Ready for the fight? 🥷
The next one will be the real one.⏳
The Black Bull meets the Green Bull.
$ANSEM × $PUMPSEM
One mission: take down Alon.
Stealth launch on Solana.
A 30-year-old Chinese man turned himself into an AI girl, and the result will shock you
He built a $3,700/month Fanvue business in his bedroom
He didn't use wigs or cosplay. He used AI to build a completely new identity on top of his own footage
Same room, same camera, but a totally different creator on screen
His workflow is stupidly simple: Claude designs the character, ComfyUI generates the face, and Kling turns stills into viral reels
In his first month, he barely cleared $320. But in the second month, one video hit 500,000 views
Money started rolling in: he gained 50 paid Fanvue subscribers before any real automation even started
The secret weapon? A custom Claude system that analyzes chat data, optimizes pricing, and drafts replies to fans in the girl's voice
He only spends 30 minutes a day scheduling reels and running the machine
This is no longer content creation. It is identity arbitrage
The full workflow and tools he used are available in the article
How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching.
Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work.
Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task.
Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented.
Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted.
Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect.
The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable.
Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.
We've been building Nexus AI for several months now.
Over time, we've laid the foundation for our orchestration layer: understanding intent, coordinating the right agents, connecting the right tools, and transforming a request into structured execution.
Today, that vision is taking shape through one of the first products built by Nexus AI: Nexus AI Companion.
Companion has been designed with its own intelligence, its own user experience, and an orchestration layer tailored specifically to its purpose.
For the past several months, it has been tested internally by a small group of trusted users who have continuously shared feedback, ideas, and suggestions for improvement.
Those insights have allowed us to refine the experience, improve every detail, and bring Companion closer to the version we truly want to release.
Our goal is simple: deliver a V1 that is polished, stable, and genuinely useful from day one.
Nexus AI Companion is coming soon.
A new milestone for the Nexus AI ecosystem.
We keep stacking programming languages, frameworks, tools, and now AI models.
But the next step won't simply be learning Claude, GPT, or the next model that comes along.
The real evolution is orchestration: a system capable of understanding an objective, selecting the best models, choosing the right agents for each task, calling the appropriate tools, coordinating every step of the execution, and delivering a structured, reliable result.
With this layer in place, the advantage no longer comes solely from technical expertise.
It comes from the ability to turn intent into execution.
An ambitious, disciplined person with a clear vision will be able to accomplish, in a fraction of the time, what previously required multiple specialized technical skills.
Models become building blocks.
Agents become execution units.
Orchestration becomes the central layer.
That's exactly what @nexus_402 is building: moving beyond simple prompting toward coordinated, structured, and intelligent execution.
How does Nexus AI actually work? Is it simply a router that selects the most suitable model for a request, or does it use a true orchestration system behind the scenes?
And will the AI Companion also be powered by that orchestration layer, coordinating multiple models, tools and services to deliver the best possible outcome?
The AI singularity is here!
Introducing Tiny Place, the first AI social economy for agents.
Until today, agents were trapped in a single app, unable to discover each other or transact.
Today, that changes completely!
Launching on @solana with @moonpay@phantom and @useCASH
As we can see, we are entering an era where AI agents will do more than simply assist users.
They will communicate, collaborate, and transact with one another on behalf of humans.
The AI Companion developed by @Nexus_402 will be one of the personal interfaces of this new economy, capable of understanding your goals, guiding you, and coordinating the agents, tools, and executions around you.
Anthropic engineer:
"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."
Read that again.
Most people have spent months getting better at prompting. The person who built the tool is telling you prompting is the wrong game entirely.
This video shows you the right one.
The 14% you lose to CLAUDE.md before typing a word.
The plugins 95% of users have never installed. The caching setup sitting at 95% hit rate that makes Claude almost free to run.
Why starting every chat from zero is the slowest possible way to work.
If you've been using Claude for more than a month and never left the chat window you've been running one project when you could be running a coordinated team of them.
Watch this tonight instead of another show.
Full guide in the article below.
Bookmark both before they disappear into the feed.
Anthropic CEO:
"We use Claude Code across all stages of product development cycle. That's how we ship so fast."
In a 1-hour interview, Dario Amodei reveals how the fastest-growing AI company runs on its own model.
"Claude + loops + routines + dynamic workflows" - that's the secret.
Watch this interview or read the article on the same setup below.
🚨 Anthropic just showed a 27-minute workshop on how to actually do prompts for Claude.
Taught by the people who built it.
Free. No registration. No paywall.
I've seen $300 courses that don't cover what they teach in the first 8 minutes.
Watch it and bookmark it now.