Depin: decentralized physical infrastructure networks
While Depin projects, in theory, try to provide real utility to cryptography, there are few that really solve real -life problems, have a sensitive commercial model capable of interrupting existing companies and cannot be easily falsified. Most are simply solutions in search of a problem. A remarkable exception is a flight tracking network called Wingbits. Because? Because it addresses a web2 problem solving it with web 3 incentives. For anyone who has tracked a flight like BA117 from London to New York, we may have used websites such as FlightTAware or Flightradar.
Figure 1: Winged Baby Flight Monitoring Map
Source: Wingbits – Transforming flight tracking.
Flight monitoring companies generate millions in revenues selling flight data to aviation companies and buyers as financial analysts that monitor private aircraft movements for mergers and acquisitions. These companies also obtain admission of ads and subscriptions on their platforms. However, its capital expenditure does not include significant infrastructure and hardware expenses. This is because aviation surveillance technology, called ADS-B receptors, is a hardware that requires raspberry antennas and PIs, bought and configured by aviation enthusiasts. These enthusiasts expect little in return, they often receive only a free subscription to their favorite flight monitoring platform.
The main problem is that enthusiasts do not encourage to maximize data quality for these networks. Without marginal incentives, ADS-B receptors are often poorly located, for example, in the corners of the room or in excess of supply in densely populated urban areas, which leads to weak coverage in rural regions.
Figure 2: (LHS) Tradition
Source: Wingbits – Transforming flight tracking.
Wingbits is revolutionizing the monitoring of flights encouraging enthusiasts to establish stations strategically, according to altitude, while using a system similar to the Haxagonal Haxagonal Spatial Index of Uber. This approach guarantees optimized coverage, higher quality data and most importantly, fair rewards for taxpayers to the network. They achieved a coverage of 75% of the largest networks with only 1/11 the number of wing stations. It is anticipated that this high level of efficiency, combined with an expected deployment of more than 4,000 stations, will exceed the traditional flight monitoring networks by a significant margin, which offers better quality data for the end of customers.
The next conversation at the family dinner that explains this concept will be easy, since we can now point out a real world use case, driven by cryptocurrencies, which everyday people can understand.
Crypto x ai
Similar to market cycles, demand for calculation experiences of peaks and channels. GPUs can be expensive, and supply limitations make them even more. Unlocking inactive calculation in consumption devices is not a new concept, but solving the synchronization challenge in multiple devices is. Exo Labs is a pioneering project that makes advances in edge computing, allowing users to execute models on daily devices of degree of consumption, such as domestic macbooks. This means that confidential data remains under their control, reducing the risks associated with cloud -based storage or processing.
Figure 3: A 9 -layer model is divided into 3 fragments, each that runs into a separate device
Source: Transparent reference points – 12 days of EXO, Exo Labs.
Exo Labs has developed a new software infrastructure called parallel inference of Pipeline, which allows a large language model (LLM) to be divided into “fragments”, allowing different devices to execute separate parts of the model while remaining connected through the same network. This approach offers several advantages, such as reduced latency, greater security, profitability and most importantly, privacy benefits.
Explore privacy also reveals BAGEL AI, a project that has developed Zklora (low -knowledge zero knowledge adaptation), an approach that presents the privacy for the adjustment of LLMS. This innovation allows the creation of specialized models for industries such as legal services, medical care and finance, which allows to use confidential data for reinforcement learning without risking confidential information leaks.
While the preservation of privacy is a hot topic, a major challenge for most LLM is the hallucination problem, an Ai response that contains false or misleading information presented as a fact. A portfolio manager once told me: “Wisdom lies in synthesizing competitive views to discover the nuanced truth between two ends.” Blocksense is a project that has developed a patented approach called Zkschellingcoin Consensus. This method aims to overlap the subjective truths of multiple sources, for example, different LLM, to reach a single common truth. For example, imagine executing the same consultation in Chatgpt, Claude, Grok and Llama. If a model provides an incorrect exit, it is statistically unlikely that the four models generate the same false result compared to each other.
Figure 4: General description of the Zkschellingcoin consensus
Source: Network BlockSense: The Rollup ZK for programmable oracles.
The consensus of Zkschellingcoin could also be applied to add verifiability to the inference of AI. For example, how can we confirm that an AI agent correctly connected the USDC in the highest performance at the time of execution? Trust in AI would be significantly strengthened with an additional verification layer. If we can solve this without compromising cost or latency, it could lead to great advance in real world use.
The trip of exaggeration to reality in Depin and AI shows that genuine innovation lies in solving real world problems with practical and efficient solutions. Projects such as Bobits and Exo Labs demonstrate how Blockchain and AI can create a significant impact, either revolutionizing flights with strategic incentives or unlocking the power of consumption devices for safe and profitable computer science. With advances such as Zklora for the AI and Zkschellingcoin of preservation of privacy for verifiable truth, these emerging technologies are prepared to address critical challenges, racing the way for a more decentralized, efficient and verified future.
