DePin: decentralized material infrastructure networks
Although Depin Projects, in theory, try to provide a real benefit for encryption, there is a little that really solves real life problems, and has a reasonable business model capable of disrupting current companies and cannot be easily mitigated. Most of them are simply solutions to search for a problem. One of the prominent exceptions is a flying network called Wingbits. Why? Because it addresses the web2 problem by solving it with web3 incentives. For anyone who tracked a trip like the BA117 from London to New York, you may have used websites like Flightaware or Flightradar.
Figure 1: Airline tracking map in wingbits
Source: wingbits – airline tracking.
Aviation tracking companies generate millions of revenues by selling airline data to airlines and buyers such as financial analysts who monitor private aircraft movements for integration and acquisition. These companies also gain revenues from ads and subscriptions on their platforms. However, its capital expenses do not include a large infrastructure and devices. This is because aviation monitoring technology, called ADS-B, are devices that require antennas and raspberry pi, purchased and composed by flying lovers. These enthusiasts expect a little in return, and often receive just a free subscription to their favorite flying platform.
The main problem is that enthusiasts are not motivated to increase the quality of data for these networks. Without marginal incentives, ADS-B reception devices are often placed badly-for example, in the corners of the hall room or an increase in populated urban areas, which leads to poor coverage in rural areas.
Figure 2(LHS) Traditional ADS-B reception
Source: wingbits – airline tracking.
Wingbits revolutionized aviation tracking by motivating enthusiasts to create stations strategically, based on height, with a system similar to the Uber Hexagonal Sharkical index. This approach guarantees improved coverage, high -quality data, and most importantly, fair rewards for the network shareholders. They have covered 75 % of the largest networks with only 1/11, the number of wingbits stations. This high level of efficiency, along with an expected group of more than 4000 stations, is expected to exceed traditional flying networks with a large margin, providing better quality data for customers.
The next family dinner conversation that explains this concept will easily come as we can now refer to the state of the real world use, driven by encryption incentives, which ordinary people can understand.
Crypto X AI
Similar to market courses, the demand for peak and pelvic calculation experiences. Graphics processing units can be expensive, and supply restrictions make them more. Opening the lethargy account on consumer devices is not a new concept, but solving the synchronization challenge across multiple devices is. EXO LABS is a pioneering project that achieves the edge computing, which enables users to run models on daily consumer devices, such as home Macbooks. This means that sensitive data remains under your control, which reduces the risks associated with storage or treatment -based treatment.
Figure 3The 9 -layer model is divided into 3 fragments, each of which works on a separate device
Source: transparent standards – 12 days from Exo, Exo Labs.
EXO LABS has developed a new infrastructure for programs called parallel inference pipelines, which enables the Great Language model (LLM) to divide “fragments”, allowing different devices to operate separate parts of the model while staying connected across the network itself. This approach provides different advantages such as decreased cumin, improving security, cost efficiency, and most importantly, the benefits of privacy.
Privacy exploration also reveals Bagel Ai, a ZKLora project (low -ranking zero knowledge), which is the privacy conservation approach to hitting LLMS. This innovation allows the creation of specialized models for industries such as legal services, healthcare and financing, allowing sensitive data to learn to learn to enhance without risking secret information leaks.
Although conservation of privacy is a hot theme, the greatest challenge for most LLMS is the problem of hallucinations, a response created by artificial intelligence that contains wrong or misleading information provided as a fact. A wallet manager once told me: “The wisdom lies in the synthesis of competing views to reveal the exact truth between two parties.” Blocksense is a special approach project called ZkschellengeCoin. This method aims to overcome self -facts from multiple sources – for example, different LLMS – to reach one common truth. For example, imagine the operation of the query itself via ChatGPT, Claude, Grok and Llama. If one of the models provides incorrect output, it is unlikely that all four models will create the same wrong result when compared to each other.
Figure 4: Overview of Zkschellingcoin consensus
Source: Blocksense – ZK Rollup for Urukles.
Zkschellingcoin consensus can also be applied to add artificial intelligence deduction. For example, how can we emphasize that the USDC Dam properly in the brackets of the return at the time of implementation? Confidence in artificial intelligence will be dramatically with the additional verification layer. If we can solve this without compromising costs or cumin, this may lead to a great penetration in cases of use in the real world.
The journey from noise to reality in DePin and AI shows that real innovation lies in solving problems in the real world with practical and effective solutions. Projects such as Wingbits and EXO Labs prove how Blockchain and AI can create a meaningful effect by revolutionizing aviation tracking with strategic incentives or opening the power of consumer devices for safe and cost-effective computing. With progress like Zklora to preserve the privacy of AI and Zkschellingcoin for the fact that can be verified, these emerging technologies are ready to face critical challenges, which paves the way for a more central, effective and confident future.
