A new week, another opportunity to approach the market with more clarity. Here's how I'd use @nodiens to stay on top of the market this week: Discover: Track prices, top gainers, sentiment changes, and market intelligence across different timeframes. Watchlist: Keep the assets I'm interested in close so I can monitor their movements. Market Intelligence: Look beyond price action and pay attention to the signals that could help me understand what's changing. Predictions: Explore potential market outcomes while remembering that no prediction is guaranteed. My approach is simple: gather information, compare signals, manage risk, and make decisions based on evidence rather than FOMO. Nodiens brings useful market insights into one place. The edge comes from how consistently I put them to work. Join me on Nodiens app.nodiens.com/invite/?refe…

Let’s break down two technologies behind what @primus_labs is building: zkTLS and zkFHE. 1. What is zkTLS? zkTLS helps verify data obtained from web services while reducing the need to reveal unnecessary information. Think of proving a piece of information from a website is authentic without simply asking people to trust a screenshot or your word. The goal: bring more verifiable Web2 data into Web3. 2. What is zkFHE? FHE stands for Fully Homomorphic Encryption. It allows computations to be performed directly on encrypted data, without decrypting it first. zkFHE brings verifiability into this approach, helping establish that computations on sensitive data were performed correctly. In simple terms, the data can remain encrypted while being processed. 3. What’s the difference? 🔹 zkTLS: Focuses on verifying information from web interactions. 🔹 zkFHE: Focuses on computing over encrypted information while supporting verification. One helps answer, “Can this data be trusted?” The other helps answer, “Can we use sensitive data without exposing it?” 4. Why combine them? Imagine an onchain financial application that needs to verify information from a traditional financial service and use that information without unnecessarily exposing sensitive details. That’s where these technologies become interesting: verified data can be brought into Web3, while encrypted computation can help protect the information being used. The exact privacy guarantees depend on the implementation, but the underlying idea is powerful. My takeaway: Privacy shouldn’t mean data is impossible to verify, and verification shouldn’t require exposing everything. That’s why I’m exploring the work @primus_labs is doing at the intersection of cryptography, verification, and confidential computation. Explore Primus: s.kaito.ai/Ru2KZkh