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.
At Cloud Next ‘26, Google split its eighth-generation TPUs into training (8t) and inference (8i) silicon, launched Gemini Enterprise Agent Platform atop...
Understand Garry Tan’s gstack skill workflows. Try a focused repository review, inspect browser QA evidence, and adapt the process to your team.
Complete HTML Canvas guide: learn drawing shapes, animations, image manipulation, performance optimization, and real-world use cases for web graphics.
Use modern CSS with practical container queries, cascade layers, valid native nesting, accessible states, and migration checks.
Learn React Server Component boundaries, server-client composition, loading states, cache scope, and a measured route-by-route migration process.
When the measure becomes the target, it stops measuring well. In AI, that shows up as reward hacking, benchmark overfitting, and agents that please...
Improve Core Web Vitals with resource waterfalls, interaction traces, layout-shift diagnosis, comparable measurements, and practical performance budgets.
Learn WebAssembly integration with JavaScript, Rust, and C++. Measure end-to-end performance, inspect loading failures, and choose suitable compute kernels.
Learn WebGPU rendering and compute, then check capabilities, buffer bounds, readback requirements, fallbacks, and end-to-end performance.
Review agent skill instructions, scripts, and updates as software dependencies. Understand registry checks, local permissions, and the limits of verification.
Understand Gibberlink as data over sound, with practical decode tests, identity boundaries, and a comparison with structured network messages.
Alignment is the problem of building AI systems that reliably do what we intend—not only on average demos, but under pressure, at scale, and when incentives...
