Learn Artificial Intelligence from the basics to intermediate level. Token Tool Hub offers practical guides on machine learning, neural networks, and AI’s role in blockchain and crypto
AI x Crypto: Autonomous Agents, Intents, and On-Chain Coordination “Intents” and agentic flows are the UX shift of 2025. Users don’t want to micromanage approvals and gas, they want to say what they want (“swap 200 USDC to ETH at best price under 20 bps slippage”) and let software handle the how. This guide turns […]
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AMD on DePIN (2025): The Real State of ROCm & HIP Rendering vs. ML Compatibility Can AMD GPUs earn on decentralized GPU networks today? Short answer: yes for a growing chunk of rendering, and limited, but improving options for ML. This operator-focused guide explains what actually works in 2025 across ROCm/HIP on Linux and Windows,
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Inference as a Side Business: Hosting LLM Endpoints on Decentralized Networks with SLAs If you can serve fast, reliable LLM responses at a fair price, there’s steady demand from agencies building chat tools to startups needing overflow capacity. The twist: instead of buying expensive GPUs, you can rent compute on decentralized networks and still promise
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From Home to Colocation: When a Garage Rack Makes Sense, Power Budgeting, and ROI Modeling There’s a point where a “home lab” stops being cute and starts being a utility. Fans get louder, breakers trip, the summer heat kicks in, and your power bill looks like a second rent. This guide helps you decide with
GPU Efficiency Playbook: Undervolt, Fan Curves, and VRAM Pad Upgrades for 24×7 Compute Around-the-clock compute pushes graphics cards far beyond “gaming for a few hours.” Machine learning training runs, render farms, scientific compute, and validators demand weeks of continuous duty. This playbook shows you how to cut 10–35% power draw, shave 5–20°C from hotspot temps,
GPU Efficiency Playbook: undervolt, fan curves, and VRAM pad upgrades for 24×7 compute. Read More »
What If AI Goes Wrong? The Real Risks of Machine Learning The upside of AI is obvious: speed, scale, and new capabilities. The downside often hides in quiet corners, skewed data, silent failure modes, brittle assumptions, and attack surfaces that look like ordinary inputs. This guide is a practical map of risks across data, models,
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Can You Learn AI in 30 Days? A Beginner’s Challenge You won’t become a research scientist in a month, but you can learn enough AI to automate real work, ship small apps, hold your own in conversations with experts, and build a portfolio that opens doors. This challenge is a structured, hands-on plan to do
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Why Every Web3 Builder Should Understand AI Now More Than Ever Web3 is programmable value; AI is programmable knowledge. The two are colliding into a new stack where agents have wallets, data has provenance, models earn and pay, and governance is increasingly mediated by machine intelligence. This masterclass explains the convergence, what’s real, what’s hype,
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AI Will Change Everything. Here’s What You Can Do to Stay Ahead. AI isn’t one product, it’s a capability wave sweeping through every role, industry, and workflow. Some people will be swept along. Others will surf. This masterclass is a playbook for the second group: what to learn, how to practice, which habits compound, and
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7 Things You Didn’t Know AI Could Do (And It’s Just Getting Started) AI can write emails and summarize meetings, sure. But under the hood, modern systems can do far stranger (and more useful) things: verify their own work with tools, transform sketches into working interfaces, learn from data without seeing the raw data, generate
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