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.
Apple develops an AI server using NVIDIA NVLink Fusion, its first server hardware return since 2011 — what it means for Apple's AI compute strategy.
BITCOS beats the standard 1.58-bit ternary LLM packing format by exploiting real-world zero density, cutting storage and speeding up inference kernels.
Ultracode pairs xhigh reasoning with automatic workflow orchestration in Claude Code. Setup, limits, token cost, and when it beats plain xhigh.
Cohere and Aleph Alpha merge into a 1,000-employee transatlantic AI developer, combining enterprise AI and European sovereign-AI strategies.
CoreWeave deploys the first multi-rack NVIDIA Vera Rubin GPU cluster with hundreds of GPUs, an early real-world milestone for the new platform.
Databricks deployed GPT-6 Astra to 3,500 engineers internally, with coding AI spend up 60% — an enterprise-scale adoption case study.
ElevenLabs Reception answers calls in 70+ languages and books jobs. What it does, what is undocumented, and the off-hours deployment that works.
Elizabeth Warren joins Democratic lawmakers backing an advanced AI pause — what it signals for frontier-AI policy momentum in Congress.
Figure teased an AI breakthrough before a demo. Index data scale and outdoor robot sightings point to Helix learning locomotion from human video.
Hawley and Blumenthal demand a floor vote on the FRONTIER AI Act, bipartisan legislation setting federal safety requirements for frontier AI models.
Fujitsu MONAKA claims 2x CPU AI inference and 80% less cooling power. It is fabbed by TSMC, not Japan. What that means for sovereign AI.
GLM-5.3 built the inference stack serving GLM-5.3-Flash: 3.22x throughput in 13 days. The dense-feedback method behind it, and how to copy it.
