Cofinex (CNX)

If you're running a $CNX node, your GPU isn't what determines your earnings.

Your QoS score does.

And the new QoS Diagnostics tool finally shows you why your score is moving instead of leaving you guessing.

QoS (Quality of Service) is the key metric the network uses when assigning AI workloads.

A higher score means:

• More tasks assigned to your node
• Better earning potential
• Greater network trust

It's calculated using two components:

Long-term Performance tracks your rolling performance across roughly the last 50 validation tasks. Faster, more reliable execution leads to a higher score.

Short-term Reliability (H) measures your current node health. It starts at 1.0, drops sharply after timeouts, and gradually recovers through successful task completion.

If H falls below 0.1, your node won't receive new tasks until it recovers.

Until now, operators could see their QoS changing but had no way to understand why.

Was it:

• A network issue?
• A timeout?
• Thermal throttling?
• Hardware instability?

Now you can see exactly when your score changed and what likely caused it.

That's a much bigger upgrade than it sounds.

It lets operators actually optimize their setups instead of troubleshooting in the dark.

It also improves the network itself. Reliable nodes receive more work.

Temporary issues recover automatically.

Poor-performing nodes gradually lose priority instead of hurting overall service quality.

That's exactly how a decentralized AI network should evolve.

Running a Crynux node is already one of the easiest ways to contribute spare GPU compute to decentralized AI.

But hardware alone isn't enough anymore.

Consistency wins.

Small improvements to cooling, internet stability, or system performance could translate directly into more tasks and better rewards.

This is the kind of infrastructure improvement that helps permissionless AI networks compete with centralized cloud providers.

If you're running a node, it's probably worth checking your QoS history today.

You might find there's more room for optimization than you expected.

Most decentralized AI projects are still competing for data center GPUs.

@crynuxio is betting the future of AI compute lives in millions of consumer devices.

And that changes everything.

Crynux is building a decentralized AI infrastructure network powered by edge devices:

• Home PCs
• Gaming GPUs
• Apple Silicon Macs
• Potentially mobile devices in the future

The vision is simple:

Turn spare compute sitting idle around the world into a global permissionless AI cloud.

No centralized providers. No gatekeepers. No expensive infrastructure requirements.

Anyone can contribute compute. Anyone can access AI services.

The network's Lithium mainnet, launched in June 2026, brings that vision into production.

For node operators, the process is straightforward:

• Download the node app.
• Connect compatible hardware.
• Receive AI workloads.
• Earn $CNX for contributing compute.

Adoption will ultimately determine success.

But if decentralized AI is going to scale globally, networks that can efficiently coordinate millions of edge devices may end up playing a major role.

Worth watching.

The decentralized AI compute race already has giants. That's exactly why @crynuxio is worth watching.

Look at the field first.

> Akash has been building decentralized cloud since 2018. Render since 2017.

> Bittensor runs a multi-billion-dollar marketplace for machine intelligence.

> Aethir coordinates hundreds of thousands of enterprise GPUs across dozens of countries.

You don't get four serious players, billions in combined market cap, and Nvidia-tier hardware demand around a problem that isn't real. The incumbents prove the thesis: a lot of people want AI compute they don't have to rent from Amazon, Microsoft or Google.

But here's the part worth sitting with ⬇️

They've all drifted toward the same shape. Aggregate data-center GPUs. Rent out the box. Serve the enterprise. From Akash to https://t.co/iV6fo9aCZ3 to Aethir, it's largely one playbook with different tickers.

The biggest pool of idle compute on earth isn't in data centers. It's in living rooms, on laptops, inside the Apple Silicon Mac sitting on your desk right now. Very few networks are built to tap that. And even fewer verify whether a stranger's machine actually ran your AI task honestly, or just took the payment.

That empty space on the map is what Crynux is aimed at.

→ Edge-native by design. Instead of chasing H100 clusters, Crynux turns consumer GPUs, home computers and Macs into the network. Different architecture, not a different logo.
→ It verifies the work. Its vssML consensus secretly spot-checks roughly 10% of tasks and slashes anyone cutting corners - trust without paying to run every job three times.
→ Fine-tuning, not just rental. Beyond inference, the network does model fine-tuning natively, through an OpenAI-compatible API that drops into existing agent and LLM stacks.

The mainnet (Lithium Network) went live June 17, with delegated staking already on, so you can back node operators even without hardware.

The honest part: it's early. Small. Unproven against networks with years and hundreds of thousands of GPUs on them.

But early plus the right gap is the whole setup for asymmetric upside. A mature project crowded into a saturated lane has no story like this. A young one pointed at open territory does.

High risk, high reward. Do your own research but put this one on the list. 🔝