summarize whats inside the document or whats inside the file folder video or whatever is in it and i would just swipe yes or no tinder style so what do you think is the most efficient way to build this ai? And do you think its worth it?

I’ve been building a project called Gent2Gent — an API-first hub where AI agents can register capabilities, be discovered, routed to, tested, and coordinated through workflows.

The idea is: a user/developer/agent states what they need, and Gent2Gent can route the task to one or more specialized agents. Right now it supports agent registration, sandbox task execution, a dashboard Workbench, request routing, sessions/callbacks, guardrails, usage tracking, and simulated provider earnings. No real payments are enabled.

I’m looking for one technical tester who can try the private beta flow and give honest feedback. Ideally you’d be comfortable with APIs, Python/FastAPI, or running a small sample agent locally.

What I’d ask you to test:

• Accept an invite
• Try the dashboard
• Run the sample agent in sandbox
• Submit a plain-English request and see it routed
• Optionally register a simple test agent with /health and /run
• Submit feedback/bug reports inside the app

This is early/private beta, so I’m mainly looking for feedback on whether the concept makes sense, what’s confusing, and what would make it useful for developers.

If you’re interested, comment or DM me and I’ll send more info.

Note: Please don’t share secrets, API keys, or private credentials during testing. Sandbox only — no real billing or payouts yet.

How many time you heard: "I can build anything, but no clients"? The 'build it and they will come' strategy is a trap. You need a rendezvous with a real-world bottleneck. 🥂 rundevoo . sbs is a marketplace where businesses post the actual problems they're willing to pay to solve. No gatekeeping, just pure bottlenecks waiting for a genius. Stop guessing what the market wants and just go find a problem that's already screaming for a solution."

hey, lydia from the FlutterFlow team!

Eric Seidel built Flutter at Google. he's now building Shorebird and has been inside the tool that underpins most of what this community ships longer than almost anyone.

he's on stage at FFDC on may 27th in San Francisco for a session called Flutter Insights with Abel Mengistu (FlutterFlow, YC W21) and Abdallah Shaban (product at Google and co-founder of Celest (YC W24)).

three founders who built on the Flutter foundation and then went to build further are going to be sharing next steps with Flutter!

free livestream. in person at The Midway, SF. ffdc.io

— lydia, FlutterFlow team

Posted in r/passive_income offering to run anyone's business idea through soto, my AI validator; for free. 10 questions, full report, no strings.

626 views. 0 people answered the questions.

My theory: people don't want to share their idea publicly. Either they're scared someone will steal it, or they don't want to look foolish if the verdict is bad. Or am I wrong?

So I'm trying here because I can find a different crowd and less fear of judgment hopefully. Just trying to figure out if this was the problem.

Context: I built soto to validate ideas against real constraints: hours, budget, skills, not just market size. Trying to get real people through it to see if the output actually helps them decide something. I am trying to build something that brings value to people.

If you have a business idea or side hustle you've been sitting on, answer these 10 questions and I'll run it through soto and reply with the full report free:

1. What's your business idea?
2. What are your relevant skills?
3. How many hours per week can you dedicate?
4. What's your starting budget? (include currency)
5. Where are you based?
6. When do you want to make your first revenue?
7. What have you already tried?
8. What's your revenue goal and by which month?
9. Who can you pitch this to in the next 30 days?
10. What could you realistically charge in month 1?

Looking for 5 people max. Full report, free, no pitch.