{"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.\nWhat 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)\n00:00 – The problem with AI‑sounding writing\n00:45 – Meet Aleksandr Lashkov & the early Linguix journey\n02:30 – Who uses grammar tools (native vs. non‑native)\n05:20 – From rules to LLMs: the 3‑layer stack\n07:45 – Post‑ChatGPT: why grammar tools didn’t die\n10:30 – Delivery beats model choice (extensions, in‑context help)\n12:40 – Humanizer: removing AI tells & emerging etiquette\n15:20 – AI in education: hints over answers, critical thinking\n18:40 – Why “writing coach” flopped at work\n21:30 – Simplifier vs. paraphraser: usage hockey stick\n24:05 – Two educator camps & using analytics for support\n26:50 – The future: AI everywhere, natural language as the new UI\n29:30 – Build vs. buy: open source, data costs, and evals\n33:10 – What Aleksandr would do differently today\n36:20 – Open‑source parity & getting started\n38:30 – Wrap\nLinks & mentions\n• 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}