@shmidtqq Absolutely incredible. The edge isn't generating code. It's absorbing the entire brief-to-launch pipeline. At this rate, taste is our only remaining bottleneck. Exciting times ahead.
Ideogram 4 not only revamped their model to the best they built yet, but also they flipped from closed to open weights!
With a 9B parameter model, I can't wait to see what the community will do with it
Try it now: https://t.co/EdBlvFYQAL
The weights for Ideogram 4 just dropped on GitHub for a 9.3B model that you can run completely local. No more paying high monthly fees for some typography and brand design 👀
Man. Ideogram just dropped the best open-weight image model in the world. 🤯
Ideogram 4.0 is now #1 on Design Arena's leaderboard. The only models ahead of it are closed ones from OpenAI and Google.
And Ideogram just opened theirs up. Download the weights. Fine-tune on your own data. Run it on your own hardware.
Here's what it actually does well:
Text inside images finally accurate. Logos, posters, signs, multi-font layouts. The one thing every other image model gets wrong, Ideogram nails.
Native 2K resolution. Transparent backgrounds built in. And you can control exactly where every object, text, and layout element goes.
Already live on Hugging Face, ComfyUI, Leonardo, Picsart, Krea, Cloudflare, and 12+ platforms.
Editable text, movable layers, and full image editing coming soon.
Every other AI image tool keeps their best model locked behind closed
doors. Ideogram just opened theirs up.
Ideogram 4.0 is now an open model 🤯
You can download, fine-tune, and run on your own hardware, giving creators real control across photography, art, cartoons, text-based designs, and almost every visual style
This girl cost $0.59. and you can barely tell she isn't real.
UGC creators charge $80 to $150 a video. brands book a dozen at a time and wait two weeks for the batch.
an AI agent writes the script, casts the face, and renders the whole thing. real voice, real movement, under a dollar an ad. you spin up as many as you want in an afternoon.
paying a person $100 to hold your product and read a script stops making sense fast.
UGC creators are cooked. prove me wrong.
full blueprint in the article below.
A $599 box now does the trade review I used to pay an analyst $3,000/year for not a new strategy.
Not a secret indicator.
Mac Mini M4 + a local model + my trading journal.
Same process. Completely different cost structure.
Most traders either review nothing or pay monthly for tools that lock their own data behind a subscription.
Smarter split: run the heavy journal analysis locally, save the cloud calls for the few things that actually need a frontier model.
Roughly $4/month in electricity instead of a recurring bill that never stops.
Apple sold me a desktop computer.
It quietly became the most honest trading coach I've ever had.
It doesn't flatter me. It shows me exactly where I bleed money — and it remembers every mistake I've ever made.
Save this before everyone's running their edge on a box under the desk.
THIS CHINESE SCHOOLBOY CONNECTED AN NVIDIA CHIP TO HIS IPHONE AND MAKES $8,000/MONTH FROM AN AGENT THAT RUNS HIS PHONE ON ITS OWN
Phone Agent Control Panel on screen - powered by Qwen2.5 via llama.cpp - the agent opens apps, dials numbers, navigates the UI and executes tasks one digit at a time
NVIDIA handles the compute locally, iPhone is just the hands - no cloud, no API fees, no subscription
421 tokens per second running locally - fast enough for real-time phone automation that actually works
he types the task once - the agent handles everything else on the phone while he does something else
while his classmates are still doing homework manually his phone is out there closing deals
xAI shipped a frontier video model in 90 days.
The engineer behind it just gave away the entire playbook.
Ethan He sat down for 1 hour 40 minutes and broke down what it actually took to build Grok Imagine from zero to one.
Why iteration speed beats everything else in model development, why tiny data bugs cause the biggest gains.
Why generative UI may replace traditional interfaces, why language models will unlock the next wave of video.
Most labs take 3 years to ship what xAI shipped in a quarter.
The exact reason why is in this conversation.
Starcloud just became the fastest YC company ever to a $1B valuation after Demo Day. 17 months. Building data centers in orbit.
The hardest possible problem, the fastest possible ascent. This is what we should be building.
I created the entire video sequence for this MV with Dreamina AI with Octo & Dreamina Seedance 2.0.
It stays in sync with your canvas, thinks with your ideas, and turns story fragments into motion.
This is vibe create.
A trader from Singapore was drowning in subscriptions.
Claude. ChatGPT. Three other AI tools. $400 every month, gone.
Then he did the math nobody wants to do.
$400/month × 12 = $4,800/year. Just to rent AI he could own.
One weekend he bought a $599 box. Plugged it in under his desk.
Now it runs his entire trading analysis 24/7: → Morning macro briefs → News sentiment scans → His 4-year trading journal, analyzed locally → Patterns he'd never find by hand
Nothing leaves his network. Not to Anthropic. Not to OpenAI. Nobody.
Last month's bill: $3. In electricity.
He stopped renting his edge. He started owning it.
The full setup 👇
Meet Pupper V3
Exploring the future of robotics with this open-source quadruped robot powered by ROS, AI, and autonomous control. From robot locomotion and computer vision to real-world robotics applications, Pupper V3 is an incredible platform for learning robotics engineering and embedded systems.
Building, coding, testing, and pushing the limits of what a four-legged robot can do. 🚀
Follow for more robotics projects, AI development, ROS tutorials, robot programming, STEM innovation, and engineering content.
Project by Stanford robotics club
GitHub link - https://t.co/yDG4GPaXHY
Project Documentation Link -
https://t.co/i3X4mSrdMe
#PupperV3 #Robotics #QuadrupedRobot #RobotDog #ROS ROS2 ArtificialIntelligence MachineLearning RobotProgramming Engineering STEM OpenSource EmbeddedSystems ComputerVision Automation TechInnovation RoboticsEngineering DIYRobotics FutureTech TechReel
AI Isn’t Just ChatGPT. It’s the Result of Decades of Innovation.
Most people see AI through the lens of ChatGPT, Claude, Gemini, or Midjourney.
But those tools sit at the very top of a technology stack that has been evolving for decades.
From Classical AI that relied on rules and logic…
➡️ To Machine Learning that learned from data.
➡️ To Neural Networks inspired by the human brain.
➡️ To Deep Learning that unlocked breakthroughs in vision, speech, and language.
➡️ To Generative AI that can create text, images, video, and code.
➡️ And now to Agentic AI, systems that can remember, plan, use tools, and autonomously execute complex tasks.
The future won’t be defined by who uses AI.
It will be defined by who understands how these layers connect and how to orchestrate them into real-world outcomes.
We’re moving from AI as a tool ➜ to AI as a collaborator ➜ to AI as a digital workforce.
And we’re still in the early innings.
The biggest opportunity isn’t learning one AI application.
It’s understanding the roadmap and positioning yourself ahead of the curve.
What layer do you think will create the most disruption over the next 5 years: Generative AI or Agentic AI?
i use at least 3 different AI models every week
every time i switch i start from zero, re-explain everything, same context same preferences over and over
there is a Chrome extension that finally fixes this 🧵
A woman who cannot code is running a supercomputer with 3,000 NVIDIA GPUs and an $800,000 per month subscription to Claude Opus.
She did not write the code. She directed the intelligence that wrote it.
The machine her team is running generates 13 teraflops of raw compute. The AI brain powering it was purchased from Anthropic at $800,000 a month. Not a one time cost. Every single month.
And right now that brain is 60 percent of the way through building a quantum computer.
The remaining 40 percent is not a skill gap. It is a technology gap. The physical components do not exist yet at the scale needed. By 2035 they will.
When that machine is finished the distance between it and anything you currently call computing will not be measurable in normal terms.
The people building it are not waiting for the world to catch up. They are running at the edge of what physics currently allows, using AI as the engineer, the coder, and the research assistant simultaneously.
The gap between that level and where most people are sitting right now is already enormous.
It grows every month.
The moment that quantum machine is operational the economy around human labor changes in ways most people are completely unprepared for.
Speed is the only real advantage left.
@NeuroClubAi exists for the people who understand that.
Learn to become a creator, automate the work, and stop selling your hours before the hours stop being worth anything.