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Fluent
Make your AI fluent in what matters
Fluent works inside ChatGPT, Claude, and other AI apps, bringing in what matters for each question. A clothing purchase can draw on your closet and clothing budget. A meal plan can draw on your tastes, grocery needs, and grocery spending.
Hi Product Hunt, I'm Shane, the solo maker behind Fluent. I built it at night over the past six months because I kept asking AI apps for help with decisions that depended on more than the current conversation knew, such as what was already in my closet, what I planned to spend, how we like to eat, and what we needed from the store.
Fluent works inside ChatGPT, Claude, Codex, OpenClaw, and other AI apps that support MCP. It brings in the information that matters for each question. A clothing purchase can draw on your closet and clothing budget. A meal plan can draw on your tastes, grocery needs, and grocery spending. Each question uses the combination that makes sense for it.
I built Fluent three times. The first version was a full closet app for iOS and web. The second centered on its own agent. I scrapped both. Each version made the central idea clearer: the valuable part was a shared layer that worked inside the AI apps people already use.
One moment that made the idea click was a simple purchase question: should I buy another black tee? Fluent could bring my closet and remaining clothing budget into the same conversation. The assistant could see that I already owned several similar tees and that the purchase would use more than the budget I had left. That is the kind of answer Fluent is for: advice that can take more than one relevant thing into account.
Meals is the deepest area today. My assistant and I can plan a week, build the grocery list, and use preferences and grocery-spending context while deciding what to cook. When I later correct a recipe's timing or ingredients, that correction is available when the recipe comes up again.
Changes are shown before they are saved, and Fluent returns the saved result so the assistant can confirm what changed. You can inspect, correct, export, or delete the information you add.
I'd especially value feedback on how quickly the product makes sense, whether setup feels straightforward, and which decision you would want Fluent to help with first.
About Fluent on Product Hunt
“Make your AI fluent in what matters”
Fluent was submitted on Product Hunt and earned 12 upvotes and 10 comments, placing #142 on the daily leaderboard. Fluent works inside ChatGPT, Claude, and other AI apps, bringing in what matters for each question. A clothing purchase can draw on your closet and clothing budget. A meal plan can draw on your tastes, grocery needs, and grocery spending.
On the analytics side, Fluent competes within Productivity, Open Source, Artificial Intelligence and GitHub — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Fluent performed against the three products that launched closest to it on the same day.
Who hunted Fluent?
Fluent was hunted by Shane Rodness. 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.
For a complete overview of Fluent including community comment highlights and product details, visit the product overview.
Shane Rodness
Hi Product Hunt, I'm Shane, the solo maker behind Fluent. I built it at night over the past six months because I kept asking AI apps for help with decisions that depended on more than the current conversation knew, such as what was already in my closet, what I planned to spend, how we like to eat, and what we needed from the store.
Fluent works inside ChatGPT, Claude, Codex, OpenClaw, and other AI apps that support MCP. It brings in the information that matters for each question. A clothing purchase can draw on your closet and clothing budget. A meal plan can draw on your tastes, grocery needs, and grocery spending. Each question uses the combination that makes sense for it.
I built Fluent three times. The first version was a full closet app for iOS and web. The second centered on its own agent. I scrapped both. Each version made the central idea clearer: the valuable part was a shared layer that worked inside the AI apps people already use.
One moment that made the idea click was a simple purchase question: should I buy another black tee? Fluent could bring my closet and remaining clothing budget into the same conversation. The assistant could see that I already owned several similar tees and that the purchase would use more than the budget I had left. That is the kind of answer Fluent is for: advice that can take more than one relevant thing into account.
Meals is the deepest area today. My assistant and I can plan a week, build the grocery list, and use preferences and grocery-spending context while deciding what to cook. When I later correct a recipe's timing or ingredients, that correction is available when the recipe comes up again.
Changes are shown before they are saved, and Fluent returns the saved result so the assistant can confirm what changed. You can inspect, correct, export, or delete the information you add.
The full build story is here:
https://meetfluent.app/case-study/
Try Fluent:
https://meetfluent.app
The Apache-2.0 runtime is available here:
https://github.com/shaner-git/fluent-oss
I'd especially value feedback on how quickly the product makes sense, whether setup feels straightforward, and which decision you would want Fluent to help with first.