the Postgres-as-backend part is what makes this interesting to me over the usual Airtable clones, since your data isn't stuck in some proprietary format if you ever want to leave or query it directly. the migration piece is the thing I'd want to test hardest though, especially anything with deeply nested lookups or rollups built on filtered views. that structural logic is usually where "we support Airtable migration" tools quietly fall apart even when the raw values move over fine. did you build this off real customer migrations or mostly synthetic test bases while developing it?
About Teable 3.0 on Product Hunt
“AI Spreadsheet for Business”
Teable 3.0 launched on Product Hunt on July 23rd, 2026 and earned 454 upvotes and 132 comments, earning #1 Product of the Day. Turn your business data into AI workflows and custom apps. Connect any system, migrate any data, and build anything that fits your business 100%.
On the analytics side, Teable 3.0 competes within Productivity, Spreadsheets, Artificial Intelligence, GitHub and OpenAI Day — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Teable 3.0 performed against the three products that launched closest to it on the same day.
Who hunted Teable 3.0?
Teable 3.0 was hunted by Rohan Chaubey. 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.
Rohan Chaubey
the Postgres-as-backend part is what makes this interesting to me over the usual Airtable clones, since your data isn't stuck in some proprietary format if you ever want to leave or query it directly. the migration piece is the thing I'd want to test hardest though, especially anything with deeply nested lookups or rollups built on filtered views. that structural logic is usually where "we support Airtable migration" tools quietly fall apart even when the raw values move over fine. did you build this off real customer migrations or mostly synthetic test bases while developing it?