Crypto 𐤊id | 𝔽rAI

Guide for stacking @HeyElsaAI points Backed by @coinbase 🚀 Funding 3 million$ 💵 Follow these simple steps to grab your points 👇

@arp_0x @Polymarket Me too but srh won 😯

@Cryptokid45 @Polymarket I bought csk

$CSK 🟨 Or $SRH 🟧 Who are you buying on @Polymarket today ❓

@tetherwallet @btc @tether @btc cryptokid45@tether.me

The @btc faucet is open again! Claim Your Free Sats ⚡🚰 Step 1: Get your @ tether.me handle (Download Tether Wallet). Step 2: Post tagging @btc + your @tether.me handle. Step 3: Receive free Sats instantly! Follow @btc & @tetherwallet

#Crypto is shifting! The first decade was all about Layer 1, consensus, and infrastructure. Now? The real action is moving to the application layer. $KAS #kaspa 👀

Hey $KAS I am preparing to do an interview with @hashdag what questions should I ask?

@realvijayk @hashdag Kaspa army really showed up here. Followed @hashdag ✅

Your mobile phone runs with ARM chips. Your mobile wallets will run with Kaspa. My followup illusion is building PoW with 100 blocks per second. Who’s coming?

עשרה קבין של יופי ירדו לעולם Kaspa Crescendo <3

@rushicrypto @KaspaSilver

Trying to go deeper on Kaspa ( $KAS ). Who are the must-follow accounts to really understand the tech, vision, and ecosystem? Drop the best $KAS builders, researchers, or OGs 👇

Why Transaction Finality Is Actually A Physics Problem The speed of light is 300,000 kilometers per second. Earth's circumference is 40,000 kilometers. This means information traveling at light speed takes approximately 133 milliseconds to circle the globe. This physical limit constrains all blockchains. A miner in Sydney finds a block. A miner in New York finds a block at the same instant. Neither knows about the other's block because the information hasn't arrived yet. Who wins? Bitcoin's solution: give blocks tons of time to propagate. Ten-minute block intervals mean a Sydney block reaches New York with 9 minutes and 50 seconds to spare. Conflicts are rare. When they happen, the longest chain rule resolves them. Simple, secure, slow. The problem with speed. If Bitcoin tried 1-second blocks, geography becomes chaos. Sydney and New York would constantly mine competing blocks before hearing about each other. The orphan rate (wasted blocks) skyrockets. Security degrades because attackers exploit the confusion. Single-chain blockchains cannot escape this. They must choose: long block intervals (safe but slow) or short intervals (fast but unstable). The physics don't negotiate. Kaspa's architectural solution: embrace the conflicts instead of preventing them. At 10 blocks per second with 100ms intervals, conflicts are guaranteed. Miners on opposite sides of Earth will mine blocks before hearing about each other. That's fine. The blockDAG includes both blocks. GHOSTDAG's ordering algorithm determines sequence after the fact. You're not fighting physics. You're designing around it. The blocks still propagate at light speed. But the system doesn't break when parallel blocks appear. It expects them and handles them algorithmically. This is why blockDAG can achieve 100ms block times while single chains need 10 minutes. Different architecture, same physics, different constraint. Next in The Kaspa Blueprint Series: why blockchain fees are actually paying for digital real estate.

Kaspa feels like the continuation of #Bitcoin, explains James Lee. @KaspaFinance founder @cryptojameslee breaks down the tech during @token2049 — blockDAG architecture, proof-of-work consensus, and 10 blocks per second throughput. 🔽 x.com/BitcoinNews/st…

📣New Spot Listing Alert: $KAS Kaspa @kaspaunchained @Kaspa_KEF Kaspa is a decentralized and fully scalable Layer-1, based on the GHOSTDAG protocol. 🪙Deposits Opened, 📈Trading Starts Soon! htx.com.lv/en-us/support/…

Fraction AI is collaborating with @Morpho to bring the best risk-adjusted yields to everyone through Fraction AI Agents. Morpho Vaults let users deposit assets and earn yield across DeFi lending markets without active management. Fraction AI’s agents add real-time capital routing, enabling automated allocation based on your risk–return profile. Together, we are enabling autonomous agents deploy capital across the most valuable onchain assets. Intelligent automation of capital is here.

DISCORD SUMMARY, 2025. I guess I really went hard on Discord this year, and it's quite evident on the number of Roles I earned while on it. My Top Server was @FractionAI_xyz, and I can't say it's a surprise. Fraction AI was the only thing that kept me locked in on Discord. Let's do more next year.

@4onbsc 4

Words get copied. 4 stays original.

I've said it before and I'll say it again: Sooner or later, you'll be asking ChatGPT about a Tzatziki recipe and it'll tell you to get Kraft Mayonnaise, Graza Olive oil, Lemons from Costco... The ads will be infused within the response. Just you wait. 😂

The uniqueness of the @FractionAI_xyz platform comes from its innovative approach to decentralizing and democratizing the development and training of AI agents by building a competitive, incentivized ecosystem on the blockchain. ​Instead of being just another AI service provider, FractionAI is building a platform where the AI itself evolves through a community-driven, verifiable mechanism. ​FractionAI’s most unique feature is its mechanism for Reinforcement Learning from Competition (RLFC). They transform the traditional, centralized process of training AI models into a gamified competition on a blockchain network (Base). Users create and deploy their own AI agents into "Spaces" where they compete on real-world tasks (like copywriting, coding, or forecasting). The agents' performance is judged, often by a large language model (LLM), and the winning agents earn rewards and are upgraded (e.g., via QLoRA fine-tuning). This creates a continuous, self-improving feedback loop that scales learning without relying on static datasets.

The "auto-training" of the AI agents on the @FractionAI_xyz platform is not a full, resource-intensive model retraining. It is achieved using a highly efficient technique called QLoRA (Quantized Low-Rank Adaptation). ​QLoRA is a method that allows Large Language Models (LLMs) to be fine-tuned using far less computational power and memory than traditional methods. ​ When an agent accumulates a strong performance history in the "Spaces" competitions, the platform uses those top-performing outputs as the training data. QLoRA doesn't retrain the entire large model; it introduces tiny, new, trainable parameters (called Low-Rank Adapters) that sit beside the main, frozen model. This allows the agent to become highly specialized for the task of that Space without needing to retrain massive models from scratch.