This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
ContentAgent
AI writing in your voice. Scored before you see it.
ContentAgent learns your voice from three interview questions and runs every generation through a quality gate that catches generic AI patterns before you see the draft. 14 templates, free to start.
I'm Armin. I built ContentAgent because every AI writing tool I tried produced the same kind of output. Helpful hedging. The word "delve." Em dashes everywhere. A tidy rule-of-three. A motivational closer. Technically correct content, written in a voice that wasn't mine.
The fix isn't a better prompt. It's architecture.
ContentAgent runs voice calibration before any generation. Three interview questions about your real business experience. Things like "Tell me about the worst client you've ever dealt with" and "What did you say when they pushed back on your price?" The model extracts your actual patterns. Sentence rhythm. Vocabulary range. What you reach for first. What you'd never say. That profile is injected into every generation as a constraint. Not a stylistic suggestion.
Then a quality gate runs before you see the draft. Eight detection categories. Sycophantic openers. Generic conclusions. AI vocabulary. Structural tells like mechanical parallelism. Metronome rhythm. Platform format compliance. If the score is too low, it revises. You only see output that passes the gate.
A specificity radar runs alongside it. Separate system. It flags sentences that make claims without proof. "Significant improvements" gets caught. "Reduced support tickets by 34%" doesn't.
14 templates. Platform constraints enforced. LinkedIn's 3,000 chars. Twitter's 280. Email subject's 60. Copywriting frameworks from Caples, Schwartz, Hopkins, and Halbert baked into the generation context, not as templates but as the model's working brief.
Built solo over three months, 11 sprints. The stack is Next.js 16, Clerk for auth, Polar.sh for billing, Drizzle on Neon Postgres, OpenRouter for model access. Around 8,800 lines of TypeScript. Live billing, working webhooks, no fake demo mode.
Free tier is 10 generations a month, every template, every quality check. Pro is $19/month for 50 generations, model picker, and LLM voice review.
I'm in this thread all day. Ask me anything, the messy stuff included. What broke. What I'd build differently. Where ChatGPT is genuinely better. Why I went with Polar over Stripe. Why the free tier is generous on features and limited on count.
One thing I'd love feedback on: when you sign up and run voice calibration, does the generated content sound like you? I genuinely want to know.
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About ContentAgent on Product Hunt
“AI writing in your voice. Scored before you see it.”
ContentAgent was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #121 on the daily leaderboard. ContentAgent learns your voice from three interview questions and runs every generation through a quality gate that catches generic AI patterns before you see the draft. 14 templates, free to start.
ContentAgent was featured in Writing (59.2k followers), Marketing (464k followers) and Artificial Intelligence (469.3k followers) on Product Hunt. Together, these topics include over 178.5k products, making this a competitive space to launch in.
Who hunted ContentAgent?
ContentAgent was hunted by Armin. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how ContentAgent stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Armin
I'm Armin. I built ContentAgent because every AI writing tool I tried produced the same kind of output. Helpful hedging. The word "delve." Em dashes everywhere. A tidy rule-of-three. A motivational closer. Technically correct content, written in a voice that wasn't mine.
The fix isn't a better prompt. It's architecture.
ContentAgent runs voice calibration before any generation. Three interview questions about your real business experience. Things like "Tell me about the worst client you've ever dealt with" and "What did you say when they pushed back on your price?" The model extracts your actual patterns. Sentence rhythm. Vocabulary range. What you reach for first. What you'd never say. That profile is injected into every generation as a constraint. Not a stylistic suggestion.
Then a quality gate runs before you see the draft. Eight detection categories. Sycophantic openers. Generic conclusions. AI vocabulary. Structural tells like mechanical parallelism. Metronome rhythm. Platform format compliance. If the score is too low, it revises. You only see output that passes the gate.
A specificity radar runs alongside it. Separate system. It flags sentences that make claims without proof. "Significant improvements" gets caught. "Reduced support tickets by 34%" doesn't.
14 templates. Platform constraints enforced. LinkedIn's 3,000 chars. Twitter's 280. Email subject's 60. Copywriting frameworks from Caples, Schwartz, Hopkins, and Halbert baked into the generation context, not as templates but as the model's working brief.
Built solo over three months, 11 sprints. The stack is Next.js 16, Clerk for auth, Polar.sh for billing, Drizzle on Neon Postgres, OpenRouter for model access. Around 8,800 lines of TypeScript. Live billing, working webhooks, no fake demo mode.
Free tier is 10 generations a month, every template, every quality check. Pro is $19/month for 50 generations, model picker, and LLM voice review.
I'm in this thread all day. Ask me anything, the messy stuff included. What broke. What I'd build differently. Where ChatGPT is genuinely better. Why I went with Polar over Stripe. Why the free tier is generous on features and limited on count.
One thing I'd love feedback on: when you sign up and run voice calibration, does the generated content sound like you? I genuinely want to know.
https://contentagent.kern.web.za