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).
RemiAI is a local AI assistant designed to help you interact with your files, run searches, and connect to external tools (MCP servers). It is built with Next.js, TypeScript, and the AI SDK libraries, offering a lightweight, self‑hosted alternative to cloud‑based assistants.
Have you considered adding a built-in vector store option so the local assistant can index files for semantic search right out of the box. That would make it way more useful for people working with large doc collections without needing to bolt on a separate database.
the fact that it runs fully local and still hooks into MCP servers is honestly really clean, most self-hosted assistants I have tried feel half-baked but this one actually feels polished
honestly this looks solid, but one thing that would be a game changer for me is proper file watching so the assistant can auto index new or modified files without me having to manually re run a search. basically keep the local context fresh in the background without extra clicks.
About RemiAI on Product Hunt
“Local AI assistant with powerful tools.”
RemiAI was submitted on Product Hunt and earned 0 upvotes and 5 comments, placing #45 on the daily leaderboard. RemiAI is a local AI assistant designed to help you interact with your files, run searches, and connect to external tools (MCP servers). It is built with Next.js, TypeScript, and the AI SDK libraries, offering a lightweight, self‑hosted alternative to cloud‑based assistants.
RemiAI was featured in Productivity (656k followers), Artificial Intelligence (473.5k followers), GitHub (41.3k followers) and Virtual Assistants (16.1k followers) on Product Hunt. Together, these topics include over 278.9k products, making this a competitive space to launch in.
Who hunted RemiAI?
RemiAI was hunted by Nicolas H.. 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.
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Nicolas H.
Have you considered adding a built-in vector store option so the local assistant can index files for semantic search right out of the box. That would make it way more useful for people working with large doc collections without needing to bolt on a separate database.