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.
Write MCP tool descriptions with clear boundaries, precise inputs, useful failure responses, and evaluations for tool choice and argument correctness.
Meta FAIR's Brain2Qwerty v2 decodes full sentences from non-invasive MEG at 39% average WER — 22% for the best participant. No implants, no surgery.
Design agent error contracts with failure categories, partial results, retry guidance, and coverage gaps so coordinators can recover from failures.
Ollama 0.31 — Gemma 4 multi-token prediction on MLX. 95 vs 50 tok/s on Aider, auto-tuned MTP, ollama launch coding agents. Full breakdown.
Alibaba-linked Proxy-KD paper (arXiv:2401.07013) explains how to distill GPT-4 without weights. Why HN resurfaced it after Anthropic's Fable 5 extraction claims.
Run Qwen 3.6 27B locally with llama.cpp MTP, OpenCode config, tok/s benchmarks on M5 Max, vs 35B MoE and Gemma 4 31B for coding.
SimpleX Chat has no phone numbers, no usernames, no IDs. After Telegram CEO arrest and global messaging bans, here is why that matters and how to set it up.
Stanford's MemoryDAX dataset charts 65 years of DRAM, HBM, and NAND prices. What the data shows about AI's impact on memory costs and what comes next.
Design structured extraction with tool schemas, nullable fields, and bounded retries. Separate strict output guarantees from business-rule validation.
DeepSeek, Zhipu, Moonshot, MiniMax, Qwen — ranked by models, funding, and enterprise fit. Updated June 2026 with pricing and how to try each stack.
A practical taxonomy of AI agent types — by autonomy, loop architecture, domain, tool access, and human involvement — with a decision matrix for choosing the right agent design in 2026.
US AI startups raised $285B vs China's $12B — yet the model gap is 2.7%. Compare funding, open-source strategy, regulation, and startup moats.
