Crypto Werse

Calculating Circulating Supply of a Cryptocurrency

The **circulating supply** of a cryptocurrency refers to the number of coins or tokens that are currently available and circulating in the market. It excludes coins that are locked, reserved, or not yet released. To calculate the circulating supply, you generally:

- Start with the total number of coins that have been mined or created.
- Subtract any coins that are locked, reserved, or held by the project team and not available for public trading.
- The result is the number of coins actively circulating among investors and users.

This figure is important because it reflects the actual supply that can influence market dynamics.

Using Circulating Supply to Calculate Market Capitalization

**Market capitalization (market cap)** is a key metric used to assess the overall value of a cryptocurrency. It is calculated by multiplying the current price of one coin/token by the circulating supply:

```
Market Cap = Circulating Supply × Price per Coin
```

For example, if a coin has a circulating supply of 10 million coins and each coin is priced at \$5, the market cap would be:

```
10,000,000 × \$5 = \$50,000,000
```

This calculation helps investors understand the relative size and market value of a cryptocurrency compared to others.

- **Circulating supply** is the number of coins currently available in the market (excluding locked or reserved coins).
- **Market capitalization** is calculated by multiplying the circulating supply by the current price of the coin.
- This metric is widely used to rank and compare cryptocurrencies by their market value.

Here are top real-life utility NFT projects that are actually performing well and are worth watching in 2025, based on use case strength, community, and real-world adoption:

---

🔥 1. Nike’s .SWOOSH (Apparel & Loyalty)

Use Case: NFTs as digital sneakers, apparel, and loyalty rewards.

Why It Matters: Nike integrates NFTs into user accounts, offering exclusive drops and metaverse wearables.

Success: Over $200M in NFT revenue through RTFKT (acquired in 2021), expanding into wear-to-earn models.

Watch for: Their upcoming mobile integration and real-world redemption.

---

🎟️ 2. GET Protocol (Event Ticketing)

Use Case: NFT-based event tickets with fraud protection and traceability.

Why It Matters: Used by real-world artists, venues, and festivals.

Success: Millions of tickets sold; partnered with big European events.

Watch for: Expansion into U.S. and Asia and integration with mainstream ticketing apps.

---

🏠 3. Propy (Real Estate on Blockchain)

Use Case: Tokenizing real estate ownership using NFTs.

Why It Matters: NFTs represent property titles, enabling secure on-chain sales.

Success: Real properties in the U.S. have been sold via NFTs (first in Tampa, Florida).

Watch for: Global expansion and integration with legal frameworks.

---

🧑‍🎓 4. POAP (Proof of Attendance Protocol)

Use Case: NFTs as event badges, proof of participation, or achievements.

Why It Matters: Used by companies, conferences (ETHGlobal, DevCon), and communities.

Success: Widely adopted in crypto communities as social proof.

Watch for: Integration into education, corporate training, and loyalty systems.

---

👔 5. VeeFriends by Gary Vee (Business & IP Licensing)

Use Case: NFTs as intellectual property with real-world utility (access, events, licensing).

Why It Matters: Every NFT offers perks—conference tickets, mentorship, licensing rights.

Success: Hosted multi-year VeeCon events, partnered with Macy’s & Reebok.

Watch for: Expansion into kids’ media, TV shows, and IP mone

Building Trust in AI

As AI becomes more central to human activity, concerns about tampering, manipulation, and adversarial attacks only continue to grow. AI models, especially those handling critical decisions, must be resistant to attacks that would corrupt their outputs. Of course, we want AI applications to be safe. It’s not just about AI safety in the typical sense (i.e., fears of AI going rogue) but also about creating a trustless environment where the model itself is easily verifiable.

In a world where models proliferate, we're essentially living our lives guided by AI. As the number of models grows, so too does the potential for attacks where the integrity of the model is undermined. This is particularly worrisome in scenarios where the output of an AI model might not be what it seems.

By integrating ZK cryptography into AI, we can start building trust and accountability in these models now. Like an SSL certificate or security badge in your web browser, there will likely be a symbol for AI verifiability — one that guarantees the model you’re interacting with is what you expect.