{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"AI Native Podcast","title":"Stop Sounding Like a Bot: The New Rules of AI Writing","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/b315322b\"></iframe>","width":"100%","height":180,"duration":2355,"description":"AI writing shouldn’t sound like AI. In this episode of AI Native, we sit down with Aleksandr Lashkov, co‑founder of Linguix, to unpack seven years of building grammar tools from rule‑based systems to LLM‑powered assistants. We dig into why delivery beats model choice (hello, browser extensions), how “humanizer” features reduce AI tells, and where AI helps—or harms—learning. Aleksandr shares hard‑won product lessons, what changed after ChatGPT, and practical advice for builders weighing open‑source models vs. APIs and the real costs of data, evals, and hiring.What you’ll learnWhy “AI‑sounding” emails are becoming a new professional faux pas.Native vs. non‑native users: who actually benefits from grammar tools (and why).The evolution from rules → LLMs → heuristics (and how to marry them).“Delivery > model”: placing help where users write (Gmail, Docs, chat UIs).Education vs. productivity: when AI should hint—not answer.Product lessons: simplify, surface proactively, reduce clicks.How to approach a custom model: open source options, data realities, and evals.Chapters (YouTube) 00:00 – The problem with AI‑sounding writing 00:45 – Meet Aleksandr Lashkov & the early Linguix journey 02:30 – Who uses grammar tools (native vs. non‑native) 05:20 – From rules to LLMs: the 3‑layer stack 07:45 – Post‑ChatGPT: why grammar tools didn’t die 10:30 – Delivery beats model choice (extensions, in‑context help) 12:40 – Humanizer: removing AI tells & emerging etiquette 15:20 – AI in education: hints over answers, critical thinking 18:40 – Why “writing coach” flopped at work 21:30 – Simplifier vs. paraphraser: usage hockey stick 24:05 – Two educator camps & using analytics for support 26:50 – The future: AI everywhere, natural language as the new UI 29:30 – Build vs. buy: open source, data costs, and evals 33:10 – What Aleksandr would do differently today 36:20 – Open‑source parity & getting started 38:30 – WrapLinks & mentions • Sponsor: AIorNot.com — detect whether text is human or...","thumbnail_url":"https://img.transistorcdn.com/2mE-O6D1MTXZW0o6P3aqVV8ebbrPfNImANXDuw3NpVs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80NTcz/MmNjODU1ZWYxOTAy/YTUxYjNhYmJkMjhm/NGY5YS5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}