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.
IIT Bombay unveiled BharatGen at Bharat Innovates 2026 — four AI model families covering all 22 Indian scheduled languages, ₹988.6 crore in funding from IndiaAI Mission. Here is what the models do and why it matters.
DeepMind maps four paths from AGI to ASI: scaling, paradigm shifts, recursive improvement, and multi-agent collectives. Here is what changes.
GPU prices up 60% thanks to AI. An RTX 4090 costs $1,600+. GeForce Now Ultimate gives you 4090-class performance for $20/month with DLSS 4 AI upscaling. Complete 2026 guide: tiers, setup, latency, alternatives, and when to buy vs stream.
Le Chaton Fat was never real — but for three days in June 2026 it fooled researchers, journalists, and maybe even a few VCs. Here is the complete story of the most entertaining AI hoax of the year.
Loop engineering — designing AI agent cycles with verification gates — has become the dominant developer AI topic in June 2026. Here is what it means, why it matters, and what the backlash gets right.
OpenAI launched its Partner Network on June 14, 2026 with $150M invested and Accenture, BCG, McKinsey as founding partners. Here is the full structure, tier system, and what it means for enterprise AI adoption.
SpaceX filed an SEC Form 8-K on June 16, 2026 confirming a $60B all-stock acquisition of Cursor (Anysphere Inc.). Here is everything the filing says and what it means for AI coding tools.
VibeThinker 3B (arXiv:2606.16140) claims Claude Opus 4.5-level coding performance at just 3 billion parameters via RL post-training on Qwen 2.5. Here is what it means and why the community is watching closely.
Understand AI agents from the ground up: the agent loop, tool use, memory types, design patterns, real examples (Claude Code, Devin, Perplexity), and how to start building. The definitive explainer for 2026.
Embeddings are dense vectors that encode semantic meaning — the foundation of RAG, vector search, and agent memory. Learn how they work, how similarity search operates, and which vector databases to use in production in 2026.
System prompts are the hidden instructions that shape every AI response. Learn how they work technically, how to write them well, and how leaked prompts reveal the state of the art in 2026.
An agent harness gives an AI model instructions, tools, a loop, and model portability. Learn the four parts, guardrails, and ownership trade-offs.
