اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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پستهای کانال
Claim 100 complimentary credits to access top-tier NVIDIA GPUs. Use them to run batch compute jobs on
@nvidia
H200s at $2.16/hr, pay-per-use with on-demand access. Learn more: https://docs.oncompute.ai/ocean-network-dashboard/claim-your-compy-tokens
https://x.com/ONcompute/status/2070430740047634880?s=20
| 2 | Memory is the difference between a tool and a mind.
An agent that forgets everything between jobs is starting from zero every time. Persistent storage on Ocean Network changes that. It learns once, writes to a bucket you own, and every job after picks up exactly where the last one stopped.
Now let them share. Open one bucket to many agents through an on-chain access list. One plans. Others execute. The memory is common, the work divides itself.
This is what an agent economy was waiting for. Agents that remember can specialize. Agents that share can coordinate. And all of it stays yours, on your terms.
Try it here: https://docs.oncompute.ai/persistent-storage/quickstart | 132 |
| 3 | @everyone
10k compute jobs later, and the only queue is the one that doesn't exist.
Turns out, when you skip the waitlist, the sales call, & the enterprise pricing, work just gets done.
And if you own GPUs, one of those next 10k jobs could run on your hardware: dashboard.oncompute.ai/run-node/setup
https://x.com/oncompute/status/2069741407208681651?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 150 |
| 4 | Forget provisioning.
What if your training job spun up its own isolated container, ran on your selected GPUs, and tore down clean the moment it finished?
Here is the flow:
Open the Ocean Network Dashboard and pick the GPUs that match your specs. Lock the environment you want and take it straight to your IDE.
Write your training job the way you already do, point it at the data, and dispatch. It runs sealed in its own container on the hardware you selected, the data never leaves where it lives, and you get the output back.
The moment it finishes, the container tears down clean.
Learn more about IDE-native GPU deployment: https://docs.oncompute.ai/ocean-orchestrator/using-ocean-orchestrator-with-ocean-dashboard | 175 |
| 5 | Stop waiting in line for compute.
The hardware shortage was real. The access shortage was a choice.
NVIDIA H200s are live on the Ocean Network Dashboard at $2.16/hr. You pay for what you run and nothing else
https://x.com/oceanprotocol/status/2069106670664290795?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 86 |
| 6 | Attention builders: $100 in complimentary tokens are waiting for you to claim on the Ocean Network Dashboard.
Claim them and deploy to high-quality @nvidia GPUs for your AI workloads.
Get started here: https://docs.oncompute.ai/ocean-network-dashboard/claim-your-compy-tokens
https://x.com/oncompute/status/2067879556111810886?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 223 |
| 7 | Before Airbnb, most travelers relied on hotels. Then Airbnb unlocked rooms that were already sitting empty in people's homes. The supply existed all along; it just wasn't accessible.
GPUs are in a similar position today. Large amounts of hardware sit idle across data centers, labs, and workstations. Decentralized compute turns that unused capacity into accessible supply.
Ocean Network built that marketplace for GPUs: https://dashboard.oncompute.ai/ | 199 |
| 8 | Decentralized compute sounds technical, but the idea is simple.
At any given moment, GPU capacity sits idle across research labs and workstations around the world.
Decentralized compute turns that unused capacity into a marketplace anyone can access.
You list your GPU, someone rents it. You need a GPU, someone has one. No single company owns the supply or decides who gets access.
The result is a larger pool of compute, better utilization of existing hardware, and more options for builders.
That's the model Ocean Network is built on.
https://x.com/oncompute/status/2067264755874554303?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 86 |
| 9 | Compute used to be personal. Then it became centralized. Today, some of the most powerful hardware in the world sits behind waitlists and platform gatekeepers.
Ocean Network changes that with on-demand compute access and escrow-secured payments.
Train your AI and ML workloads on pay-per-use @nvidia H200 GPUs starting at $2.16/hr: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oncompute/status/2066895321096225031?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 191 |
| 10 | What's actually blocking your AI and ML work right now?
https://x.com/oceanprotocol/status/2066459795880640904 | 179 |
| 11 | An AI agent without memory isn't autonomous. It's a very expensive goldfish.
Persistent storage on Ocean Network gives agents a memory that lasts. An agent stores what it learns once in a bucket you own and control, and any future job picks up exactly where the last one left off.
It gets stronger when agents work together. Share a bucket through an on-chain access list, and many agents can read and write the same memory. One plans, others execute, and results come back to one place.
That's the missing layer for an agent economy. Agents that remember can specialize. Agents that share memory can coordinate. And the memory is yours, on your terms.
Run it on the most affordable NVIDIA H200 anywhere, $2.16/hr on the Ocean Network Dashboard: https://dashboard.oncompute.ai/run-job/environments
https://x.com/ONcompute/status/2065431928585527431?s=20 | 117 |
| 12 | 👀 Something new is coming to @lunor_ai ...
News shapes how the world understands conflict, alliances, and global power. The next quest will ask you to look closely at how international actors are portrayed in war and conflict coverage: who supports whom, who criticizes whom, and where the stance is unclear.
No coding required. Just careful reading, sharp judgment, and attention to context.
🗓️ Launching: June 9 💰 Reward Pool: 2,500 USDC
Get ready for MediaLens.
https://x.com/oceanprotocol/status/2064247373073645621?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 251 |
| 13 | $2.16/hr for an H200. The asterisk just says "that's the price."
Funny thing about that asterisk. Everywhere else, it hides a minimum, a contract, a waitlist. Here it's just pay-per-use, and then you get on with your day.
Launch your first job: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oncompute/status/2063912271919608173?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 277 |
| 14 | Agentic AI changes what matters in GPU infrastructure. It's not just FLOPs anymore.
Long-context reasoning, large KV caches, retrieval pipelines, and concurrent tool calls make memory capacity a first-class constraint.
The NVIDIA H200 was built for workloads like these. Ocean Network provides on-demand H200 access from $2.16/hr on a pay-per-use basis.
Run agents on infrastructure built for them: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oncompute/status/2062224795979149545 | 295 |
| 15 | Bring the idea. We'll handle the universe it runs on.
Browse on-demand GPUs from the Ocean Network Dashboard. Select the hardware your workload needs. Pull that environment directly into your IDE with Ocean Orchestrator.
Run inference, fine-tuning, embeddings, or agent workloads without provisioning infrastructure or managing servers. Containerized compute jobs execute on a remote chosen node and return results directly to your workflow.
The dashboard finds the compute. Ocean Orchestrator puts it to work.
So, what will you build?
https://x.com/ONcompute/status/2061846805185245335?s=20 | 139 |
| 16 | $2.16 won’t buy you a latte.
$2.16 won’t get you a parking spot downtown.
But $2.16 will get you an NVIDIA H200 on Ocean Network for 1 hour.
In 10 hours, you could fine-tune an open model, run large-scale inference workloads, process massive embedding pipelines, or train experiments back to back without dealing with infrastructure overhead.
With 141GB of HBM3e memory and 4.8TB/s bandwidth, the H200 handles workloads that are still inaccessible to most developers, and 10 hours would cost roughly $21.60.
Most people still assume this class of compute is reserved for hyperscalers and well-funded AI labs.
Access it here: https://dashboard.oncompute.ai/run-job/environments
https://x.com/ONcompute/status/2060400783816892724?s=20 | 177 |
| 17 | NVIDIA H200s are becoming one of the best GPUs for multi-agent AI workloads.
Agent systems create massive KV cache pressure, parallel reasoning demand, and long-context memory strain across multiple active inference streams.
That's exactly where H200s shine, with 141GB HBM3e memory and massive memory bandwidth built for high-concurrency AI workloads.
Access them through the Ocean Network Dashboard from just $2.16/hr on a pay-per-use basis: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oceanprotocol/status/2060013979351564567?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 265 |
| 18 | Most AI teams obsess over model quality while ignoring the biggest cost in the stack: compute utilization.
An NVIDIA H200 is $2.16 per hour on Ocean Network. The same compute can cost several times more on traditional cloud infrastructure, depending on the provider and availability.
One unit of H200 compute is enough to:
Fine-tune a model with QLoRA
Generate synthetic training data
Run large-scale inference
Plus, Ocean Network charges you only for what you use, with no vendor lock-in, and payments are made through escrow. Explore available environments: dashboard.oncompute.ai/run-job/enviro
https://x.com/ONcompute/status/2058910465099497595?s=20 | 142 |
| 19 | Many compute providers bill by the hour. Your workloads don't run by the hour.
With Ocean Network, you pay for execution time and not a cent more, even on the NVIDIA H200.
Access some of the most affordable GPUs on the market, configured exactly to what your experiment needs: GPU, CPU, RAM, disk space, and runtime.
Run a 22-minute fine-tune and pay for 22 minutes.
That's it: dashboard.oncompute.ai
https://x.com/oncompute/status/2057474746799792588?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 144 |
| 20 | The majority of LoRA, QLoRA, and fine-tuning practitioners are wasting time switching between their IDE and cloud consoles just to provision a GPU.
Ocean Orchestrator brings IDE-native GPU compute orchestration into VS Code, Cursor, Antigravity, and Windsurf. Select your exact specs on the Ocean Network Dashboard, including NVIDIA H200 on-demand access at a fraction of hyperscaler rates, and bring that instance into your IDE on a pay-per-use basis.
Your logs, outputs, and results land directly in your workspace. You never leave the IDE.
Install the Ocean Orchestrator extension and start your next fine-tune in minutes: https://open-vsx.org/extension/OceanProtocol/ocean-protocol-vscode-extension
https://x.com/ONcompute/status/2057070583641248127?s=20 | 129 |
