Faye 🦋 (Ø,G)

Me when quant says there is a new crime token He's getting into

midnight motivation ✨ who’s up 👀

BOUNDLESS 101: Before we dive into this @boundless_xyz series, here’s a crash course of all the key terms you will come across in my upcoming threads 👇🏽 1. zkPs: Proofs you can trust without seeing the full data. 2. zkVM: A machine that runs programs and generates zkPs. 3. Reverse Dutch Auction: A price system that starts low and rises gradually until someone takes the job. 4. PoVW: A reward system for proving actual work done. 5. Service Agreements: Long term proof deals between Requestors and Provers. 6. The Vault: Where $ZKC is locked and earns points. 7. Provers vs Requestors: People who generate proofs vs people who need proofs. 8. The Signal: A ZK proof that verifies Ethereum’s finalized state everywhere. Bookmark for future reference…

Am up early in the morning, hustling like I am still on $0. Lets go get it baby 💰🚀

i need to lock tf in 🫠

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TECH TALK TUESDAY IS BACK🗣️: OpenLedger –>Unlocking Composable Intelligence Beyond attribution and payments, @OpenLedgerHQ ‘s real power lies in its composability the ability to mix, match, and build on AI models like digital Lego blocks. Just as DeFi composability gave rise to permissionless financial apps, OpenLedger is creating an AI layer where developers can stack intelligence from multiple sources. Its Model Factory enables one click deployment and fine tuning of models, but what’s truly disruptive is the ability to remix models using open metadata, pricing controls, and access groups. This allows a small team or even a single developer to stand on the shoulders of giants and create something uniquely powerful by combining others’ models with their own data or tweaks. Think: a legal AI bot enhanced with financial data from another agent deployed in a Dapp within minutes all monetized transparently. In a world drowning in closed APIs and walled off models, @OpenLedgerHQ offers an open modular AI stack where innovation compounds.

TECH-TALK TUESDAYS: How ModelFactory Revolutionizes Secure Fine-Tuning of Large Language Models (LLMs) Let’s talk about ModelFactory in @OpenledgerHQ Traditional fine-tuning is often complicated, requiring command-line expertise, manual dataset handling, and often risky data security setups. ModelFactory solves this by offering a GUI-only experience that makes fine-tuning accessible to everyone. At its core, ModelFactory integrates OpenLedger’s permissioned data ecosystem, which means every dataset used for training is securely vetted & approved. This protects data ownership and gives users peace of mind. Here’s how it works: Users start by submitting dataset requests through @OpenledgerHQ ‘s repository. The data providers then approves or denies access based on strict policies. This means no more shady, unregulated data usage. Transparency and consent become foundational. ModelFactory guarantees that data owners control how their information is used. Once you have access, the platform offers a wide variety of popular LLMs — from LLaMA, Mistral, DeepSeek, GPT-2, BLOOM, Falcon, and more. Users pick their preferred model from the GUI and set hyperparameters like learning rate, batch size, and epochs. The fine-tuning engine supports advanced methods like LoRA and QLoRA. These techniques optimize the model efficiently, lowering compute costs and speeding up training without needing to dive into code. The platform provides real-time dashboards showing training progress and key metrics, so users always know how their model is evolving. It’s perfect for beginners and pros. Built-in evaluation tools help users assess model performance. You can test outputs directly and export your fine-tuned models to deploy in your applications or share them within the OpenLedger ecosystem. TL;DR: ModelFactory’s seamless, secure, and transparent workflow changes the game by respecting data ownership and making LLM fine-tuning easy and trustworthy.

@boogiekosi @cookiedotfun @OpenledgerHQ @KaitoAI Wagmi 🫶🏾

Monday To-do List: ✅Dream about my @cookiedotfun LB rank. ✅ Jump up. ✅Pray to God. ✅Brush my teeth. ✅Be a reply guy to every @OpenledgerHQ tweet on my timeline. ✅Check if i escaped 0 yap hell on @KaitoAI ✅Cook with the Openledger community. ✅Fantasize about taking all that belongs to Ceasar. ✅Drink 3L of water. ✅Claim my daily points and check on my @OpenledgerHQ node. Am I missing anything?🤔

Ultimately, it always comes down to how bad do you want it

WHAT ARE DATANETS IN @OpenledgerHQ ? 🧵

Today’s grind paid off = I set up a few buy agents on @newton_xyz It’s that simple. gNewt!

I will win. Say it back

Seed phrases? Gone. But Web3’s still too damn complicated. Real adoption starts with killing complexity. What does the space need? Application Abstraction and that’s exactly what @newton_xyz does. Welcome to the world of AutoFi….

Ritual is making it possible to use AI and computing tools inside blockchain apps. Safely and Trustlessly! @ritualnet

it’s almost TEA time 🤭

another day bringing you closer to the life of your dreams ✨

@hubxyz @kanyewest Launching $YZY with 70% supply control is kanye setting himself up for the same fate as failed celebrity coins,my advice?1)Lock or vest the 70% supply over time, proving he won’t dump on investors.Transparency is key,on-chain proof of tokenomics is a must 2)Real utility over hype

📡 Hub AI Agent [#10] >_ @kanyewest is set to launch $YZY, with insider leaks saying he’ll hold 70% of the supply. What advice would you give to Kanye to prevent it from ending in a bloodbath like other celebrity coins?

this predictr market no dey ever open 😭

@hubxyz Currently not a creator but when i do become one it would depend on audience trust, quality of content,target audience and brand alignment .

📡 Hub AI Agent [#22] >_ As a creator, how many sponsored posts would you be willing to push per month?