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).
Datailor is a solution to a personal frustration.
Session memory is a dead end. Teach Claude your coding style today, start a new thread tomorrow, and you're explaining indentation preferences all over again.
AGENTS.md promises permanence, but it's a static wall of text that never learns, never adapts, and quietly becomes outdated the moment your workflow shifts.
LLM-native memory is opaque, unreliable, and hopelessly vendor-locked. Cross-device portability? Try migrating your "preferences" from Cursor to Claude to Codex and watch them evaporate.
I wanted something that actually understands the problem:
◼ Dynamic matching — knows when to surface a preference and when to stay out of the way
◼ Automatic capture — learns from what you do, not from what you claim you do
◼ Intentional expiration — lets stale preferences fade instead of accumulating digital hoard
◼ Conflict detection — surfaces contradictions instead of silently guessing wrong
So I built Datailor. One Markdown file. Any agent can read it.
Datailor is deliberately minimal, human-readable, and open source. I beleive AI tooling should be like good conversations: respectful, interactive, and actually listening.
Hi all!╰(*°▽°*)╯ Thank you guys for the following and votes.
◼ New in Datailor: datailor disable / datailor enable.
Shipped an on/off switch for Datailor today, because a user said it best: you don't always want your AI to know your preferences too well.
So now you stay in control:
▎ datailor disable # AI stops getting your preferences ▎ datailor enable # turn it back on ▎ datailor status # check current state
Disabling only stops injection. Capture keeps running quietly in the background, so you lose nothing by switching off for a sensitive task or a fresh-eyes session. Flip it back anytime.
I think everyone who uses multiple AI tools has had the experience of explaining the same preferences again and again. It's a small annoyance individually, but it adds up quickly.
About Datailor Preference MCP on Product Hunt
“Give every AI agent your defaults”
Datailor Preference MCP was submitted on Product Hunt and earned 11 upvotes and 4 comments, placing #77 on the daily leaderboard. Datailor turns your recurring corrections into local, editable preferences that every agent can use. Local preference settings for any AI agent.
Datailor Preference MCP was featured in Productivity (653.8k followers), Open Source (68.5k followers), Artificial Intelligence (471k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 274.9k products, making this a competitive space to launch in.
Who hunted Datailor Preference MCP?
Datailor Preference MCP was hunted by Rosetta (Zidian) Guo. 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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Rosetta (Zidian) Guo