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It’s hilarious how robots are now creating viral content.
We watch a machine fail a dance move, laugh, and share it exactly like we would if it were a clumsy human. They’ve essentially been integrated into our social media culture as just another creator. At this point of rapid advancement, our desensitization is wild—if a real alien showed up tomorrow, we’d probably just make a meme out of it and move on with our day.
We used to fear technology, but now we just find it relatable. When everything is advanced, nothing is shocking anymore.
What are your thoughts?
The rain.
The city.
The helicopters.
The nightclub scenes.
The giant crowds staring at her.
This isn’t even a real movie. It’s an AI-generated neo-noir thriller called VELVET CITY and somehow it feels more cinematic than half the stuff releasing lately.
We are entering absurd territory.
Alexander Kiesel / Periti Studios
They just introduced Google Omni, and the idea is simple: Create anything from anything.
A new AI system inside Gemini that moves way beyond chatbots and starts turning AI into a machine that can create almost anything from anything.
Until now, AI tools mostly felt separated.
One model for chat.
Another for video.
Another for images.
Another for editing.
Omni looks like Google stitching everything together into one system.
You talk to it once and it can search, think, generate, edit, animate, redesign, expand scenes, change environments, create characters, and eventually probably simulate entire worlds.
The video part is just the beginning. What Google is really building looks much bigger than an AI video tool.
Omni points toward a future where one system understands everything at once: text, images, video, voice, code, physics, space, time, movement, environments, and context together. Instead of switching between different apps and models, you simply describe an idea and AI turns it into whatever you need, from a movie scene to an app, a game, a robot training environment, a simulation, or eventually an entire interactive world.
https://www.youtube.com/watch?v=jM5R9phTCN4
Original pop-videos/Suno songs and more here.
This restaurant in Croatia is delivering pizza by drone.
One drone is a cool moment. But imagine every restaurant on the same street doing this at the same time.
Suddenly the sky becomes filled with autonomous machines flying above people, buildings, cars, pets, and wildlife. Add the noise, wind, dust, and chaos, and you realize our current infrastructure was never designed for this kind of traffic.
Flying deliveries will probably still become normal. We just need to figure out how to do it properly.
Rebuilt from the ground up with multi-agent teams, scheduled tasks, native voice and one-click integration with other Google products.
Most people already outsource boring physical work whenever technology makes it possible.
We bought washing machines because nobody wanted to wash clothes by hand. We bought vacuum cleaners because nobody wanted to sweep every corner forever. We bought dishwashers, cars, microwaves, and every other “lazy” invention that eventually became normal life.
Figure AI just released a video of two humanoid robots walking into a bedroom and making a bed together in under 2 minutes.
What makes it interesting is that the robots were not explicitly communicating with each other.
According to Figure’s Director of AI, they coordinated visually in real time through movement and observation alone. Just autonomous coordination using their new Helix 02 system.
One robot adjusted based on what the other robot was doing, almost the same way two humans naturally cooperate when cleaning a room together.
Right now this still looks futuristic and expensive.
But so did washing machines, vacuum cleaners, dishwashers, smartphones, and cars at one point.
Once these robots become affordable and actually useful, a huge percentage of people will want one in their home simply because humans will always choose convenience when they can afford it.
At the same time, there are still millions of people on Earth without clean water, electricity, food, or even a roof over their heads.
The hope is that the same acceleration also helps us build a future where basic human needs become easier, cheaper, and more reachable too.
In comments
Hey everyone,
I currently have 6 micro-SaaS live, bringing in a bit over $20k in MRR.
The crazy part? I barely wrote a single line of code. I used AI to generate everything, from the database to the UI.
It wasn’t magic on day one. I spent hours stuck on broken code before I finally cracked the system:
Lately, I see too many non-tech people give up at the first AI bug. It sucks because the technical barrier is basically gone.
So, I’m starting a Skool community.
Full transparency: I will probably charge for the full course down the line. It makes sense given the exact workflows and copy-paste prompts I’ll be sharing.
But the main goal right now is to build together. Building alone is the fastest way to quit.
If you want to join and build your own AI SaaS with us: drop a comment or shoot me a DM, and I’ll send you the invite!
🚀 Google Antigravity Just Changed AI Coding Forever: 1,453+ Agentic Skills for Claude, Cursor, Google Gemini & Codex 🤯
The AI coding world is moving beyond prompts.
We’re now entering the era of installable AI capabilities — where you can give coding agents reusable skills, workflows, debugging systems, security audits, UI intelligence, multi-agent orchestration, and much more.
One of the most insane open-source projects I’ve seen recently is:
👉 Google Antigravity Awesome Skills Repository
This project provides 1,453+ installable AI agent skills for tools like:
✅ Claude Code
✅ Cursor AI
✅ Google Gemini CLI
✅ OpenAI Codex CLI
✅ GitHub Copilot
✅ Google Antigravity, Kiro, OpenCode & more
🔥 Current stats:
• 1,453+ AI skills
• 37K+ GitHub stars
• Workflow-based AI execution
• Plugin-safe distributions
• Multi-agent support
• Security, DevOps, UI/UX, SEO, testing, infra, automation & MCP integrations
Some wild skills included 👇
🧠 u/brainstorming → Turns ideas into MVP plans
🛡️ u/security-auditor → Finds auth/security risks
🧪 u/test-driven-development → AI-powered TDD workflow
🎨 u/frontend-design → Better UI/UX generation
⚡ u/create-pr → Generates production-ready PR workflows
🔍 u/debugging-strategies → Systematic debugging playbooks
Installation is ridiculously simple:
npx antigravity-awesome-skills
This is where AI engineering is heading:
➡️ AI workflows instead of prompts
➡️ Reusable agent memory & skills
➡️ Tool-aware AI systems
➡️ Multi-agent collaboration
➡️ AI-native developer operating systems
We’re watching the rise of the “Skill Layer” for AI agents in real time. 🔥
Full GitHub Repository Link 👇
Here are the Ten commands I use consistently.
/init — Generates your CLAUDE.mdfrom your existing project. Set CLAUDE_CODE_NEW_INIT=1 first for the full interactive setup: skills, hooks, and personal memory. Not perfect, but 80% done in three seconds. You edit, not write./compact [instructions] — Run at 70-75% context usage, not when Claude warns you. Always pass instructions: /compact focus on the auth module, ignore the migration files. Without them, you get a generic summary. With them, the important context survives./rewind — Full checkpoint rollback. Reverts the conversation and all file changes back to any earlier point. Use it when Claude goes out of scope, breaks something with an unsolicited "improvement," or you want to try a different approach from the same starting point./plan [description] — Pre-load the task into the command: /plan refactor contract validation to handle Arabic RTL edge cases. Claude enters plan mode already thinking about your specific problem, not waiting for a follow-up./context — Shows a colored breakdown of what's consuming your context window. Not just a number, it tells you what's causing the number. Found my CLAUDE.mdwas eating a noticeable slice of context on every message. Trimmed it that day./btw [question] — Ask a side question without adding it to conversation history. The response doesn't carry forward. Zero context cost. I use it for quick one-off lookups mid-session: library defaults, pattern support, anything I'd otherwise open a new tab for./security-review — Analyzes the git diff of your current branch for vulnerabilities. Fast because it looks at what changed, not the whole codebase. I run it before every pull request on anything handling user data. It has flagged subtle input handling issues three times that I would have shipped./insights — Generates an analysis of your recent Claude Code sessions: where you spend the most turns, where friction keeps appearing. I ran it after two months on a project and found I was re-explaining the same parsing logic every session. That pointed directly to a gap in my CLAUDE.md. The fix took 15 minutes./diff — Opens an interactive viewer of uncommitted git changes with per-turn diffs from the current session. Left and right arrows switch between the full diff and individual Claude turns. You can trace exactly which turn added a function, changed a variable, or introduced an edge case./effort [low | medium | high | max] — Controls reasoning depth without changing the model. Use low for documentation and comment cleanup. Use high or max for architectural decisions and complex refactors. Defaulting to max for everything wastes tokens on tasks that don't need the depth.Most people use Claude like a smarter Google search.
Ask a question.
Get an answer.
Start over tomorrow.
I was doing the same thing… until I turned it into a persistent “AI coworker” system.
Now it:
The difference was building a simple “Claude Cowork” setup.
Here’s exactly how I structured it:
I switched from the browser version to the desktop app.
It feels way better for long sessions, local files, and persistent workflows.
I made a folder called:
Claude Cowork
Inside it:
/ABOUT ME
/OUTPUTS
/TEMPLATES
Inside ABOUT ME, I added files like:
about-me.md
my-company.md
writing-style.md
anti-ai-writing-style.md
These files basically teach Claude:
This alone massively improved output quality.
This part was surprisingly powerful.
I literally asked Claude:
>
It started asking:
Then I saved the answers into markdown files.
Now the AI has long-term context without me repeating myself constantly.
I added instructions like:
Always read the ABOUT ME folder before starting any task.
Match my writing style.
Use existing templates when relevant.
This made responses dramatically more consistent.
I use:
Way more efficient than using the same model for everything.
I opened the entire folder inside Obsidian.
Now:
It basically becomes a second brain + AI workspace.
Whenever Claude produces something great, I save the structure into /TEMPLATES.
Examples:
Over time, the system gets smarter because you stop starting from zero.
Most people think:
better prompts = better outputs
But honestly:
better context > better prompts
Once the AI deeply understands:
…the outputs become insanely more useful.
It stops feeling like a chatbot and starts feeling like a collaborator.
Curious if anyone else here is building similar “AI operating systems” around Claude or other tools?