Expert profile
Founder & AI Product Leader
Yash Thakker is a Generative AI expert with over 12 years of experience in product leadership and technical strategy. As the founder of explainx.ai, he has taught over 300,000 learners and built AI platforms serving millions of users globally. He specializes in Agentic AI, Multimodal RAG, and the intersection of LLMs with consumer hardware.
How AI transforms music, art, writing, and photography hobbies in 2026. Tools for every creative type, honest takes on the ownership question, and how to use AI to learn skills rather than bypass them.
Complete guide to the global AI regulatory landscape in 2026 — EU AI Act risk tiers, GPAI rules, US federal and state law, China's generative AI rules, and a practical compliance checklist for product teams.
Clone any site to Next.js with Claude Code or Cursor: template setup, legality, vs Claude Design, motion-site limits, and the 5-phase pipeline explained.
Two weeks after the US blocked Fable 5 and Mythos 5 globally, Sakana AI and China's 360 launched cyber-focused models pitched as alternatives. Here is the...
Build a local MCP server, inspect its advertised capabilities, and verify handler behavior and access boundaries before connecting an agent.
Master Claude Code Plan Mode — how to invoke /plan, read a plan, edit it, chain multi-phase plans, and integrate planning into team workflows. With CLAUDE.md and worktree examples.
Learn how Claude Code subagents work — the orchestrator/worker model, git worktrees for isolation, parallel vs serial workflows, and real multi-agent patterns for coding tasks.
Full-stack guide to context engineering: anatomy of a context package, token budget management, agentic context design, common mistakes with before/after examples, a practical checklist, and how to measure context quality.
Data centers use ~1.5% of global electricity—not 50%. US water is ~0.3% of supply, not “drinking America dry”—but Arizona and Ireland prove local impact is...
DSpark boosts DeepSeek V4 throughput 51–400% via speculative decoding. Not a new model — DeepSpec repo, vs DFlash, vLLM setup, and training draft models.
Build eval sets, run A/B tests, use LLM-as-judge, and automate regression testing for your prompts. Complete guide to treating prompt engineering like software engineering.
Fable 5 live July 1; GPT-5.6 GA next. GLM-5.2, Kimi K2.7, Qwen3 enterprise alternatives — benchmarks, licenses, vLLM at scale.
