Thanks for joining last week’s live test.
We’re refining the flywheel so rewards land more precisely on real AI usage — product use feeds the main pool; social and invites stay capped support.
Both sides grow together. Share the upside of actually using AI.
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All feedback received — iterating fast.
API live. Compute orders in.
Thanks community.
i decided to test the @Pan_Ecosystem Playground with a simple prompt: “Analyze data.” 👀
here's what i got 👇
🔸 it generated a python example using 5 sample crypto wallets.
🔸 it ran the analysis and displayed the total volume, average transactions per user, and the wallet with the highest volume.
🔸 it explained the results step by step in a way that's easy to understand.
the whole process took around 15 seconds.
another thing i liked is that i could copy or download the code directly from my phone. no need to sit in front of a computer.
for anyone exploring AI tools, this is a simple way to test a model's capabilities before integrating it into an app or agent using an API key.
still exploring #PAN# and discovering more features as i go. one step at a time.
have you tried the Playground yet ?
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369 approved for weekend beta.
Thank you for testing with us—we’ll keep improving.
starting my #PAN# journey
exploring a more open future for ai compute with @Pan_Ecosystem
pan is building a demand layer for ai computations, and i'm getting started early.
-> go to:
• sign up
• go to the pan tasks section
• complete tasks to earn points
• stay active for future updates
#PAN#
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PAN is still in beta.
Your feedback matters.
What felt useful?
Community testing is live.
A few are already in.
More soon.
Cheap GPU hours are not cheap compute.
A medical or agent workload
dies on the run that never finishes.
Hourly price is a listing.
Completed-job cost is the bill.
Buy runtime that starts, checkpoints, and settles.
Not idle silicon.
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Users don’t buy GPUs.
They buy reliable runtime:
job starts, job finishes, and usage is metered correctly.
Agent compute is not one big bill.
It is hundreds of small executions
that become visible only after they add up.
14,000 designs. 9 binders. 3 sub-nanomolar.
The agent didn't win because it was smart.
It won because the loop was closed:
design → wet-lab validate → feed back.
Autonomous execution only pays off
when there's a checkpoint at the end.
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Using NVIDIA Proteina-Complexa, @muni_bio's autoresearch agent explored nearly 14,000 protein designs.
@adaptyvbio validated nine TREM2 binders, three with sub-nanomolar affinity.
The results show how agents can connect computational design with wet-lab feedback to improve the next round of discovery. 📘
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A wallet is not an approval system.
If an agent can spend,
you need to define:
what it can buy,
how much it can spend,
and when a human must approve.
Open models do not just create more builders.
They create more agent attempts.
More experiments.
More tool calls.
More failed runs.
More inference demand.
More need to verify what actually happened.
The open-source wave expands the execution market, not just the model market.
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Agents need receipts.
Not screenshots.
Not vague logs.
A real one should show:
intent → compute → verification → payment → result.
PAN is building that execution record.
Voice is becoming the control layer for agents.
ChatGPT on desktop can now listen, speak, coordinate work and direct multiple agents across your computer.
That means AI is moving from “answering questions” to operating workflows.
As agents take action, they will need infrastructure to access compute, verify execution and settle value automatically.
PAN is building that layer:
verifiable compute settlement for the agent economy.
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ChatGPT Voice is now in the desktop app.
Control your computer and direct multiple agents running in ChatGPT Work or Codex, using just your voice.
It's powered by GPT-Live, so it can speak, listen, and coordinate work in the app at the same time.
Rolling out globally today on macOS and Windows to Plus, Pro, Business, Edu, and Enterprise plans.
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“Real” is the right word.
Qwen-Image-3.0 shows that image generation is moving beyond aesthetics into real workflows: design, education, content, UI, e-commerce and scientific production.
Real workflows create real compute demand.
As AI agents begin to generate, revise and execute these tasks autonomously, compute usage becomes continuous and transactional.
The next layer is not just more GPUs.
It is a way for agents to access compute, pay for it, verify execution and settle value automatically.
That is what PAN is building:
the verifiable compute settlement layer for the agent economy.
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🎨 Meet Qwen-Image-3.0 — the third generation of our foundational image generation model.
If 1.0 was about "Precision," and 2.0 added "Variety, Completeness, Beauty & Authenticity," then 3.0 comes down to a single word: Real (实).
Three dimensions of "Real":
📰 Rich Content — prompts up to 4.5k tokens. One-pass generation of complex layouts: newspapers, storyboards, exam papers — even a 3×3 infographic grid or picture-in-picture-in-picture UIs.
🔬 Authentic Details — text legible down to 10px, full LaTeX paper pages, pores, hair strands & near-photographic skin texture.
🌏 Deep Knowledge — native rendering in 12 languages, 100+ art styles, realistic UIs (web / games / livestreams), plus world knowledge & live web retrieval.
Not just "good-looking" — genuinely useful. Image generation as a real productivity tool for design, content, education & e-commerce.
Go create 🏃🎨
💬Qwen Chat:
📝Blog:
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What is an AI Agent?
Not just a chatbot — it's a system that autonomously plans, calls tools, and completes multi-step tasks.
From checking the weather to executing trades, agents move AI from "answering" to "doing."
The 2026 mainstream trend is already here.
What's the first thing you'd want an agent to do for you?
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Kimi K3 (2.8T MoE) + NVIDIA LeRobot GR00T 1.7.
Massive open models for long-context reasoning meet physical AI platforms for humanoid robots.
More frontier inference + real-world deployment = exploding GPU/edge compute demand.
The race isn't just training anymore.
It's agents that think, see, and act.
#ComputeRace# #AIAgents#
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AI agents are not just a chatbot trend.
The real shift is happening inside enterprise workflows:
customer support, compliance, coding, research, operations.
Every task that moves from human-only to AI-assisted creates more inference demand.
That is where the next layer of compute growth comes from.
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Grok Build being open-sourced is another signal that AI infrastructure is moving closer to developers.
When AI tooling becomes easier to access, more teams can build, test, and deploy real workflows.
That raises an important question:
Will the next wave of GPU demand come from model training,
or from millions of AI workflows running in production?
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We've open-sourced Grok Build and have reset usage limits for all users.
Open sourcing Grok Build allows anyone to support making a reliable and robust harness. Check out our code, including the Git repo for the Grok Build CLI.
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