A review of several popular projects in the Crypto+AI sector over the past month reveals three significant trends: 1) Projects are adopting more pragmatic technical paths, focusing on performance data rather than just conceptual packaging; 2) Niche vertical scenarios are becoming the focus of expansion, with generalized AI giving way to specialized AI; 3) Capital is placing greater emphasis on validated business models, with projects that have cash flow clearly favored; Attached: Project summaries, highlight analyses, and personal comments: 1. @yupp_ai Project Summary: A decentralized AI model evaluation platform that completed a $33 million seed round in June, led by a16z, with participation from Jeff Dean. Highlight Analysis: It applies the advantages of human subjective judgment to the shortcomings of AI evaluation. By crowdsourcing evaluations for over 500 large models, user feedback can be redeemed for cash (1000 points = $1), attracting companies like OpenAI to purchase data, resulting in real cash flow. Personal Comment: This project has a relatively clear business model and is not purely a burn-money model. However, preventing fraudulent orders is a significant challenge, and the anti-sybil attack algorithm needs continuous optimization. The $33 million financing scale indicates that capital is clearly more focused on projects with monetization validation. 2. @Gradient_HQ Project Summary: A decentralized AI computing network that completed a $10 million seed round in June, led by Pantera Capital and Multicoin Capital. Highlight Analysis: With the Sentry Nodes browser plugin, it has gained some market consensus in the Solana DePIN field. Team members come from Helium, and they have launched the Lattica data transmission protocol and Parallax inference engine, making substantial explorations in edge computing and data verifiability, reducing latency by 40% and supporting heterogeneous device access. Personal Comment: The direction is correct, aligning well with the trend of "localization" in AI. However, when handling complex tasks, efficiency needs to be compared with centralized platforms, and the stability of edge nodes remains a concern. Nevertheless, edge computing is a new demand emerging from web2AI and is also a distributed framework advantage of web3AI. I am optimistic about advancing practical performance with specific products. 3. @PublicAI_ Project Summary: A decentralized AI data infrastructure platform that incentivizes global users to contribute multi-domain data (medical, autonomous driving, voice, etc.), with cumulative revenue exceeding $14 million and a network of over a million data contributors. Highlight Analysis: Technically integrates ZK verification and BFT consensus algorithms to ensure data quality, and uses Amazon Nitro Enclaves privacy computing technology to meet compliance requirements. Interestingly, it has launched the HeadCap brainwave collection device, expanding from software to hardware. The economic model is also well-designed, allowing users to earn $16 + 500,000 points for 10 hours of voice annotation, and reducing the cost of enterprise data service subscriptions by 45%. Personal Comment: I feel the project's greatest value lies in addressing the real demand for AI data annotation, especially in fields like medical and autonomous driving, which have high data quality and compliance requirements. However, a 20% error rate is still higher than the 10% of traditional platforms, and data quality fluctuations are an ongoing issue that needs to be resolved. The brain-computer interface direction has considerable imaginative potential, but the execution difficulty is also significant. 4. @sparkchainai Project Summary: A distributed computing network on the Solana chain that completed $10.8 million in financing in June, led by OakStone Ventures. Highlight Analysis: It aggregates idle GPU resources through dynamic sharding technology, supporting large model inference like Llama3-405B at a cost 40% lower than AWS. The design of tokenized data trading is quite interesting, directly turning computing power contributors into stakeholders and encouraging more people to participate in the network. Personal Comment: This is a typical "aggregating idle resources" model, which logically makes sense. However, a 15% cross-chain validation error rate is indeed a bit high, and technical stability needs further refinement. Nevertheless, it has advantages in scenarios like 3D rendering, which do not require real-time performance, but the key is whether the error rate can be reduced; otherwise, even the best business model will be dragged down by technical issues. 5. @olaxbt_terminal Project Summary: An AI-driven cryptocurrency high-frequency trading platform that completed a $3.38 million seed round in June, led by @ambergroup_io. Highlight Analysis: The MCP technology can dynamically optimize trading paths, reducing slippage, with a measured efficiency improvement of 30%. It aligns with the #AgentFi trend, finding a niche in the relatively blank field of DeFi quantitative trading, thus filling a market demand. Personal Comment: The direction is correct; DeFi indeed needs smarter trading tools. However, high-frequency trading has extremely high requirements for latency and accuracy, and the real-time synergy between AI predictions and on-chain execution still needs validation. Additionally, MEV attacks pose a significant risk, and technical protective measures must keep pace. Note: For more new projects in the AI+Crypto sector, everyone can add to the comments section, and I will filter projects with research value to follow up and share. Thank you.