AI CONTENT PIPELINE
Watch an agent write, rewrite, and ship its own frontmatter.
A real 3-step Groq LLM chain, not a mockup: an honest scaffold that marks where you add real code and numbers instead of faking them, a copy-edit pass that strips AI-tell phrases, and SEO frontmatter generation — with token usage, latency, and a live eval shown for every step.
Why a humanizing pass, and why measure it
First drafts from an LLM tend to lean on the same tells — “in conclusion”, “delve into”, “leverage”, “comprehensive”. The second call is a targeted rewrite instructed to avoid that exact list, and the metrics panel counts how many of those phrases show up before and after — a small, concrete eval instead of just trusting that it worked.
What actually happens on submit
Your topic, keywords, tone, and length go to a Next.js route handler, which makes three sequential calls to Groq’s llama-3.1-8b-instant model — scaffold, copy-edit, then a JSON frontmatter call matching this blog’s real frontmatter shape. The scaffold step is told to leave [TODO: …] placeholders wherever it would otherwise have to invent code, a benchmark, or personal experience — an honest draft you finish, not fake content you publish. Nothing is saved anywhere; the output only exists in your browser for this run.
Free tool by Adesh Shukla — runs a real 3-step Groq LLM pipeline server-side. A few free runs per day, no signup; after that, paste your own Groq API key (never logged or stored, used only for that request).