Retry-safe is a property of the key, not the number of attempts.

One of the biggest barriers to ZK adoption isn’t the technology itself. It’s the cost and complexity of generating proofs. That’s the problem @fermah_xyz is working to solve. 👇 ZK proof generation can demand serious computing power, specialized hardware, and infrastructure that developers don’t always want to manage themselves. Fermah approaches this with a Universal Proof Market that connects proof requests with available computing resources. Here’s why it matters: → Developers can request proofs without managing all the proving infrastructure themselves. → CPUs, GPUs, and FPGAs can contribute computing power. → Better resource coordination can help make proof generation more accessible. → A shared proving marketplace can support the growing demand for ZK applications. The goal is simple: make proof generation easier to access so builders can focus more on creating products and less on infrastructure. ZK needs more than innovation. It needs proving infrastructure that can scale with adoption. That’s where Fermah comes in.

A public attestation trail gives every capture more accountability.

you can recompute its hash and check whether it matches the public attestation.

Reproducible results across different hardware will be the real proof

Defaults are a guess, /slots is the receipt. Measure before you trust the flag. xitter.cf/automater_ai/status/21…

What if AI didn’t depend on a handful of tech giants? Discover how Acurast turns smartphones into a decentralized compute network for more private, verifiable AI. Explore the future of Crypto AI and how to get involved.

One of the weirdest realizations is that physical work can become a digital asset without tokenizing the robot itself I spent time looking through what @konnex_world is building and the important part is not another robotics demo It is the work A drone completes an inspection A robot moves inventory An AI model provides the control policy Independent validators verify the outcome The machine gets paid in stablecoins The entire process becomes measurable economic activity This matters because robots have already proven they can work. Konnex points to more than 65k hours of real warehouse work and over 100k totes moved. The next problem is figuring out how machines prove what they did, negotiate new tasks, and receive payment without requiring a company to manually coordinate every step That is where Proof of Physical Work gets interesting The Konnex testnet currently has 258 miners, 7 validators, and 3 active SubNets. KONNEX Quests also gives users a way to participate in verified ecosystem activity while earning points connected to the upcoming airdrop The bigger idea is simple AI models compete to control machines Machines perform useful physical work Validators establish trust Verified outcomes trigger settlement $KNX helps secure the economy around it Not your proof, not your payment Everyone is focused on whether robots will take jobs. I am more interested in a different question What happens when robots can find work, prove they completed it, and get paid without waiting for a human operator? The machine economy is no longer only a prediction. The infrastructure is starting to show up #KonnexWorld #KNX #Robotics #PhysicalAI #DePIN

The most overlooked shift in demand-driven networks isn't the data architecture. It's the profile of the node operator. Traditional DePIN often rewards hardware uptime. You deploy a device, connect it, and wait for emissions. @vangrid_io introduces a different supply-side behavior. When demand is expressed through USDC bounties tied to specific locations, simply keeping hardware online is not enough. The sensor has to reach where the demand exists. That changes the operator's job. Instead of optimizing only for uptime, operators start optimizing for location, distance, timing and availability. The network stops looking like a static server farm. It starts looking more like a decentralized logistics fleet. That's a significant behavioral shift. Passive yield requires capital. Active spatial capture requires capital plus coordination. And that creates the real economic test: Are localized spatial data bounties valuable enough to compensate operators for the additional operational friction? Because once physical movement becomes part of the supply side, the network isn't just coordinating data anymore. It's coordinating labor, hardware and location.

Most data platforms hand you a payout and nothing else, no way to see what you actually contributed. Perceptron's Node Dashboard shows your exact uptime and bandwidth in real time. You can watch your own node prove its worth to the network.

🟠 Lido · Ethereum Lido Curated Module v2 (CuratedModule) set a target validator limit on node operator 6. • Target: 1 • Mode: off • Executed by governance proposal 15 • +2 related changes on 3 contracts in the same transaction gliese.io/contracts/ethereum…

Realworld data isn’t the bottleneck, it’s trust. Who verifies the sensor isn’t just feeding the AI whatever it wants to hear?

🧌 Mostro Node v0.20.1 is out! ⭐ Reputation export: your reputation can now be exported from a Mostro node, so it moves with you from one Mostro to another. It builds on the import that arrived in v0.20.0. 🩺 Fewer false alarms in node health checks: relay, payout and LND sync warnings now wait longer before firing. 🧾 The payouts check now ignores a single bad invoice or stale orders. 📚 New operator guide for reputation in the docs. Update recommended for node operators.

One thing I want to see from decentralized compute is proof that the work actually delivers. It's easy to talk about available hardware and network capacity. But what happens when a job is submitted? Did it produce a useful result? How efficiently was it completed? Can the outcome be verified? These are the questions that matter when moving from an interesting concept to infrastructure people can rely on. That's what makes the idea behind @quipnetwork worth following. The real progress isn't just running more jobs. It's making the results count. 🦋 $QUIP #QuipNetwork #PoUW #DecentralizedCompute

Self-attestation is the only monitoring that survives an adversarial operator.

Legible rewards are how you keep an operator past the first payout. xitter.cf/PerceptronNTWK/status/…

Acurast: Turning the old phone in your drawer into a computing resource When discussing AI infrastructure, most attention is focused on building increasingly massive data centers. Acurast takes a different approach: leveraging hardware that people already own such as that old phone sitting idle in a drawer. @Acurast What kind of tasks is it suited for? Training a cutting-edge model is a vastly different undertaking from classifying emails, filtering messages, or running a small decision-making model. While phones have limitations, there is still a market for small, repetitive tasks that do not require an entire cluster of GPUs. This is where the intersection of AI and DePIN (Decentralized Physical Infrastructure Networks) becomes compelling. Try it out yourself Laya demos showcase decision-making models running on the CPUs of Android phones handling tasks like email categorization, chat moderation, and screening for prompt injection. Acurast notes that each decision takes approximately 0.2–1 second. You can test it at laya.acurast.com and see which device is processing your request. The development team also acknowledges certain limitations: classification errors can occur, and queues may grow longer when the volume of requests exceeds a phone’s processing capacity. The trust challenge If a phone belongs to someone else, how can you be sure that the device executing your task is legitimate? Acurast employs hardware-based attestation (Google Key Attestation and Apple App Attest) and monitors for revoked credentials, removing compromised or invalid devices from the pool of those eligible to receive tasks and earn rewards. @Acurast Verification is therefore continuous, rather than occurring only when a device first joins the network. That said, the security model remains dependent on the hardware manufacturers and platform providers. The role of ACU ACU serves several defined functions: covering network fees, incentivizing the provision of computing power, staking, and governance. The most notable mechanism is "Staked Compute": providers lock up tokens to commit a specific amount of computing power, while ACU holders who lack hardware can delegate their tokens to these providers. Benchmarks and "heartbeat" signals monitor capacity and availability, ensuring providers have an economic incentive to honor their commitments. It is worth noting that the tokenomics include an annual inflation rate of 5%, with 70% of that allocated to the Staked Compute Pool. Therefore, staking rewards should not be confused with revenue generated from customers paying for computing services. While incentives can help build supply, it is the return of actual customers that truly validates the value of that supply. Key metrics to track: •Number of paid tasks and the rate of returning developers •Cost per successfully completed task •Stability under sustained load •Growth in usage relative to token-based incentives These metrics offer a much more meaningful picture than simply counting the number of registered devices. How to get started? The "Cloud Rebellion" program allows you to explore the ecosystem by completing tasks, deploying applications, and onboarding a supported device to the network. @Acurast You earn "MIST" points which are distinct from ACU tokens. My advice: try out the demo and use a spare device before making any further investments.