In 2026, mastering artificial intelligence is more effective not through textbooks, but through the live feeds of practitioners who work with models every day. A stable community of researchers, developers, and entrepreneurs has formed on X who openly explain how large language models work, demonstrate the assembly of applications and agents, and share real revenue figures.
Cryptalist selected 20 accounts covering virtually all directions — from the fundamentals of LLMs to advertising, solo startups, and corporate adoption.
Foundations and Model Architecture
@karpathy — Andrej Karpathy, co-founder of OpenAI and former head of AI at Tesla. One of the few top-level researchers who explains how large language models work in simple terms. Publishes breakdowns and educational materials — the starting point for understanding how LLMs work from the inside.
@AndrewYNg — Andrew Ng, founder of Coursera and Google Brain, creator of DeepLearning.AI. Writes about AI systems engineering and how the industry is changing the labor market.
@Hesamation — Hesam Sheikh, an AI engineer with a master's degree in AI from the University of Bologna, author of the QiuByte newsletter. Breaks down machine learning and working with models in accessible language.
@emollick — Ethan Mollick, professor at the Wharton School of Business and author of the book Co-Intelligence. Analyzes AI's impact on productivity based on scientific data rather than marketing promises.
Development Practice and Agents
@steipete — Peter Steinberger, creator of OpenClaw, an open AI agent. In early 2026 he moved to OpenAI and handed the project over to an independent foundation. Honestly talks about the problems of building agents.
@rileybrown — Riley Brown, one of the main popularizers of vibe coding — creating applications through dialogue with AI without deep programming knowledge.
@corbin_braun — Corbin Braun, author of educational content on Cursor, a code editor with built-in AI. Breaks down techniques, settings, and common mistakes.
@kloss_xyz — an account about systems architecture: how to design AI products so they don't fall apart under growing load.
@claudeskills101 — a specialized account on working with Claude by Anthropic: prompting techniques and use cases.
Solo Entrepreneurs and Monetization
@gregisenberg — Greg Isenberg, entrepreneur and investor, head of Late Checkout. Breaks down finding niches and validating startup ideas.
@levelsio — Pieter Levels, the most famous solo entrepreneur in the world, creator of Nomad List and Photo AI. Regularly publishes revenue and shows how one person does the work of an entire company.
@marclou — Marc Lou, author of the ShipFast template for quickly launching products. Lots of practice, little theory.
@jackfriks — Jack Friks, an independent developer who openly shares download numbers, income, and failures.
@EXM7777 — the Machina account, whose author runs AI agencies. Publishes systems and schemes for automating routine processes.
@eptwts — an anonymous account about monetization with AI: ideas, case studies, and approaches to earning.
@ai_explorer25 — a review account about new AI tools, testing services that appear almost daily.
@vasuman — Vas, former Meta engineer and founder of the agency Varick AI Agents. A balance between technical depth and business logic.
Marketing, Advertising, and Corporate Adoption
@mardehaym — Mark Eisenstadt, writes about AI adoption in large companies: budgets, constraints, and real results.
@AmirMushich — Amir Mushich, a specialist in AI advertising: generating creatives and scaling campaigns.
@0xROAS — an anonymous author who built the AI Marketing Mafia community. Specializes in UGC videos created by neural networks and shares working combinations.
Cryptalist analysis: The shift of AI learning from paid courses to the open feeds of practitioners reflects the maturity of the market: the speed at which tools are updated exceeds the production cycle of educational programs, and verifiable revenue and open metrics are becoming the main criterion of expertise. For investors and operators, this is a signal that human capital in AI is being formed in a decentralized way, and the quality of competencies is increasingly confirmed by public case studies rather than certificates.